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Exploratory Data AnalysismediumMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

Exhibit

Refer to the exhibit.

CloudWatch Logs Insights query:
fields @timestamp, @message
| filter @message like /ERROR/
| stats count() by bin(1h)
| sort @timestamp desc
| limit 10

A DevOps engineer runs the CloudWatch Logs Insights query shown above on the log group for an ML training job. The result shows a spike in ERROR messages at a specific hour. What should the engineer do next to identify the root cause?

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

Modify the query to display the actual @message for the hour with the spike.

Modifying the query to include the @message field for the specific hour with the spike allows the engineer to see the actual error messages, which is the direct way to identify the root cause. Option B is incorrect because removing the filter would show all messages, which is less targeted. Option C is incorrect because changing the bin to 5m might be too granular and not helpful without examining the messages. Option D is incorrect because increasing the limit does not help identify the cause without seeing the messages.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Modify the query to display the actual @message for the hour with the spike.

    Why this is correct

    Directly see error details.

  • Remove the filter on ERROR to see all messages.

    Why it's wrong here

    Would include non-error messages.

  • Change the bin to 5m to see more detailed spikes.

    Why it's wrong here

    Not needed; spike is clear.

  • Increase the limit to 50 to see more hours.

    Why it's wrong here

    Spike is already visible.

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