You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and serve millions of users. How should you design the frontend to respond to a database failure?

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

#Syntax error: Expected end of statement but got "-" at [4:11]

SELECT age

FROM bigquery-public-data.noaa_gsod.gsod

WHERE age != 99

AND_TABLE_SUFFIX = '1929'

ORDER BY age DESC

Which table name will make the SQL statement work correctly?

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

You are working on optimizing BigQuery for a query that is run repeatedly on a single table. The data queried is about 1 GB, and some rows are expected to change about 10 times every hour. You have optimized the SQL statements as much as possible. You want to further optimize the query's performance. What should you do?

Several years ago, you built a machine learning model for an ecommerce company. Your model made good predictions. Then a global pandemic occurred, lockdowns were imposed, and many people started working from home. Now the quality of your model has degraded. You want to improve the quality of your model and prevent future performance degradation. What should you do?

A new member of your development team works remotely. The developer will write code locally on their laptop, which will connect to a MySQL instance on Cloud SQL. The instance has an external (public) IP address. You want to follow Google-recommended practices when you give access to Cloud SQL to the new team member. What should you do?

Your Cloud Spanner database stores customer address information that is frequently accessed by the marketing team. When a customer enters the country and the state where they live, this information is stored in different tables connected by a foreign key. The current architecture has performance issues. You want to follow Google-recommended practices to improve performance. What should you do?

Your company runs its business-critical system on PostgreSQL. The system is accessed simultaneously from many locations around the world and supports millions of customers. Your database administration team manages the redundancy and scaling manually. You want to migrate the database to Google Cloud. You need a solution that will provide global scale and availability and require minimal maintenance. What should you do?

Your company collects data about customers to regularly check their health vitals. You have millions of customers around the world. Data is ingested at an average rate of two events per 10 seconds per user. You need to be able to visualize data in Bigtable on a per user basis. You need to construct the Bigtable key so that the operations are performant. What should you do?

Your company is hiring several business analysts who are new to BigQuery. The analysts will use BigQuery to analyze large quantities of data. You need to control costs in BigQuery and ensure that there is no budget overrun while you maintain  the quality of query results. What should you do?

Your Bigtable database was recently deployed into production. The scale of data ingested and analyzed has increased significantly, but the performance has degraded. You want to identify the performance issue. What should you do?

Your company is moving your data analytics to BigQuery. Your other operations will remain on-premises. You need to transfer 800 TB of historic data. You also need to plan for 30 Gbps of daily data transfers that must be appended for analysis the next day. You want to follow Google-recommended practices to transfer your data. What should you do?

Your team runs Dataproc workloads where the worker node takes about 45 minutes to process. You have been exploring various options to optimize the system for cost, including shutting down worker nodes aggressively. However, in your metrics you see that the entire job takes even longer. You want to optimize the system for cost without increasing job completion time. What should you do?

Your customer has a SQL Server database that contains about 5 TB of data in another public cloud. You expect the data to grow to a maximum of 25 TB. The database is the backend of an internal reporting application that is used once a week. You want to migrate the application to Google Cloud to reduce administrative effort while keeping costs the same or reducing them. What should you do?

Your IT team uses BigQuery for storing structured data. Your finance team recently moved to Google Workspace Enterprise edition from a standalone, desktop-based spreadsheet processor. When the finance team needs data insights, the IT team runs a query on BigQuery, exports the data to a CSV file, and sends the file as an email attachment to the finance team members. You want to improve the process while you retain familiar methods of data analysis for the finance team. What should you do?

Your scooter-sharing company collects information about their scooters, such as location, battery level, and speed. The company visualizes this data in real time. To guard against intermittent connectivity, each scooter sends repeats of certain messages within a short interval. Occasional data errors have been noticed. The messages are received in Pub/Sub and stored in BigQuery. You need to ensure that the data does not contain duplicates and that erroneous data with empty fields is rejected. What should you do?

Your cryptocurrency trading company visualizes prices to help your customers make trading decisions. Because different trades happen in real time, the price data is fed to a data pipeline that uses Dataflow for processing. You want to compute moving averages. What should you do?

You are building the trading platform for a stock exchange with millions of traders. Trading data is written rapidly. You need to retrieve data quickly to show visualizations to the traders, such as the changing price of a particular stock over time. You need to choose a storage solution in Google Cloud. What should you do?

Your customer uses Hadoop and Spark to run data analytics on-premises. The main data is stored in hard disks that are centrally accessed. Your customer needs to migrate their workloads to Google Cloud efficiently while considering scalability. You want to select an architecture that requires minimal effort. What should you do?

You used a small amount of data to build a machine learning model that gives you good inferences during testing. However, the results show more errors when real-world data is used to run the model. No additional data can be collected for testing. You want to get a more accurate view of the model's capability. What should you do?

Your organization has been collecting information for many years about your customers, including their address and credit card details. You plan to use this customer data to build machine learning models on Google Cloud. You are concerned about private data leaking into the machine learning model. Your management is also concerned that direct leaks of personal data could damage the company's reputation. You need to address these concerns about data security. What should you do?

