PDE practice questions 231–240

1. Question 231 · 340 in bank

   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?

   • ASet 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.
   • BRenew the TLS certificate of the HTTPS endpoint. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
   • CCreate 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.
   • DUpdate 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.

   Show answer

   Answer: C

   Explanation

   - It addresses the likely issue: that the signed URLs have expired or are otherwise invalid. By creating a new TSV file with freshly generated signed URLs (with a longer validity period), you're ensuring that the Storage Transfer Service has valid authorization to access the files. - Splitting the TSV file and running parallel jobs might help in managing the workload more efficiently and overcoming any limitations related to the number of files or transfer speed.

   Source: Adapted from community discussion — raaad

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2. Question 232 · 340 in bank

   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?

   • ACreate 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.
   • BCreate a BigQuery table partitioned by datetime value of the weather date. Set up partition expiration to 30 days.
   • CCreate a BigQuery table partitioned by ingestion time. Set up partition expiration to 30 days.
   • DCreate 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.

   Show answer

   Answer: B

   Explanation

   https://cloud.google.com/bigquery/docs/partitioned-tables Here it mentions “ For TIMESTAMP and DATETIME columns, the partitions can have either hourly, daily, monthly, or yearly granularity.l So you should not calculate the amount of partitions on second granularity

   Source: Adapted from community discussion — d11379b

   References

   • Google Cloud documentation

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3. Question 233 · 340 in bank

   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?

   • AUpdate the current pipeline and use the drain flag.
   • BUpdate the current pipeline and provide the transform mapping JSON object.
   • CCreate a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.
   • DCreate a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

   Show answer

   Answer: A

   Explanation

   Correct Option : A Explanation:-This option is correct as the key requirement is not to lose the data, the Dataflow pipeline can be stopped using the Drain option. Drain options would cause Dataflow to stop any new processing, but would also allow the existing processing to complete

   Source: Adapted from community discussion — VishalB

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 234 · 340 in bank

   You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

   • ARun a scheduled query to pull the necessary data at specific intervals dally.
   • BUse a cached query to accelerate time to results.
   • CLimit the query columns being pulled in the final result.
   • DCreate a materialized view based off of the query being run.

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   Answer: D

   Explanation

   Create a materialized view as query source. Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency.

   Source: Adapted from community discussion — AllenChen123

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5. Question 235 · 340 in bank

   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?

   • AUse a Deduplicate PTransform in Dataflow before sending the messages to the sales agents.
   • BCreate a transactional database that monitors the pending messages.
   • CUse Pub/Sub exactly-once delivery in your pull subscription.
   • DCreate a new Pub/Sub push subscription to monitor the orders processed in the agent's system.

   Show answer

   Answer: C

   Explanation

   Believe me all questions were from Exam topic all were there yesterday in exam. But yes dont go with starting questions mainly focus questions after 200 and latest questions are at last page.

   Source: Adapted from community discussion — meh_33

   References

   • Google Cloud documentation

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6. Question 236 · 340 in bank

   You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization’s data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?

   • AUse Analytics Hub to facilitate data sharing.
   • BCreate authorized datasets to publish shared data in the subscribing team's project.
   • CCreate a new dataset for sharing in each individual team’s project. Grant the subscribing team the bigquery.dataViewer role on the dataset.
   • DUse BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

   Show answer

   Answer: A

   Explanation

   Analytics Hub is a fully managed data sharing platform provided by Google Cloud. It allows organizations to publish, discover, and subscribe to datasets securely and efficiently. It facilitates collaboration across teams or even across organizations by enabling self-service access to shared data without duplicating or moving it.

   Source: Adapted from community discussion — FireAtMe

   References

   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 237 · 340 in bank

   You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?

   • AExecute the job as part of a deployment in a new Google Kubernetes Engine cluster.
   • BExecute the job from a new Compute Engine VM.
   • CExecute the job in a new Dataproc cluster.
   • DExecute as a Dataproc Serverless job.

   Show answer

   Answer: D

   Explanation

   Priority is "minimize installation and management effort" which is done via Dataproc Serverless. Furthermore, with Dataproc serverless you can still specify resource settings for your job, such as the number of vCPUs and memory per executor (https://cloud.google.com/dataproc-serverless/docs/concepts/properties)

   Source: Adapted from community discussion — chicity_de

   References

   • Google Cloud documentation

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8. Question 238 · 340 in bank

   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?

   • ADisable external IP addresses from worker VMs and enable Private Google Access.
   • BLeave external IP addresses assigned to worker VMs while enforcing firewall rules.
   • CDisable external IP addresses and establish a Private Service Connect endpoint IP address.
   • DEnable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

   Show answer

   Answer: A

   Explanation

   A. Disable external IP addresses from worker VMs and enable Private Google Access. This approach ensures that your worker VMs can access Google APIs and services securely without using external IP addresses, which reduces costs and enhances security by keeping the traffic within Google's network

   Source: Adapted from community discussion — Pime13

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9. Question 239 · 340 in bank

   You are using Workflows to call an API that returns a 1KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?

   • ACreate a Cloud Composer environment and run the logic in Cloud Composer.
   • BCreate a Dataproc cluster, and use PySpark to apply the logic on your JSON file.
   • CInvoke a Cloud Function instance that uses Python to apply the logic on your JSON file.
   • DInvoke a subworkflow in Workflows to apply the logic on your JSON file.

   Show answer

   Answer: C

   Explanation

   C. Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file. Using a Cloud Function allows you to run your Python code in a serverless environment, which simplifies deployment and management. It also ensures quick execution and scalability, as Cloud Functions can handle the processing of your JSON response efficiently

   Source: Adapted from community discussion — Pime13

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10. Question 240 · 340 in bank

    You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

    • ABuild authorized views on top of the sales table to aggregate data at the day and month level.
    • BEnable BI Engine and add your sales table as a preferred table.
    • CBuild materialized views on top of the sales table to aggregate data at the day and month level.
    • DCreate a scheduled query to build sales day and sales month aggregate tables on an hourly basis.

    Show answer

    Answer: C

    Explanation

    C. Build materialized views on top of the sales table to aggregate data at the day and month level. Materialized views are precomputed views that cache the results of a query, which can significantly improve query performance and reduce costs by avoiding repeated computation. They automatically update with changes to the base table, ensuring data freshness without additional maintenance. https://cloud.google.com/bigquery/docs/materialized-views-intro

    Source: Adapted from community discussion — Pime13

    References

    • Google Cloud documentation

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PDE practice, question 231. 340 questions available.