Professional-Data-Engineer試験無料問題集「Google Certified Professional Data Engineer 認定」

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

B. Create a transactional database that monitors the pending messages.

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

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

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

B. Create a Spark job and submit it to Dataproc Serverless.

C. 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.

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

A. Increase the acknowledgement deadline to 10 minutes.

B. Use exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.

C. Use immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

D. Use exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

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

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

C. Migrate the workloads to Dataproc plus HOPS, modernize later

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

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

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

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

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

A. Both batch and streaming

B. Only batch

C. Only streaming

D. BigQuery cannot be used as a sink

A. VPN

B. SOCKS

C. HTTPS

D. HTTP

A. Use a row key of the form <timestamp>.

B. Use a row key of the form >#<sensorid>#<timestamp>.

C. Use a row key of the form <sensorid>.

D. Use a row key of the form <timestamp>#<sensorid>.

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

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

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

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

A. 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

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

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

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

A. 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

B. 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.

C. 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.

D. 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.

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

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

C. 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.

D. 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.

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

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

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

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