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Google Certified Professional Data Engineer Exam Sample Questions (Q301-Q306):

NEW QUESTION # 301
You are monitoring your organization's data lake hosted on BigQuery. The ingestion pipelines read data from Pub/Sub and write the data into tables on BigQuery. After a new version of the ingestion pipelines is deployed, the daily stored data increased by 50%. The volumes of data in Pub/Sub remained the same and only some tables had their daily partition data size doubled. You need to investigate and fix the cause of the data increase. What should you do?

A. 1. Check for code errors in the deployed pipelines.
2. Check for multiple writing to pipeline BigQuery sink.
3. Check for errors in Cloud Logging during the day of the release of the new pipelines.
4. If no errors, restore the BigQuery tables to their content before the last release by using time travel.
B. 1. Roll back the last deployment.
2. Restore the BigQuery tables to their content before the last release by using time travel.
3. Restart the Dataflow jobs and replay the messages by seeking the subscription to the timestamp of the release.
C. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Schedule daily SQL jobs to deduplicate the affected tables.
3. Share the deduplication script with the other operational teams to reuse if this occurs to other tables.
D. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Check the BigQuery Audit logs to find job IDs.
3. Use Cloud Monitoring to determine when the identified Dataflow jobs started and the pipeline code version.
4. When more than one pipeline ingests data into a table, stop all versions except the latest one.

Answer: D

NEW QUESTION # 302
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?

A. Cloud Datastore
B. BigQuery
C. Cloud Bigtable
D. Cloud SQL

Answer: D

NEW QUESTION # 303
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 2 answers.

A. The subscriber code does not acknowledge the messages that it pulls.
B. The subscriber code cannot keep up with the messages.
C. Publisher throughput quota is too small.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.

Answer: B,D

NEW QUESTION # 304
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?

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

Answer: D

NEW QUESTION # 305
You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures.
What should you do?

A. Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter totrue.
B. Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enablenotification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.
C. Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable emailnotifications.
D. Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter totrue.

Answer: B

Explanation:
To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:
BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.
Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.
Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub/Sub topic upon failure, you can create a decoupled and scalable notification system.
Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.
Implementation Steps:
Set up a BigQuery Scheduled Query:
Create a scheduled query in BigQuery to run your SQL transformation every two hours.
Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.
Create a Pub/Sub Topic:
Create a Pub/Sub topic that will receive messages from the scheduled query.
Develop a Cloud Function:
Write a Cloud Function that subscribes to the Pub/Sub topic.
Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.
Reference Links:
BigQuery Scheduled Queries
Pub/Sub Documentation
Cloud Functions Documentation
SendGrid Email API
Gmail API

NEW QUESTION # 306
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