EXAM GOOGLE ASSOCIATE-DATA-PRACTITIONER REGISTRATION - ADVANCED ASSOCIATE-DATA-PRACTITIONER TESTING ENGINE

Exam Google Associate-Data-Practitioner Registration - Advanced Associate-Data-Practitioner Testing Engine

Exam Google Associate-Data-Practitioner Registration - Advanced Associate-Data-Practitioner Testing Engine

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Google Cloud Associate Data Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

  • A. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
  • B. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
  • C. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
  • D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: B

Explanation:
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery's built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.


NEW QUESTION # 65
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

  • A. Enable access control by using IAM roles.
  • B. Update dataset privileges by using the SQL GRANT statement.
  • C. Export the data to Cloud Storage, and use signed URLs to authorize access.
  • D. Encrypt the data by using customer-managed encryption keys (CMEK).

Answer: C

Explanation:
Using IAM roles to enable access control in BigQuery is the best approach to ensure that only authorized personnel can query the sensitive customer data. IAM allows you to define granular permissions at the project, dataset, or table level, ensuring that users have only the access they need in accordance with the principle of least privilege. For example, you can assign roles like roles/bigquery.dataViewer to allow read-only access or roles/bigquery.dataEditor for more advanced permissions. This approach provides centralized and manageable access control, which is critical for protecting sensitive data.


NEW QUESTION # 66
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. Seek back to the last acknowledged message.
  • C. Implement flow control on the subscribers
  • D. Retry messages until they are acknowledged.

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 # 67
You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

  • A. Store the data in a multi-region bucket.
  • B. Set up a Cloud CDN in front of the bucket.
  • C. Enable Object Versioning on the bucket.
  • D. Store the data in Nearline storaqe.

Answer: A

Explanation:
Storing the data in a multi-region bucket ensures high availability and durability, even in the event of a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.


NEW QUESTION # 68
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

  • A. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
  • B. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
  • C. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
  • D. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Answer: C

Explanation:
Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.


NEW QUESTION # 69
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