Databricks Data Engineer Professional Question 220
Select 3You are tasked with monitoring a production Databricks job that processes terabytes of data daily. The job occasionally fails due to data inconsistencies, and your team needs to identify the root cause quickly when such failures occur. Which of the following actions should you take to ensure effective monitoring and logging for this job?
- A
Enable structured logging to capture details such as input data schema, job parameters, and error stack traces.
- B
Set up cluster metrics monitoring to track resource utilization, such as CPU and memory, for the job cluster.
- C
Use the 'print' statement in your code to log messages directly to the standard output of the notebook.
- D
Configure alerts in Databricks to notify your team when the job fails or exceeds a specific runtime.
- E
Disable Spark event logging to reduce storage costs for log files.
Show answer and explanation
Correct answers: A, B, D
Explanation
To effectively monitor and debug a production Databricks job, you need structured and actionable logging, cluster resource monitoring, and proactive alerting for potential issues. These steps ensure that your team can quickly identify and resolve failures without losing critical context or time. Avoid practices like using 'print' for logging or disabling essential logs, as they hinder effective monitoring and root cause analysis.
- A. Correct.
Structured logging is essential for capturing detailed information about job execution, including errors, which helps in identifying root causes quickly.
- B. Correct.
Monitoring cluster metrics provides insights into resource utilization, which can help identify performance bottlenecks or resource-related failures.
- C. Incorrect.
Using 'print' statements for logging is not a best practice as it lacks structure, scalability, and integration with monitoring tools.
- D. Correct.
Configuring alerts ensures that the team is promptly notified of job failures or performance issues, enabling faster response times.
- E. Incorrect.
Disabling Spark event logging is counterproductive as it removes critical information required for debugging and performance analysis.