MLS-C01 Exploratory Data Analysis Practice Question
Exhibit
Refer to the exhibit. [ERROR] 2023-01-15T10:30:00.000Z 12345678-1234-1234-1234-123456789012 Task timed out after 300.00 seconds [ERROR] 2023-01-15T10:35:00.000Z 12345678-1234-1234-1234-123456789012 Task timed out after 300.00 seconds
Refer to the exhibit. A data scientist is using AWS Glue ETL jobs to process data from a source database. The job logs show repeated timeout errors. Which EDA step should the scientist perform to diagnose the issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the source database table sizes and row counts over time.
The timeout errors indicate that the Glue ETL job is exceeding its configured timeout. To diagnose the root cause, the data scientist should check the source database table sizes and row counts over time (option B). This helps determine if the data volume has increased, causing longer processing times. Option A (testing network connectivity) addresses network issues but not processing delays. Option C (switching job type) may change performance but does not diagnose the cause. Option D (increasing timeout) is a temporary workaround, not a diagnostic step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Test network connectivity from the Glue job to the source database using telnet.
Why it's wrong here
Connectivity issues would cause different errors.
- ✓
Check the source database table sizes and row counts over time.
Why this is correct
Identifies if data volume growth causes timeouts.
- ✗
Switch the Glue ETL job type from Spark to Python shell to reduce overhead.
Why it's wrong here
Python shell has even smaller timeout limits.
- ✗
Increase the Glue job timeout to 600 seconds and rerun.
Why it's wrong here
Workaround, not diagnostic.
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