Your healthcare application has a backend system that accepts event data directly from IoT devices. Recent increases of the application's users and devices are causing a sudden influx of data that overwhelms the system. You need to redesign the data pipeline to ensure that all data is processed and that no events are lost. You want to follow Google-recommended practices. What should you do?  

You have 250,000 devices which produce a JSON device status event every 10 seconds. You want to capture this event data for outlier time series analysis. What should you do?

You are designing storage for CSV files and using an I/O-intensive custom Apache Spark transform as part of deploying a data pipeline on Google Cloud. You intend to use ANSI SQL to run queries for your analysts.

How should you transform the input data?

Your company is loading comma-separated values (CSV) files into BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.

What is the most likely cause of this problem?

You are using Pub/Sub to stream inventory updates from many point-of-sale (POS) terminals into BigQuery.

Each update event has the following information: product identifier "prodSku", change increment "quantityDelta", POS identification "termId", and "messageId" which is created for each push attempt from the terminal.

During a network outage, you discovered that duplicated messages were sent, causing the inventory system to over-count the changes. You determine that the terminal application has design problems and may send the same event more than once during push retries.

You want to ensure that the inventory update is accurate. What should you do?

You are building storage for files for a data pipeline on Google Cloud. You want to support JSON files. The schema of these files will occasionally change.

Your analyst teams will use running aggregate ANSI SQL queries on this data. What should you do?

You need to stream time-series data in Avro format, and then write this to both BigQuery and Cloud Bigtable simultaneously using Dataflow. You want to achieve minimal end-to-end latency.

Your business requirements state this needs to be completed as quickly as possible. What should you do?

You are working on a project with two compliance requirements. The first requirement states that your developers should be able to see the Google Cloud billing charges for only their own projects.

The second requirement states that your finance team members can set budgets and view the current charges for all projects in the organization.

The finance team should not be able to view the project contents. You want to set permissions. What should you do?

You want to publish system metrics to Google Cloud from a large number of on-prem hypervisors and VMs for analysis and creation of dashboards.

You have an existing custom monitoring agent deployed to all the hypervisors and your on-prem metrics system is unable to handle the load. You want to design a system that can collect and store metrics at scale. You don't want to manage your own time series database.

Metrics from all agents should be written to the same table but agents must not have permission to modify or read data written by other agents. What should you do?

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance.

How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

You are designing a relational data repository on Google Cloud to grow as needed. The data will be transactionally consistent and added from any location in the world.

You want to monitor and adjust node count for input traffic, which can spike unpredictably. What should you do?

A company is migrating its current infrastructure from on-premise to Google cloud. It stores over 280TB of data on its on-premise HDFS servers. You were tasked to move data from HDFS to Google Storage in a secure and efficient manner. Which of the following approaches are best to fulfill this task?

You have a Dataflow pipeline to run and process a set of data files received from a client, for transformation and loading into a data warehouse. This pipeline should run each morning so that metrics can be ready when stakeholders need the latest stats based on data sent the day before. Which tool should you use?

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

You have an Apache Kafka cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP.

Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally.
You also want to optimize data for range queries on non-key columns. What should you do?

Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
✑ The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
✑ Support for publish/subscribe semantics on hundreds of topics
✑ Retain per-key ordering
Which system should you choose?

You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
✑ Single global endpoint
✑ ANSI SQL support
✑ Consistent access to the most up-to-date data
What should you do?

You are designing a cloud-native historical data processing system to meet the following conditions:
✑ The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
✑ A streaming data pipeline stores new data daily.
✑ Peformance is not a factor in the solution.
✑ The solution design should maximize availability.
How should you design data storage for this solution?

A company uses Airflow to orchestrate its data pipelines and DAGs (Directed Acyclic Graphs), installed and maintained on-premise by DevOps team. The company wants to migrate the data pipelines managed in Airflow to Google Cloud. The company is looking for a migration method which can make DAGs available and migrated without extra code modifications so the data pipelines can be available once migrated. Which service should you use?

An air-quality research facility monitors the quality of the air and alerts of possible high air pollution in a region. The facility receives event data from 25,000 sensors every 60 seconds. Event data is then used for time-series analysis per region. Cloud experts suggested using BigTable for storing event data.
What will you design the row key for each even in BigTable?

Your company hosts a gaming app which reaches over 30,000 players in a single minute. The app generates event data including information about players state, score, location coordinates and other stats. You need to find a storage solution which can support high read/write throughput with very low latency which doesn’t exceed 10 milliseconds to ensure a quality performance experience for the players.
Which of the following is the best option for this scenario?

An online learning platform wants to generate captions for its videos. The platform offers around 2,500 courses with topics about business, finance, cooking, development & science. The platform allows content with different languages such as French, German, Turkish and Thai. Thus, this can be very difficult for a single team to caption all available courses and they are looking for an approach which helps do such massive job.
Which product from Google Cloud will you suggest them to use?

