You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

Assign the compute. networkUser role to the Dataflow service agent.

Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

Assign the dataflow, admin role to the Dataflow service agent.

Assign the dataflow, admin role to the service account that executes the Dataflow pipeline.

You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

Build Dataform assertions into your code

Write a Spark-based stored procedure.

Build BigQuery user-defined functions (UDFs).

Create Dataplex data quality tasks.

You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore

Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore

Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore

Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.

Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.

You orchestrate ETL pipelines by using Cloud Composer One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed. What should you do?

Assign a function with notification logic to the on_failure_callback parameter for the operator responsible for the task at risk.

Configure a Cloud Monitoring alert on the sla_missed metric associated with the task at risk to trigger a notification.

Assign a function with notification logic to the sla_miss_callback parameter for the operator responsible for the task at risk.

Assign a function with notification logic to the on_retry_callback parameter for the operator responsible for the task at risk.

Assign a function with notification logic to the on_failure_callback parameter for the operator responsible for the task at risk.

You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do?Choose 2 answers

Use Pub/Sub Seek with a timestamp.

Use Pub/Sub Snapshot capture two days before the deployment.

Use Pub/Sub Seek with a timestamp.

Use the Pub/Sub subscription clear-retry-policy flag.

Create a new Pub/Sub subscription two days before the deployment.

Use the Pub/Sub subscription retain-asked-messages flag.

Use Pub/Sub Snapshot capture two days before the deployment.

You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.

Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify the Dataflow pipeline and new clients to use all instances

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.

Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete the old instance.

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.

You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another Your networking team relies on Google Cloud network tags to define firewall rules You need to identify the issue while following Google-recommended networking security practices. What should you do?

Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.

Determine whether your Dataflow pipeline has a custom network tag set.

Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.

Determine whether your Dataflow pipeline is deployed with the external IP address option enabled.

Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.

You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

Disable external IP addresses from worker VMs and enable Private Google Access.

Leave external IP addresses assigned to worker VMs while enforcing firewall rules.

Disable external IP addresses and establish a Private Service Connect endpoint IP address.

Disable external IP addresses from worker VMs and enable Private Google Access.

Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

You work for a large ecommerce company. You store your customers order data in Bigtable. You have a garbage collection policy set to delete the data after 30 days and the number of versions is set to 1. When the data analysts run a query to report total customer spending, the analysts sometimes see customer data that is older than 30 days. You need to ensure that the analysts do not see customer data older than 30 days while minimizing cost and overhead. What should you do?

Use a timestamp range filter in the query to fetch the customer's data for a specific range.

Set the expiring values of the column families to 30 days and set the number of versions to 2.

Use a timestamp range filter in the query to fetch the customer's data for a specific range.

Set the expiring values of the column families to 29 days and keep the number of versions to 1.

Schedule a job daily to scan the data in the table and delete data older than 30 days.

You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class. 2 Set a locked retention policy on the bucket. 3. Create a BigQuery external table on the exported files.

1 Create a BigQuery table clone. 2. Query the clone when you need to perform analytics.

1 Create a BigQuery table snapshot. 2 Restore the snapshot when you need to perform analytics.

1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class. 2 Enable versionmg on the bucket. 3. Create a BigQuery external table on the exported files.

1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class. 2 Set a locked retention policy on the bucket. 3. Create a BigQuery external table on the exported files.

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1 Use a regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by one day to recover the acknowledged messages 4 Start the Dataflow job in a secondary region and write in a bucket in the same region

1 Use a multi-regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1. Use a dual-region Cloud Storage bucket with turbo replication enabled 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region.

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1 Use a regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by one day to recover the acknowledged messages 4 Start the Dataflow job in a secondary region and write in a bucket in the same region

1 Use a multi-regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

1. Use a dual-region Cloud Storage bucket with turbo replication enabled 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region.

You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding What should you do?

Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.

Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.

Create a Spark job and submit it to Dataproc Serverless.

Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.

You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type. 2. Load the booking and user profile data into a BigQuery table.

