DOP-C02 Monitoring and Logging Practice Question
Exhibit
CloudWatch Logs Insights query: fields @timestamp, @message | filter @logStream = 'prod-app-server-1' | filter @message like /ERROR/ | stats count() by bin(5m) | sort @timestamp desc | limit 20 Sample output: | @timestamp (5m bin) | count() | | 2025-03-15 14:55:00 | 12 | | 2025-03-15 14:50:00 | 8 | | 2025-03-15 14:45:00 | 3 | | 2025-03-15 14:40:00 | 1 |
Refer to the exhibit. A DevOps engineer runs this query to investigate a spike in errors. What is the most likely interpretation?
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
✓
The error rate is increasing sharply in the last 15 minutes.
The query counts ERROR messages per 5-minute bin for a specific log stream. The output shows a clear increasing trend from 1 to 12 errors over the last 20 minutes, indicating a recent escalation of errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The error rate is increasing sharply in the last 15 minutes.
Why this is correct
The query results show a sharp upward spike in the error count in the most recent time bins, jumping from 1 to 12, which is a clear and significant acceleration in the error rate over the last 15 minutes. This trend is not random fluctuation; the consistent climb in the final bins indicates an emerging incident that requires immediate attention. The slope of the line in the visualization confirms the error rate is increasing, not just an isolated outlier.
- ✗
The error rate is decreasing over time.
Why it's wrong here
This is incorrect because the data exhibits the exact opposite pattern: the error count in the latest 15-minute buckets is substantially higher than earlier buckets, with the most recent bin reaching 12. A decreasing error rate would show a consistent downward slope or declining counts over time, which is absent from the results. If the rate were falling, the last few bins would show lower numbers than the earlier ones, but they show a dramatic rise.
- ✗
The error rate is stable with no significant change.
Why it's wrong here
This is incorrect because the error count is not oscillating around a consistent mean; instead, it remains low (around 1) for most of the window and then explodes to 12 in the final bin. A stable rate would exhibit minor random variation within a narrow band, but this 12-fold increase in the last 15 minutes is a statistically significant deviation that breaks any pattern of stability. The sharp jump is a clear anomaly, not normal steady-state behavior.
- ✗
The query is incorrectly filtering log streams.
Why it's wrong here
This is incorrect because the query's filter logic is correctly scoped to the intended log group and error pattern, as evidenced by the valid error events returned in the results. If the filter were misconfigured (e.g., wrong log stream, wrong time range, or a malformed pattern), the query would either return no data, unrelated logs, or an error message—none of which match the observed output. The consistent upward trend in the returned error counts confirms the filter is capturing the right logs and simply reflecting the real increase in errors.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DOP-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DOP-C02 exam.