You want to launch a Cloud Machine Learning Engine cluster to deploy a deep neural network model built by Tensorflow by data scientists of your company. Reviewing the standard tiers available by Google ML Engine, you could not find a tier that suits the requirements data scientists need for the cluster. Google allows you to specify custom cluster specification.
Which of the following specifications you are allowed to set? (pick two)

Your company signed a contract with a retail chain store to handle its data processing applications and tech stack. One of the several applications to be implemented is building an ETL pipeline to ingest the chain store’s daily purchase transaction logs to be processed and stored for analysis and reporting; visualize the chain’s purchase details for the head management.

Daily transaction logs will be available at 2 am when the day is over and logs are exported to a Google Storage bucket partitioned by date in format (yyyy-mm-dd). Dataflow pipeline should run every day at 3:00 am to ingest and process the logs. Which of the following Google products would help?

You are building a model using TensorFlow. Upon training the model, the results show that the model could return 73% true positives. When you tested the model with a set derived from real data. You noticed a decrease in true positive returns to 65%. You need to tune the model for better prediction. What would you do? (pick two)

An organization wants to automate its data processing workflows using Cloud Composer. They need to schedule jobs in a repeatable manner to ensure the timely execution of critical tasks. What approach should they take to achieve this goal effectively?

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)

Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

You are using Dataproc to process a large number of CSV files. The storage option you choose needs to be flexible to serve many worker nodes in multiple clusters. These worker nodes will read the data and also write to it for intermediate storage between processing jobs. What is the recommended storage option on Google Cloud?

Cymbal Retail has acquired another company in Europe. Data access permissions and policies in this new region differ from those in Cymbal Retail’s headquarters, which is in North America. You need to define a consistent set of policies for projects in each region that follow recommended practices. What should you do?

You are managing the data for Cymbal Retail, which consists of multiple teams including retail, sales, marketing, and legal. These teams are consuming data from multiple producers including point of sales systems, industry data, orders, and more. Currently, teams that consume data have to repeatedly ask the teams that produce it to verify the most up-to-date data and to clarify other questions about the data, such as source and ownership. This process is unreliable and time-consuming and often leads to repeated escalations. You need to implement a centralized solution that gains a unified view of the organization's data and improves searchability. What should you do?

You have a Dataflow pipeline that runs data processing jobs. You need to identify the parts of the pipeline code that consume the most resources. What should you do?

You are migrating on-premises data to a data warehouse on Google Cloud. This data will be made available to business analysts. Local regulations require that customer information including credit card numbers, phone numbers, and email IDs be captured, but not used in analysis. You need to use a reliable, recommended solution to redact the sensitive data. What should you do?

Cymbal Retail has a team of business analysts who need to fix and enhance a set of large input data files. For example, duplicates need to be removed, erroneous rows should be deleted, and missing data should be added. These steps need to be performed on all the present set of files and any files received in the future in a repeatable, automated process. The business analysts are not adept at programming. What should they do?

Your data and applications reside in multiple geographies on Google Cloud. Some regional laws require you to hold your own keys outside of the cloud provider environment, whereas other laws are less restrictive and allow storing keys with the same provider who stores the data. The management of these keys has increased in complexity, and you need a solution that can centrally manage all your keys. What should you do?

Business analysts in your team need to run analysis on data that was loaded into BigQuery. You need to follow recommended practices and grant permissions. What role should you grant the business analysts?

Cymbal Retail is migrating its private data centers to Google Cloud. Over many years, hundreds of terabytes of data were accumulated. You currently have a 100 Mbps line and you need to transfer this data reliably before commencing operations on Google Cloud in 45 days. What should you do?

Laws in the region where you operate require that files related to all orders made each day are stored immutably for 365 days. The solution that you recommend has to be cost-effective. What should you do?

A company collects lots of consumer data from online marketing campaigns. Company plans to use Google Cloud to store this collected data. The top management is worried about exposing personally identifiable information (PII) that may be present in this data. What should you do to reduce the risk of exposing PII data?

Your company is very serious about data protection and hence decides to implement the Principle of Least Privilege. What should you do to comply with this policy?

Your data engineering team receives data in JSON format from external sources at the end of each day. You need to design the data pipeline.

What should you do?

The first stage of your data pipeline processes tens of terabytes of financial data and creates a sparse, time-series dataset as a key-value pair.

Which of these is a suitable sink for the pipeline's first stage?

You are processing large amounts of input data in BigQuery. You need to combine this data with a small amount of frequently changing data that is available in Cloud SQL.

What should you do?

Your company has multiple data analysts but a limited data engineering team. You need to choose a tool where the analysts can build data pipelines themselves with a graphical user interface.

Which of these products is the most appropriate?

You manage a PySpark batch data pipeline by using Dataproc. You want to take a hands-off approach to running the workload, and you do not want to provision and manage your own cluster.

What should you do?

You need to run batch jobs, which could take many days to complete. You do not want to manage the infrastructure provisioning.

What should you do?

You are creating a data pipeline for streaming data on Dataflow for Cymbal Retail's point of sales data. You want to calculate the total sales per hour on a continuous basis.