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type. 2. Load the booking and user profile data into a BigQuery table.

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type. 2. Load the booking and user profile data into a BigQuery table.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the email mask as the data masking rule. 3. Assign the policy to the email field in both tables. A 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking. 2. Create a policy tag with the default masking value as the data masking rule. 3. Assign the policy to the email field in both tables. 4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries. What should you do?

Create a sale3_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.

Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.

Create a sale3_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.

Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.

You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow You want to improve the performance of these queries What should you do?

Upgrade the external table to a BigLake table Enable metadata caching for the table.

Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

Change the storage class of the Hive partitioned data objects from Coldline to Standard.

Upgrade the external table to a BigLake table Enable metadata caching for the table.

Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.

Use a Cloud SQL for PostgreSQL highly available instance first, and 8gtable with US. Europe, and Asia replication alter securing funding

Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.

You are designing a system that requires an ACID-compliant database. You must ensure that the system requires minimal human intervention in case of a failure. What should you do?

Configure a Cloud SQL for PostgreSQL instance with high availability enabled.

Configure a Cloud SQL for MySQL instance with point-in-time recovery enabled.

Configure a Cloud SQL for PostgreSQL instance with high availability enabled.

Configure a Bigtable instance with more than one cluster.

Configure a BJgQuery table with a multi-region configuration.

You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.

Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the 'ingestion' dataset as the training data.

Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.

Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.

You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?

Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.

Use Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision the infrastructure.

Use GKE to autoscale containers, and use gloud commands to provision the infrastructure.

Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.

Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.

You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?

Increase the size of the training dataset, and decrease the number of input features.

Increase the size of the training dataset, and increase the number of input features.

Increase the size of the training dataset, and decrease the number of input features.

Reduce the size of the training dataset, and increase the number of input features.

Reduce the size of the training dataset, and decrease the number of input features.

You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.

Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.

Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.

Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-code option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

Use Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.

Use the Speech-to-Text API to build a Python application in App Engine.

Use the Speech-to-Text API to build a Python application in a Compute Engine instance.

Use Dialogflow for simple queries and the Speech-to-Text API for complex queries.

Use Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

Use Data Fusion to transform the data before loading it into BigQuery.

Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.

Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

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?

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?

Use Pub/Sub exactly-once delivery in your pull subscription.

Create a transactional database that monitors the pending messages.

Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.

Use Pub/Sub exactly-once delivery in your pull subscription.

Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.

You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult You need to quickly identify and implement a cost-optimized solution to assist your organization with the following* Data management and discovery* Data lineage tracking* Data quality validationHow should you build the solution?

Use Dataplex to manage data, track data lineage, and perform data quality validation.

Use BigLake to convert the current solution into a data lake architecture.

Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.

Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.

Use Dataplex to manage data, track data lineage, and perform data quality validation.

Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:* The old data can be deleted anytime* You plan to use the visualization layer for current and historical reporting* The old data should be available instantly when accessed* There should not be any charges for data retrieval.What should you do to optimize for cost?

Create the bucket with the Autoclass storage class feature.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.

You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?

Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.

Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.

Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.

Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to 30 days.

Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.

You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?

Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.

Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.

Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.

Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.

Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.

You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

Use watermarks to define the expected data arrival window Allow late data as it arrives.

Change your windowing function to session windows to define your windows based on certain activity.

Change your windowing function to tumbling windows to avoid overlapping window periods.

Expand your hopping window so that the late data has more time to arrive within the grouping.

Use watermarks to define the expected data arrival window Allow late data as it arrives.

You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data for an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of data. What should you do?

Set the topic retention policy to 30 days.

Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

Set the topic retention policy to 30 days.

Set the subscriber retention policy to 30 days.

Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

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. What should you do?

Use Analytics Hub to control data access, and provide third party companies with access to the dataset

Create a 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.

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.

Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.

Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.

Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.

Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.

Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.

Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.

Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.

Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.

Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.

Google Professional Data Engineer Practice Test 8 - Free Online Exam Questions 281-320

You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

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Google Professional Data Engineer Practice Test 8

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Google Professional Data Engineer Practice Tests

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