You need to deploy additional dependencies to all nodes of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

Create a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

Create a table in BigQuery, and append the new samples for CPU and memory to the table

Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

Create a wide table in Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the Trust No One (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once.

Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

You have data pipelines running on BigQuery, Dataflow, and Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products or features of the platform. What should you do?

Export the information to Cloud Monitoring, and set up an Alerting policy

Run a Virtual Machine in Compute Engine with Airflow, and export the information to Cloud Monitoring

Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs

Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

Use SQL in BigQuery to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Create a new view with BigQuery that does not include a column with city information.

Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file and upload that as part of your model to BigQuery ML.

Use Cloud Data Fusion to assign each city to a region that is labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.

You work for a large bank that operates in locations throughout North America. You are setting up a data storage system that will handle bank account transactions. You require ACID compliance and the ability to access data with SQL. Which solution is appropriate?

Store transaction in Cloud Spanner. Use locking read-write transactions.

Store transaction data in Cloud Spanner. Enable stale reads to reduce latency.

Store transaction data in BigQuery. Disabled the query cache to ensure consistency.

Store transaction data in Cloud SQL. Use a federated query BigQuery for analysis.

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 package-tracking ID column.

Implement clustering in BigQuery on the ingest date column.

Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.

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

Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

Migrate the workloads to Dataproc plus Cloud Storage; modernize later.

Migrate the workloads to Dataproc plus HDFS; modernize later.

Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.

Modernize the Spark workload for Dataflow and the Hive workload for BigQuery.

You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?

Use BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.

Use BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.

Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.

Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.

You are migrating a table to BigQuery and are deciding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID, and the city and state in which the store is located. You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state, city, and individual store. How would you model this table for the best query performance?

Partition by transaction time; cluster by state first, then city, then store ID.

Partition by transaction time; cluster by store ID first, then city, then state.

Top-level cluster by state first, then city, then store ID.

Top-level cluster by store ID first, then city, then state.

You are updating the code for a subscriber to a Pub/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. Your subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created.

Set up the Pub/Sub emulator on your local machine. Validate the behavior of your new subscriber logic before deploying it to production.

Use Cloud Build for your deployment. If an error occurs after deployment, use a Seek operation to locate a timestamp logged by Cloud Build at the start of the deployment.

Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successfully acknowledged. If an error occurs after deployment, re-deliver any messages captured by the dead-letter queue.

You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.

When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE.

Use a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.

Preprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.

You are analyzing the price of a company's stock. Every 5 seconds, you need to compute a moving average of the past 30 seconds' worth of data. You are reading data from Pub/Sub and using DataFlow to conduct the analysis. How should you set up your windowed pipeline?

Use a sliding window with a duration of 30 seconds and a period of 5 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow ()

Use a fixed window with a duration of 5 seconds. Emit results by setting the following trigger: AfterProcessingTime.pastFirstElementInPane().plusDelayOf (Duration.standardSeconds(30))

Use a fixed window with a duration of 30 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow().plusDelayOf (Duration.standardSeconds(5))

Use a sliding window with a duration of 5 seconds. Emit results by setting the following trigger: AfterProcessingTime.pastFirstElementInPane().plusDelayOf (Duration.standardSeconds(30))

You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?

Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs the necessary aggregations using tumbling windows.

Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.

Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.

Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations

You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer's stated intention for contacting customer service. About 70% of customer requests are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer, more complicated requests. Which intents should you automate first?

Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests.

Automate the more complicated requests first because those require more of the agents' time.

Automate a blend of the shortest and longest intents to be representative of all intents.

Automate intents in places where common words such as 'payment' appear only once so the software isn't confused.

Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model. You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days. Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

Partition the data by transaction date.

Materialize the dimensional data in views.

You have uploaded 5 years of log data to Cloud Storage. A user reported that some data points in the log data are outside of their expected ranges, which indicates errors. You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

Create a Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage.

Import the data from Cloud Storage into BigQuery. Create a new BigQuery table, and skip the rows with errors

Create a Compute Engine instance and create a new copy of the data in Cloud Storage. Skip the rows with errors.

Create a Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage.

You want to rebuild your batch pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run. To expedite development and pipeline run time, you want to use a serverless tool and SOL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Convert your PySpark commands into SparkSQL queries to transform the data and then run yur pipeline on Dataproc to write the data into BigQuery

Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data and then use federated qeunes from BigQuery for machine learning

Use Apache Beam Python SDK to build the transformation pipelines and write the data into BigQuery

You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using SideInputs to join data. You noticed that the pipeline is taking longer to complete than expected; what should you do to expedite the Dataflow job?

Use CoGroupByKey instead of this SideInput

Switch to compressed Avro files

Retry records that throw an error

You are building a real-time prediction engine that streams files, which may contain PII (personal identifiable information) data, into Cloud Storage and eventually into BigQuery. You want to ensure that the sensitive data is masked but still maintains referential integrity, because names and emails are often used as join keys. How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the PII data is not accessible by unauthorized individuals?

Create a pseudonym by replacing PII data with a cryptographic format-preserving token.

Create a pseudonym by replacing the PII data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.

Redact all PII data, and store a version of the unredacted data in a locked-down bucket.

Scan every table in BigQuery, and mask the data it finds that has PII.

You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery. In your current relational database, the author information is kept in a separate table and joined to the book information on a common key. Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

Create a table that includes information about the books and authors, but nest the author fields inside the author column.

Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today.

Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

Keep the schema the same, create a view that joins all of the tables, and always query the view.

CertyIQ - Google - Prof Data Eng - pt 6

Authored by Katheryne Pierce

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MULTIPLE CHOICE QUESTION

15 mins • 1 pt

You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Use Vertex AI for training existing Spark ML models

Rewrite your models on TensorFlow, and start using Vertex AI

Use Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

BigQuery

Cloud Bigtable

Cloud Datastore

Cloud SQL for PostgreSQL

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

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

Use Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from 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.

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

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?

Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.

Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

Apache Kafka

Cloud Storage

Dataflow

Firebase Cloud Messaging

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

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 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 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://

MULTIPLE CHOICE QUESTION

15 mins • 1 pt

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

Perform hyperparameter tuning

Train a classifier with deep neural networks, because neural networks would always beat SVMs

Deploy the model and measure the real-world AUC; it's always higher because of generalization

Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

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CertyIQ - Google - Prof Data Eng - pt 6

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?

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 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?

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

You work for a mid-sized enterprise that needs to move its operational system transaction data from an onpremises database to GCP. The database is about 20 TB in size. Which database should you choose?

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