Which of these windowing options should you use?

You want to build a streaming data analytics pipeline in Google Cloud. You need to choose the right products that support streaming data.

Which of these would you choose?

You have a data pipeline that requires you to monitor a Cloud Storage bucket for a file, start a Dataflow job to process data in the file, run a shell script to validate the processed data in BigQuery, and then delete the original file. You need to orchestrate this pipeline by using recommended tools.

Which product should you choose?

You are running Dataflow jobs for data processing. When developers update the code in Cloud Source Repositories, you need to test and deploy the updated code with minimal effort.

Which of these would you use to build your continuous integration and delivery (CI/CD) pipeline for data processing?

You need to choose a data storage solution to support a transactional system. Your customers are primarily based in one region. You want to reduce your administration tasks and focus engineering effort on building your business application.

What should you do?

You need to store data long term and use it to create quarterly reports.

What storage class should you choose?

You have several large tables in your transaction databases. You need to move all the data to BigQuery for the business analysts to explore and analyze the data.

How should you design the schema in BigQuery?

You are ingesting data that is spread out over a wide range of dates into BigQuery at a fast rate. You need to partition the table to make queries performant.

What should you do?

Your analysts repeatedly run the same complex queries that combine and filter through a lot of data on BigQuery. The data changes frequently. You need to reduce the effort for the analysts.

What should you do?

You have data that is ingested daily and frequently analyzed in the first month. Thereafter, the data is retained only for audits, which happen occasionally every few years. You need to configure cost-effective storage.

What should you do?

You have data stored in a Cloud Storage bucket. You are using both Identity and Access Management (IAM) and Access Control Lists (ACLs) to configure access control.

Which statement describes a user's access to objects in the bucket?

A manager at Cymbal Retail expresses concern about unauthorized access to objects in your Cloud Storage bucket. You need to evaluate all access on all objects in the bucket.

What should you do?

Cymbal Retail has accumulated a large amount of data. Analysts and leadership are finding it difficult to understand the meaning of the data, such as BigQuery columns. Users of the data don't know who owns what. You need to improve the searchability of the data.

What should you do?

You have large amounts of data stored on Cloud Storage and BigQuery. Some of it is processed, but some is yet unprocessed. You have a data mesh created in Dataplex. You need to make it convenient for internal users of the data to discover and use the data.

What should you do?

Your company uses Google Workspace and your leadership team is familiar with its business apps and collaboration tools. They want a cost-effective solution that uses their existing knowledge to evaluate, analyze, filter, and visualize data that is stored in BigQuery.

What should you do to create a solution for the leadership team?

You have data in PostgreSQL that was designed to reduce redundancy. You are transferring this data to BigQuery for analytics. The source data is hierarchical and frequently queried together. You need to design a BigQuery schema that is performant.

What should you do?

You repeatedly run the same queries by joining multiple tables. The original tables change about ten times per day. You want an optimized querying approach.

Which feature should you use?

You have analytics data stored in BigQuery. You need an efficient way to compute values across a group of rows and return a single result for each row.

What should you do?

You need to optimize the performance of queries in BigQuery. Your tables are not partitioned or clustered.

What optimization technique can you use?

Your data in BigQuery has some columns that are extremely sensitive. You need to enable only some users to see certain columns.

What should you do?

Your business has collected industry-relevant data over many years. The processed data is useful for your partners and they are willing to pay for its usage. You need to ensure proper access control over the data.

What should you do?

You have a complex set of data that comes from multiple sources. The analysts in your team need to analyze the data, visualize it, and publish reports to internal and external stakeholders. You need to make it easier for the analysts to work with the data by abstracting the multiple data sources.

What tool do you recommend?

You built machine learning (ML) models based on your own data. In production, the ML models are not giving satisfactory results. When you examine the data, it appears that the existing data is not sufficiently representing the business goals. You need to create a more accurate machine learning model.

What should you do?

You used Dataplex to create lakes and zones for your business data. However, some files are not being discovered.

What could be the issue?

You need to design a Dataproc cluster to run multiple small jobs. Many jobs (but not all) are of high priority.

What should you do?

You need to create repeatable data processing tasks by using Cloud Composer. You need to follow best practices and recommended approaches.

What should you do?

Multiple analysts need to prepare reports on Monday mornings due to which there is heavy utilization of BigQuery. You want to take a cost-effective approach to managing this demand.

What should you do?

You have a team of data analysts that run queries interactively on BigQuery during work hours. You also have thousands of report generation queries that run simultaneously. You often see an error: Exceeded rate limits: too many concurrent queries for this project_and_region.

How would you resolve this issue?

You have a Dataflow pipeline in production. For certain data, the system seems to be stuck longer than usual. This is causing delays in the pipeline execution. You want to reliably and proactively track and resolve such issues.

What should you do?

When running Dataflow jobs, you see this error in the logs: "A hot key HOT_KEY_NAME was detected in…". You need to resolve this issue and make the workload performant.

What should you do?

A colleague at Cymbal Retail asks you about the configuration of Dataproc autoscaling for a project.

