Databricks Data Engineer Professional Question 221
Select 3You are tasked with monitoring a production Databricks job that processes a large volume of streaming data. The job occasionally fails due to unexpected data anomalies, and your goal is to ensure failures are promptly detected and logged for debugging. Which of the following steps should you take to effectively monitor and log the job in Databricks?
- A
Use Databricks' built-in job run status and configure email notifications for failure events.
- B
Enable Structured Streaming metrics in your job and use a custom dashboard to visualize streaming performance.
- C
Write a custom script to parse cluster logs and schedule it to run periodically for anomaly detection.
- D
Integrate your Databricks workspace with an external monitoring tool such as Azure Monitor or AWS CloudWatch for real-time alerts.
- E
Rely solely on Databricks' automatic logging without additional configurations or integrations.
Show answer and explanation
Correct answers: A, B, D
Explanation
Effective monitoring and logging in Databricks require a combination of built-in features, such as job run status and Structured Streaming metrics, along with external monitoring tools for advanced alerting and observability. This ensures timely detection of failures and comprehensive logging for debugging.
- A. Correct.
Configuring email notifications for job failures ensures that you are immediately aware of any issues, making this an essential step for job monitoring.
- B. Correct.
Enabling Structured Streaming metrics allows you to track key performance indicators such as input rates and processing times, which are critical for diagnosing potential bottlenecks or anomalies.
- C. Incorrect.
While a custom script might help, it is not an efficient or scalable solution compared to built-in or external monitoring tools.
- D. Correct.
Integrating with external monitoring tools like Azure Monitor or AWS CloudWatch provides real-time alerts and advanced monitoring capabilities, which are highly effective for production workloads.
- E. Incorrect.
Relying solely on automatic logging without additional configurations or integrations is insufficient for proactive monitoring and does not provide real-time alerts.