TRAINING ASSOCIATE-DATA-PRACTITIONER FOR EXAM | ASSOCIATE-DATA-PRACTITIONER INTERACTIVE QUESTIONS

Training Associate-Data-Practitioner For Exam | Associate-Data-Practitioner Interactive Questions

Training Associate-Data-Practitioner For Exam | Associate-Data-Practitioner Interactive Questions

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Associate-Data-Practitioner Interactive Questions & Real Associate-Data-Practitioner Questions

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Google Cloud Associate Data Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) copyright. You need to plan and execute this analysis using the most efficient approach. What should you do?

  • A. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use copyright to summarize the data.
  • B. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the copyright API to summarize the data.
  • C. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the copyright API to summarize the pre- processed data.
  • D. Query the BigQuery table from within a Python notebook, use the copyright API to summarize the data within the notebook, and store the summaries in BigQuery.

Answer: A

Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using copyright to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the copyright model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.


NEW QUESTION # 50
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

  • A. Use gcloud storage commands.
  • B. Use Cloud Storage FUSE.
  • C. Use a Transfer Appliance.
  • D. Use Storage Transfer Service.

Answer: D

Explanation:
Using the Storage Transfer Service is the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.


NEW QUESTION # 51
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

  • A. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
  • B. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.
  • C. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
  • D. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

Answer: D

Explanation:
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.


NEW QUESTION # 52
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

  • A. Forward unacknowledged messages to a dead-letter topic.
  • B. Retry messages until they are acknowledged.
  • C. Implement flow control on the subscribers
  • D. Seek back to the last acknowledged message.

Answer: C

Explanation:
Implementing flow control on the subscribers allows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.


NEW QUESTION # 53
Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

  • A. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.
  • B. Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.
  • C. Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.
  • D. Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.

Answer: B


NEW QUESTION # 54
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