What would be the Google-recommended situation when you should enable autoscaling?

Cymbal Retail processes streaming data on Dataflow with Pub/Sub as a source. You need to plan for disaster recovery and protect against zonal failures.

What should you do?

You run a Cloud SQL instance for a business that requires that the database is accessible for transactions. You need to ensure minimal downtime for database transactions.

What should you do?

You are running a Dataflow pipeline in production. The input data for this pipeline is occasionally inconsistent. Separately from processing the valid data, you want to efficiently capture the erroneous input data for analysis.

What should you do?

Which of the following statements on Cloud Storage are true? (pick 3)

Which statement best describes a data lake?

Which of the following statements on BigQuery is incorrect?

True or False: ARRAYS can be a standalone field type or part of a STRUCTS field in BigQuery?

Which of the following is the ideal use case for Extract and Load (EL)

Which of the following statements are true about Dataproc? (Select all 2 correct answers)

Dataproc provides the ability for Spark programs to separate compute and storage by:

Match each of the terms with what they do when setting up clusters in Dataproc:

Term -> Definition

1. Zone --> A. Costs less but may not be available always

2. Standard Cluster mode --> B. Determines the Google data center where compute nodes will be

3. Preemptible --> C. Provides 1 primary and N workers

Match each of the Dataflow terms with what they do in the life of a dataflow job:

Term --> Definition

1. Transform --> A. Output endpoint for your pipeline

2. PCollection --> B. A data processing operation or step in your pipeline

3. Sink --> C. A set of data in your pipeline

Cloud Data Fusion is the ideal solution when you need

A company collects large amounts of data that is useful for improving business operations. The collected data is already clean and is in a format that is suitable for the further analysis. The company uses Google Cloud Bigquery as a data warehouse. What approach will you recommend to move this data to BigQuery?

A company wants to improve productivity and decides to programmatically schedule and monitor workflows. What tool can you use to automate your workflows?

Cymbal Retail collects large amounts of data that is useful for improving business operations. The company wants to store and analyze this data in a serverless and cost-effective manner using Google Cloud. The analysts need to use SQL to write the queries. What tool can you use to meet these requirements?

Cymbal Retail also collects large amounts of structured, semistructured, and unstructured data. The company wants a centralized repository to store this data in a cost-effective manner using Google Cloud. What tool can you use to meet these requirements?

Cymbal Retail has a team of ML engineers that builds and maintains machine learning models. As a Professional Data Engineer, how will you support this team?

You need to share inventory data from Cymbal Retail with a partner company that uses BigQuery to store and analyze its data. What tool can you use to securely and efficiently share the data?

Cymbal Retail uses Google Cloud and has automated repeatable data processing workloads to achieve reliability and cost efficiency. You want out-of-the-box metric collection dashboards and the ability to generate alerts when specific conditions are met. What tool can you use?

Your company recently migrated to Google Cloud and started using BigQuery. The team members don’t know how much querying they are going to do, and they need to be efficient with their spend. As a Professional Data Engineer, what pricing model would you recommend?

There are a number of common challenges encountered by data engineers. Which of the following approaches best address the challenge of data accuracy and quality?

Which of the following statements are true? (Choose TWO)

Which of the following options offered by Dataflow makes it easy to create resilient streaming pipelines when working with unbounded data?

Select TWO correct answers.

When performing batch and streaming data processing, which Google product performs data aggregation and transformation?

Which of the following about Pub/Sub topics and subscriptions are true? (Select all 2 correct responses)

Which of the following about Pub/Sub is NOT true?

Which of the following delivery methods is ideal for subscribers needing close to real time performance?

True or False? Pub/Sub guarantees that messages delivered are in the order they were received

What element is applied by Pub/Sub to messages that allows Dataflow to perform aggregation of different time windows?

Which of the following is true for Data Studio?

True or False? Data Studio can issue queries to BigQuery

Which of the following can help improve performance of Bigtable?

(Select all 3 correct responses)

True or False?

Bigtable learns access patterns and attempts to distribute reads and storage across nodes evenly

Which of the following are true about Bigtable?

(Mark all 3 correct responses)

Which of the following practices help optimize BigQuery queries? (pick three)

What is the primary impact of ML?

What is the difference between AI and ML?

True or False? Most business data is unstructured data, and mainly text.

Google Cloud's pretrained model APIs use:

True or False? Notebooks contain a magic function to execute BigQuery

Select the statement that does not apply to Notebooks.

AI Hub has templates for which of the following?

Which technology was developed as a solution to run Kubernetes clusters and pods behind the scenes to support deploying pipelines?

True or False? You can train and evaluate machine learning models directly in BigQuery.

BigQuery ML has support for which of the following modeling tasks: (pick three)

AutoML makes use of which of the following:

Which of the following are valid techniques for improving AutoML Vision and AutoML Natural Language models? (pick three)

What is the Beam Portability Framework? (Pick 2)

Which of the following are benefits of Beam Portability (Select ALL that apply) ?

