Free Printable Training Data Exam

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Training Data Exam

Total questions: 99

Worksheet time: 50mins

Name

Class

Date

1. Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Threading

Serialization

Dropout Methods

Dimensionality Reduction

2. 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?

Use federated data sources, and check data in the SQL query.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

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

Issue a command to restart the database servers.

Retry the query with exponential backoff, up to a cap of 15 minutes.

Retry the query every second until it comes back online to minimize staleness of data.

Reduce the query frequency to once every hour until the database comes back online.

4. 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?

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

5. You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules: • No interaction by the user on the site for 1 hour • Has added more than $30 worth of products to the basket • 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?

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

6. 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.

Create a Google Cloud Dataflow job to process the data.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

7. 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.

Put the data into Google Cloud Storage.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

8. 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.

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

9. 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.

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

10.

11. 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.

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

11.

12. 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: - The user profile: What the user likes and doesn't like to eat - The user account information: Name, address, preferred meal times - 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?

BigQuery

Cloud SQL

Cloud Bigtable

Cloud Datastore

12.

13. 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?

Introduce data compression for each file to increase the rate file of file transfer.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.

13.

14. You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) 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.)

HBase

MongoDB

Cassandra

Redis

MySQL

14.

15. 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: (View Image) What is the most likely cause of the delay for this query?

Users are running too many concurrent queries in the system

The [myproject:mydataset.mytable] table has too many partitions

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

15.

16. 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?

Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

16.

17. 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.)

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

17.

18. Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

Migrate the workload to Google Cloud Dataflow

Use pre-emptible virtual machines (VMs) for the cluster

Use a higher-memory node so that the job runs faster

Use SSDs on the worker nodes so that the job can run faster

18.

19. Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

19.

20. You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices.

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

20.

21. You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

21.

22. 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?

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

22.

23. Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

Cloud Bigtable

Google BigQuery

Google Cloud Storage

Google Cloud Datastore

23.

24. 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?

PigLatin using Pig

HiveQL using Hive

Java using MapReduce

Python using MapReduce

24.

25. You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Place the MariaDB instances in an Instance Group with a Health Check.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the StackDriver Agent and configure the MySQL plugin.

25.

26. You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern. Which service do you select for storing and serving your data?

Cloud Spanner

Cloud Bigtable

Cloud Firestore

Cloud SQL

26.

27. 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?

Batch job, PubSubIO, side-inputs

Streaming job, PubSubIO, JdbcIO, side-outputs

Streaming job, PubSubIO, BigQueryIO, side-inputs

Streaming job, PubSubIO, BigQueryIO, side-outputs

27.

28.You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps. You have the following requirements: - You will batch-load the posts once per day and run them through the Cloud Natural Language API. - You will extract topics and sentiment from the posts. - You must store the raw posts for archiving and reprocessing. - You will create dashboards to be shared with people both inside and outside your organization. You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

Store the social media posts and the data extracted from the API in BigQuery.

Store the social media posts and the data extracted from the API in Cloud SQL.

Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

Feed to social media posts into the API directly from the source, and write the extracted data from the API into

28.

29. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

cron

Cloud Composer

Cloud Scheduler

Workflow Templates on Cloud Dataproc

29.

30. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the

30.

31. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

Cloud Speech-to-Text API

Cloud Natural Language API

Dialogflow Enterprise Edition

Cloud AutoML Natural Language

31.

32.You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

32.

33. You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements: - Each department should have access only to their data. - Each department will have one or more leads who need to be able to create and update tables and provide them to their team. - Each department has data analysts who need to be able to query but not modify data. How should you set access to the data in BigQuery?

Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data

Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data

analysts the role of READER on their dataset.

Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts

the role of Editor on the project the table is in.

33.

34. You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time

Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class

Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved

Use managed export, and then import the data into a BigQuery table created just for that export, and delete

Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery

Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a

34.

35. 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?

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

35.

36. 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?

