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Professional Data Engineer: Professional Data Engineer on Google Cloud Platform
Page 7 out of 21 pages Questions 61-70 out of 205 questions
Question#61

You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
✑ You will batch-load the posts once per day and run them through the Cloud Natural Language API.
✑ You will extract topics and sentiment from the posts.
✑ You must store the raw posts for archiving and reprocessing.
✑ You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

  • A. Store the social media posts and the data extracted from the API in BigQuery.
  • B. Store the social media posts and the data extracted from the API in Cloud SQL.
  • C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
  • D. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
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C

Question#62

You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

  • A. Use Cloud Dataflow with Beam to detect errors and perform transformations.
  • B. Use Cloud Dataprep with recipes to detect errors and perform transformations.
  • C. Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.
  • D. Use federated tables in BigQuery with queries to detect errors and perform transformations.
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A

Question#63

Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

  • A. Execute gsutil rsync from the on-premises servers.
  • B. Use Dataflow and write the data to Cloud Storage.
  • C. Write a job template in Dataproc to perform the data transfer.
  • D. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
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A

Question#64

You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query `"-dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

  • A. Create a separate table for each ID.
  • B. Use the LIMIT keyword to reduce the number of rows returned.
  • C. Recreate the table with a partitioning column and clustering column.
  • D. Use the bq query --maximum_bytes_billed flag to restrict the number of bytes billed.
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C

Question#65

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

  • A. Use bq load to load a batch of sensor data every 60 seconds.
  • B. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
  • C. Use the INSERT statement to insert a batch of data every 60 seconds.
  • D. Use the MERGE statement to apply updates in batch every 60 seconds.
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C

Question#66

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

  • A. Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • B. Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • C. Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
  • D. Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
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A

Question#67

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

  • A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
  • B. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
  • C. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
  • D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
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C

Question#68

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table that have a recovery point objective (RPO) of 30 days?

  • A. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
  • B. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
  • C. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
  • D. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
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C

Question#69

You used Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

  • A. Create a cron schedule in Dataprep.
  • B. Create an App Engine cron job to schedule the execution of the Dataprep job.
  • C. Export the recipe as a Dataprep template, and create a job in Cloud Scheduler.
  • D. Export the Dataprep job as a Dataflow template, and incorporate it into a Composer job.
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C

Question#70

You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Dataproc and Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

  • A. cron
  • B. Cloud Composer
  • C. Cloud Scheduler
  • D. Workflow Templates on Dataproc
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D

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