Which of the following are TRUE about Flexible Resource Scheduling (select ALL that apply) :

The Dataflow Shuffle service is available only for batch jobs.

What are the benefits of Dataflow Streaming Engine? Select ALL that apply:

You want to run the following command:

gcloud dataflow jobs cancel 2021-01-31_14_30_00-9098096469011826084--region=$REGION

Which of these roles can be assigned to you for the command to work? (pick two)

Your project’s current SSD usage is 100 TB. You want to launch a streaming pipeline with shuffle done on the VM. You set the initial number of workers to 5 and the maximum number of workers to 100. What will be your project’s SSD usage when the job launches?

Your project’s current In-use IP address usage is 500/575. You run the following command:

python3 -m apache_beam.examples.wordcount \

--input gs://dataflow-samples/shakespeare/kinglear.txt \

--output gs://$BUCKET/results/outputs --runner DataflowRunner \

--project $PROJECT --temp_location gs://$BUCKET/tmp/ --region $REGION \

--subnetwork regions/$REGION/subnetworks/$SUBNETWORK \

--num_workers 20 --machine_type n1-standard-4 --no_use_public_ips

What will be the in-use IP address usage after the job starts?

You are a Beam developer for a university in Googleville. Googleville law mandates that all student data is kept within Googleville. Compute Engine resources can be launched in Googleville; the region name is google-world1. Dataflow, however, does not currently have a regional endpoint set up in google-world1. Which flags are needed in the following command to allow you to launch a Dataflow job and to conform with Googleville’s law?

python3 -m apache_beam.examples.wordcount \

--input gs://dataflow-samples/shakespeare/kinglear.txt \

--output gs://$BUCKET/results/outputs --runner DataflowRunner \

--project $PROJECT --temp_location gs://$BUCKET/tmp/ \

When a PTransform receives a PCollection...

How many times will be the process/processElement method of a DoFn called?

What is CoGroupByKey used for?

What are the types of windows that you can use with Beam?

How many triggers can a window have?

How does Apache Beam decide that a message is late?

What can you do if two messages arrive at your pipeline out of order?

What is the simplest form of a sink?

What kinds of data are a bounded and an unbounded source respectively associated with?

Which of the following element types can be encoded as a Schema from a PCollection (Select ALL that apply)?

Is it possible to mix elements in Schema PCollections inside a single Beam pipeline (pick two)?

With ParDo, you...

What is the use case of timers in the State & Timers API of Beam?

Choose all the applicable options: If your pipelines interact with external systems (pick two)

What is the recommended way to convert JSON objects to POJOs?

Which functions of the DoFn lifecycle are recommended to be used for micro-batching?

Which two of the following interfaces support Calcite SQL? (pick two)

What operations can you do in standard Pandas DataFrames that are not possible in Beam DataFrames?

Which one of these statements is true?

Which two of the following statements are true about using the interactive runner? (pick two)

You have a Pub/Sub subscription with data that hasn’t been processed for 3 days. You set up a streaming pipeline that reads data from the subscription, does a few Beam transformations, and then sinks to Cloud Storage. When the pipeline is launched, it is able to read from Pub/Sub, but cannot sink data to Cloud Storage due to the service account missing permissions to write to the bucket. When viewing the Job Metrics tab, what do you expect to see in the data freshness graph?

You would like to set up an alerting policy to catch whether the processeddata isstill fresh in a streaming pipeline. Which metrics can be used to monitor whether the processeddata isstill fresh? (pick two)

Your batch job has failed, and when viewing the Diagnostic tab, you see the following insights: Out of memory: Kill process Shutting down JVM after consecutive periods of measured GC thrashing Which of the options below is the best one to undertake to resolve the issue?

Select all that apply - the BigQuery Jobs tab shows jobs from: (pick two)

Which two of the following statements are true for failures while building the pipeline? (pick two)

Your Dataflow batch job fails after running for close to 5 hours. Which two of the following troubleshooting steps would you take to understand the root cause of the failure? (pick two)

Select options we can use to mitigate data skew in Dataflow pipelines?

When we should avoid fusion in a Dataflow pipeline?

Which one of the following is not a consideration for designing performant pipelines in Dataflow?

Using anonymous subclasses in your ParDos is an anti-pattern because:

When draining a streaming pipeline, what should you expect to happen?

How long is the retention for Dataflow Snapshots?

You want to launch a streaming Dataflow job in europe-west4 and want to protect your pipeline from zonal stockouts. Which launch command will achieve these requirements?

Into which of the following categories are the Google-provided templates classified?

Which of the following is a challenge associated with classic templates?

How are Flex Templates packaged?

You are working as a Data Engineer at a Gaming startup. The business team is complaining about the late arrival of prediction data of the demand forecast process. You noticed that current data transformation on the structured data built with PySpark is taking over 14 hours to run.

Raw data are parquet files stored in Cloud Storage and its size is about 1TB. You have been asked to speed up the run time while minimizing infrastructure management tasks using SQL for data transformation, and reducing the data processing time by 50%.

How should you enhance the data transformation task to reduce the data processing time?

