Google Professional Data Engineer Question 163
Select 2Google Cloud PlatformYour organization is using Google Cloud Storage as a data lake to store raw and processed data. Recently, there have been concerns about data accessibility and performance issues due to frequent unoptimized queries. As a Data Engineer, how can you effectively monitor and troubleshoot your data lake's performance and reliability?
- A
Enable Cloud Storage usage logs and analyze them for access patterns and performance metrics.
- B
Use Cloud Monitoring to set up custom dashboards and alerts for bucket-level metrics like latency and throughput.
- C
Regularly run the Cloud Dataflow Job Visualizer to analyze the data pipeline transformations for bottlenecks.
- D
Inspect IAM policy logs to identify unauthorized access attempts to sensitive datasets.
- E
Leverage BigQuery's built-in query history to analyze query performance and optimize storage formats.
Show answer and explanation
Correct answers: A, B
Explanation
Monitoring a data lake requires tools that provide insights into storage access patterns, latency, and throughput. Cloud Storage usage logs and Cloud Monitoring effectively address these needs by enabling detailed tracking and alerting for performance and reliability metrics. Other options like Dataflow Job Visualizer and BigQuery query history are valuable in their contexts but are not directly applicable to data lake monitoring.
- A. Correct.
Correct: Cloud Storage usage logs provide insights into access patterns, data retrievals, and performance metrics, which are essential for monitoring the data lake.
- B. Correct.
Correct: Cloud Monitoring is a key tool for tracking bucket-level metrics such as throughput, latency, and error rates, allowing you to proactively address performance issues.
- C. Incorrect.
Incorrect: While Cloud Dataflow Job Visualizer is helpful for monitoring data pipelines, it is not directly related to monitoring the performance of a data lake stored in Cloud Storage.
- D. Incorrect.
Incorrect: IAM policy logs are valuable for security monitoring but do not provide performance and reliability metrics for the data lake itself.
- E. Incorrect.
Incorrect: BigQuery's query history analysis is specific to query optimization within BigQuery and does not address monitoring of the data lake in Cloud Storage.