Databricks Data Engineer Professional Question 218
Select 3A data engineering team is running a Databricks job that processes large volumes of data daily. They notice that the job is intermittently failing without clear error messages, making it challenging to diagnose the issue. As a data engineer, which of the following actions will help you effectively monitor and gather logs to debug the problem and ensure reliability?
- A
Enable cluster-level log delivery to a centralized storage location such as an Azure Blob or AWS S3 bucket.
- B
Use the Databricks REST API to periodically fetch job run details and error messages.
- C
Configure Structured Streaming checkpoints to store intermediate job states and recover from failures.
- D
Enable and monitor the Spark UI to inspect task-level metrics and execution plans for failed stages.
- E
Increase the worker node instance size to improve processing capacity and reduce failures.
Show answer and explanation
Correct answers: A, B, D
Explanation
To debug intermittent job failures effectively, it is essential to implement robust monitoring and logging practices. Cluster-level log delivery ensures logs are preserved for analysis, while the Databricks REST API allows for automated tracking of job details. Additionally, the Spark UI provides critical insights into execution details, making these options the most relevant for diagnosing and resolving failures. Other options, such as Structured Streaming checkpoints or increasing worker instance size, are not applicable to the core problem of debugging batch job failures.
- A. Correct.
Enabling cluster-level log delivery allows you to store and access logs from all nodes in a centralized location, making it easier to investigate failures and monitor the job's performance.
- B. Correct.
Using the Databricks REST API to fetch job run details and error messages provides a programmatic way to monitor jobs and capture meaningful insights into failures.
- C. Incorrect.
While Structured Streaming checkpoints are useful for state recovery in streaming jobs, they do not directly provide logs or insights for debugging batch jobs.
- D. Correct.
Monitoring the Spark UI helps identify task-level issues, such as skewed data or failed tasks, which are crucial for debugging intermittent job failures.
- E. Incorrect.
Increasing worker node instance size may help with performance but does not directly address the need for monitoring and logging to diagnose failures.