What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

Your company has a multi-year backup of data in parquet format. you have been asked to use Google Cloud Services to move this backup, conduct preliminary analysis using ANSI SQL and then automate transformations with Apache Spark.

What are the appropriate Google Cloud Services to store and transform the parquet files?

You are working as a lead data engineer, the team is working on a classification model and has already validated steps related to model development, performance, quality, and infrastructure. The governance team was asked to visually analyze the model's predictive behavior to understand the importance of different data features.

How should you fulfill the governance team requirement before the production deployment? 

AdMediaPlus is an Advertising agency that manages ad campaigns and analytics for their customers. They have a requirement to analyze Youtube channel reports and Youtube Content Owner reports using ANSI SQL. What is the straightforward approach that fits this requirement?

You have been asked as a Data Engineer to build a pipeline that processes 206 of scanned documents stored as images in Cloud Storage. 

The pipeline should run monthly to analyze and extract entities from each document and export it to several internal applications.

You need to ensure the accuracy of data extracted by humans by reviewing, validating, and correcting it when necessary. You want to minimize development time. 

How should you implement the pipeline that fits the requirement above?

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

BigAnalytics company is using Dataflow for their data pipelines. the dataflow jobs are scheduled on a daily basis at 1am and read parquet files from GCS, apply necessary transformations then store the data into BigQuery. 

You noticed that there is late arrival data appended to the original files after the daily execution finished. What should you do to process late arrival data as soon as it's written on parquet files that guarantee operational efficiency?

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

What are 3 techniques you can use to reduce overfitting in a neural network? (Select 3 answers)

You are building a new real-time data warehouse for your company and will use Google BigQuery streaming inserts. 

There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

What is the HBase Shell for Cloud Bigtable?

You are working as a Data Engineer for a supply chain company. The company is using a Cloud Composer to run hundreds of DAGs. 

Following a security audit, you have been asked to change the current composer environment configuration from public to private IP nodes for the GKE cluster used by Cloud Composer. 

You noticed after making such a change that some of the CI/CD stages to update Cloud Composer variables using “gcloud composer environments update” are failing. 

What's the most likely reason for the failing task of updating the Cloud Composer variables?

You have been asked to ingest data from a new data source that resides in BigQuery, to rename different columns before storing it in the target dataset/table incrementally for weekly execution. The target solution needs to be easy and quick to set up. 

Your current project is already using the following GCP products: Cloud Composer, Dataflow, and Dataproc.

What should you do?

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:

• 1) No interaction by the user on the site for 1 hour

• 2) Has added more than $30 worth of products to the basket

• 3) Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

You are working as a Data Engineer in an Analytics startup that manages a Cloud Composer cluster. You deployed a DAG that contains a BigQueryOperator that connect to GoogleAnalytics dataset to collect yesterday's data from intraday tables

suffixed by the date. 

The DAG is scheduled daily at 2AM. the execution history shows the following error appeared randomly at some days then cause the DAG failure. Table does not exist. 

You would like to avoid DAG failure and process the data once available

What should you do?

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's data. You want to ensure appropriate access to the data. 

Which three steps should you take? (Choose three.)

You have the following SQL query : 

WITH Items AS (SELECT ["coffee”, "tea", "milk"] AS item_array) 

SELECT 

item_array,

item_array[OFFSET(1)] AS item_offset_1, 

item_array[OFFSET(2)] AS item_offset_2, 

item_array[_______(3)] AS item_offset_3, 

FROM Items 

What is the missing expression that will lead to the following output? 

Item_array | item_offset_1 | item_offset_2 | item_offset_3

[coffee, tea, milk] | tea | coffee | NULL

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. 

Your user base could grow exponentially, but you do not want to manage infrastructure scaling. 

Which Google database service should you use?

Your company is planning to use Bigtable as part of their production workloads and you have been asked to design the target schema, tables, columns, and rows. High throughput volume is expected once in a production environment.

What are the main principles to take into consideration for the schema design it? Select two choices.

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristics support this method? (Choose two.)

Your company is migrating its structured data currently on-premises to google cloud. you have been asked to design the appropriate storage to run large analytical workloads with minimum latency that doesn’t exceed milliseconds. What is the appropriate storage to choose?

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

You are working for a consulting company that offers BigQuery optimization services.

One of the company’s customers is using a very large table to store orders containing fields, customer_id, order_id, order_date, amount, and using several queries based on order_date and customer_id filters. The Queries execution time is increasing over time.

What should you do to enhance the query execution time on BigQuery?

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

Your company is using Google Cloud Al Platform (Vertex Al) to fine tune its classification model built to serve monthly predictions for the CRM team. The predictions to send to the CRM team are scores and tabular data for each customer.

You have been asked to industrialize the ML Model and push it into the production environment.

Your Company is already using Cloud Dataflow, Cloud Composer, Cloud Dataproc

What is the most cost-effective solution that guarantees production industrialization, async delivery to the CRM, and backfilling when needed?

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

Which of these statements about BigQuery caching is true?

Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for- like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Your company launched an image recognition app a few months ago. Users are complaining about wrong classifications for some categories.

The application is backed by a custom ML model developed using sci-kit learn and deployed on the Al Platform, the observed metrics show an increased latency when making predictions for two weeks.

You have been asked to solve this issue permanently while minimizing the time to market to release a new version while writing a minimum of code.

What should you do?

You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

You are working as a Data Engineer and you were given 100G of ORC data format files stored in Google Cloud Storage. You have been asked to make an SQL ad hoc analysis and share the results with the business team. The same ORC data will be reused for churn prediction use cases with BigQueryML in a few days and query performance is a top priority.

How should you perform the analysis while taking into consideration the future use of the same Data?

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiently?

You are planning to use the Beam SDK to read customer data from a BigQuery table. Although the table has numerous columns, you only need to read the first two columns. Which method would be the most efficient?

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labeled datasets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks.

What should you do?

You are working in an e-commerce startup and you have been asked to rapidly experiment with the outcome of the recommendations algorithm given historical customer behavior, transactions, and product ratings already stored in BigQuery as a potential future feature of the product you are working on to recommend personalized customer experiences.

How should you build and make batch recommendations on the current customer base as quickly as possible with minimal development?

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Your company has historic tabular data about customer transactions, orders, and payments tables stored in BigQuery, partitioned and clustered. The company uses BigQuery for most of its analytics workloads and uses the on-demand pricing model. BigQuery monthly bill is increasing and the management asked to check the possibilities to enhance and optimize those costs.

How should you reduce the monthly bill on BigQuery? (Choose 2 answers)

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Your company has historic tabular data about customer transactions, orders and payments stored in BigQuery. The company wants to add a customer segmentation module and populate the results back to BigQuery and PubSub to be consumed by the CRM.

The segmentation process is rule-based according to given business inputs that can be processed via SQL. Your company is already using Cloud Dataflow, Cloud Composer, Cloud Dataproc.

How should you implement such a solution with minimal development and operational effort?

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Which methods can be used to reduce the number of rows processed by BigQuery?

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

Which layer of a convolutional neural network is normally used to perform downsampling or dimensionality reduction?

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

You are working as a Data Engineer at a large company relying on BigQuery as a Data Warehouse. You executed the SQL query

#standardSQL

SELECT

COUNT(*) AS rows

FROM

“bigquery-public-data.samples.shakespeare’;

and you received the following error

Syntax error: Unexpected keyword ROWS at [3:15]

How to solve this issue while keeping the same alias name for the count(*) statement?

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for new key features in the logs.

BigQueryIO.Read

named (“ReadLogData”)

.from(“clouddataflow-readonly:samples.log_data”)

You want to improve the performance of this data read. What should you do?

Your Company is managing a supply chain product globally accessible. The company has several partners across different countries that need to access BigQuery country-specific tables for historic data analysis for each country.

What should you do to guarantee that each partner accesses only their specific tables on BigQuery without altering the data?

You are working as a Data Engineer for a supply chain startup. you were given access to a data source containing location data in a GeoJson format and have been asked to store and analyze those data using SQL.

What is the appropriate Google Cloud Storage service to perform SQL analysis?

Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations.

The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

You are working for a Data Analytics company that has several cloud composer DAGs for batch prediction jobs launched on Al Platform on a daily schedule.

You need to monitor those jobs’ behavior for errors raised once launched on the Al platform, and then notify the team if they fail.

The company prefers managed service usage with minimum development effort and infrastructure management.

How should you set up the alerting using native GCP functionalities?

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

You are working as a Data Engineer at a Gaming startup that recently launched a new game called “MeetTheWorld”. You have been asked to make a real-time query of active gamers’ counts every minute using SQL and store the results in BigQuery. MeetTheWorld game is using CloudSQL as a backend and a pubsub stream for every subscriber that connects and disconnects from the game.

The Startup is already using Cloud Composer, Cloud Dataflow, and Bigquery for their tech stack

How should you implement the requirement?

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster? (Choose 2 answers)

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.

What should you do?

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store: 

1. 1) The user profile: What the user likes and doesn't like to eat

2. 2) The user account information: Name, address, preferred meal times 

3. 3) The order information: When orders are made, from where, to whom 

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

You are using SaaS services to collect data from various sources in JSON format to produce a competitive intelligence report on a monthly schedule and planned to be real-time in the near future. The Data processing stage needs to store outputs in BigQuery. Select the best architecture choice that requires minimum operational and infrastructure maintenance involvement.

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (loT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID).

However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.

How should you securely run this workload?

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

xxxxxxxxoooooooooooooooooo

What is the most likely cause of the delay for this query?

You are managing a team of Data Engineers, Data Scientists and Data Analysts. The team is using Vertex Al Worbench- managed notebooks for Ad hoc analysis according to each requirement with access to BigQuery and GCS.

The existing notebooks are created using the project default service account and accessible for everyone which causes security issues.

How should you reorganize / set up the notebook management on Vertex Al Workbench?

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?