Implement BigQuery with no region selected for storage or processing.

Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.

Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

36.

37. You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways: - Real-time event stream - ANSI SQL access to real-time stream and historical data - Batch historical exports Which solution should you use?

Cloud Dataflow, Cloud SQL, Cloud Spanner

Cloud Pub/Sub, Cloud Storage, BigQuery

Cloud Dataproc, Cloud Dataflow, BigQuery

Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

37.

38. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Create an API using App Engine to receive and send messages to the applications

Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Create a table on Cloud SQL, and insert and delete rows with the job information

Create a table on Cloud Spanner, and insert and delete rows with the job information

38.

39. 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?

Use Transfer Appliance to copy the data to Cloud Storage

Use gsutil cp ""J to compress the content being uploaded to Cloud Storage

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic

39.

40. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include: - Executing the transformations on a schedule - Enabling non-developer analysts to modify transformations - Providing a graphical tool for designing transformations What should you do?

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

40.

41. You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

41.

42. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

42.

43. 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?

Apache Kafka

Cloud Storage

Cloud Pub/Sub

Firebase Cloud Messaging

43.

44. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

44.

45. You need to choose a database for a new project that has the following requirements: - Fully managed - Able to automatically scale up - Transactionally consistent - Able to scale up to 6 TB - Able to be queried using SQL Which database do you choose?

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore

45.

46. What are two of the benefits of using denormalized data structures in BigQuery?

Reduces the amount of data processed, reduces the amount of storage required

Increases query speed, makes queries simpler

Reduces the amount of storage required, increases query speed

Reduces the amount of data processed, increases query speed

46.

47. Which of the following are examples of hyperparameters? (Select 2 answers.)

Number of hidden layers

Number of nodes in each hidden layer

Biases

Weights

47.

48. Which of the following are feature engineering techniques? (Select 2 answers)

Hidden feature layers

Feature prioritization

Crossed feature columns

Bucketization of a continuous feature

48.

49. You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

Both batch and streaming

BigQuery cannot be used as a sink

Only batch

Only streaming

49.

50. You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

Cancel

Drain

Stop

Finish

50.

51. Which of the following statements is NOT true regarding Bigtable access roles?

Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

You can configure access control only at the project level.

To give a user access to only one table in a project, you must configure access through your application.

51.

52. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Include multiple time series values within the row key

Keep the row keep as an 8 bit integer

Keep your row key reasonably short

Keep your row key as long as the field permits

52.

53. All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

before

after

only if

once

53.

54. In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

VPN connection

Special browser

SSH tunnel

FTP connection

54.

55. The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

application node

conditional node

master node

worker node

55.

56. Cloud Dataproc charges you only for what you really use with _____ billing.

month-by-month

minute-by-minute

week-by-week

hour-by-hour

56.

57. Scaling a Cloud Dataproc cluster typically involves ____.

increasing or decreasing the number of worker nodes

increasing or decreasing the number of master nodes

moving memory to run more applications on a single node

deleting applications from unused nodes periodically

57.

58. Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.

details

value

null

58.

59. Which action can a Cloud Dataproc Viewer perform?

Submit a job.

Create a cluster.

Delete a cluster.

List the jobs.

59.

60. Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

Blaze

Spark

Fire

Ignite

60.

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

HTTPS

VPN

SOCKS

HTTP

61.

62. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

Preemptible workers cannot use persistent disk.

Preemptible workers cannot store data.

If a preemptible worker is reclaimed, then a replacement worker must be added manually.

A Dataproc cluster cannot have only preemptible workers.

62.

63. When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

zone

node

label

type

63.

64. Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

Dataproc Worker

Dataproc Viewer

Dataproc Runner

Dataproc Editor

64.

65. What are the minimum permissions needed for a service account used with Google Dataproc?

Execute to Google Cloud Storage; write to Google Cloud Logging

Write to Google Cloud Storage; read to Google Cloud Logging

Execute to Google Cloud Storage; execute to Google Cloud Logging

Read and write to Google Cloud Storage; write to Google Cloud Logging

65.

66. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Hive

Pig

YARN

Spark

66.

67. By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Windows at every 100 MB of data

Single, Global Window

Windows at every 1 minute

Windows at every 10 minutes

67.

68. Which of the following is not true about Dataflow pipelines?

Pipelines are a set of operations

Pipelines represent a data processing job

Pipelines represent a directed graph of steps

Pipelines can share data between instances

68.

69. Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

dataflow.worker

dataflow.compute

dataflow.developer

dataflow.viewer

69.

70. You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

PCollection

Transform

Pipeline

Sink API

70.

71. Which of the following is NOT true about Dataflow pipelines?

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

71.

72. 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?

Cloud Bigtable

BigQuery

Cloud SQL

Cloud Datastore

72.

73. 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?

Disable caching by editing the report settings.

Disable caching in BigQuery by editing table details.

Refresh your browser tab showing the visualizations.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

73.

74. 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?

Use Google Stackdriver Audit Logs to review data access.

Get the identity and access management IIAM) policy of each table

Use Stackdriver Monitoring to see the usage of BigQuery query slots.

Use the Google Cloud Billing API to see what account the warehouse is being billed to.

74.

75. 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 the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

75.

76. 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?

Update the current pipeline and use the drain flag.

Update the current pipeline and provide the transform mapping JSON object.

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

76.

77. 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?

Check the dashboard application to see if it is not displaying correctly.

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

77.

78. 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?

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

78.

79. 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?

Use the TABLE_DATE_RANGE function

Use the WHERE_PARTITIONTIME pseudo column

Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD

Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

79.

80. 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?

They have not assigned the timestamp, which causes the job to fail

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

They have not applied a global windowing function, which causes the job to fail when the pipeline is created

They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

80.

81. 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?

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

81.

82. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application. They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

82.

83. 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?

Convert all daily log tables into date-partitioned tables

Convert the sharded tables into a single partitioned table

Enable query caching so you can cache data from previous months

Create separate views to cover each month, and query from these views

83.

84. You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

Create groups for your users and give those groups access to the dataset

Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

84.

85. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.

Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

85.

86. Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.

In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.

In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

86.

87. After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison. What should you do?

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

87.

88. You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account. What should you do?

Convert your batch BQ queries into interactive BQ queries.

Create an additional project to overcome the 2K on-demand per-project quota.

Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

88.

89. 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?

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read from PubSub and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read from PubSub and write to GCS.

89.

90. Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data). What should you do?

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

90.

91. You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload. What should you do?

Export Bigtable dump to GCS and run your analytical job on top of the exported files.

Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.

Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

91.

92. 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?

Use Cloud Dataflow with Beam to detect errors and perform transformations.

Use Cloud Dataprep with recipes to detect errors and perform transformations.

Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

Use federated tables in BigQuery with queries to detect errors and perform transformations.

92.

93. Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

Execute gsutil rsync from the on-premises servers.

Use Cloud Dataflow and write the data to Cloud Storage.

Write a job template in Cloud Dataproc to perform the data transfer.

Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

93.

94. You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

94.

95. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Create a cron schedule in Cloud Dataprep.

Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

95.

96. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

96.

97. You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

Publisher throughput quota is too small.

Total outstanding messages exceed the 10-MB maximum.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

The subscriber code does not acknowledge the messages that it pulls.

97.

98. Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

Cloud Dataflow

Cloud Composer

Cloud Dataprep

Cloud Dataproc

98.

99. You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

99.

100. A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Implement clustering in BigQuery on the ingest date column.

Implement clustering in BigQuery on the package-tracking ID column.

Tier older data onto Cloud Storage files, and leverage extended tables.

Re-create the table using data partitioning on the package delivery date.

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Training Data Exam

Total questions: 99

Worksheet time: 50mins

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