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AI Implementation and OperationsmediumMultiple ChoiceObjective-mapped

AI0-001 AI Implementation and Operations Practice Question

Exhibit

Refer to the exhibit.

```
Error Log:
[2025-03-15 10:23:45] ERROR: Model server 'prod-ml-01' failed health check.
[2025-03-15 10:23:46] WARNING: Inference latency exceeded threshold: 500ms (threshold 200ms).
[2025-03-15 10:23:47] INFO: Rolling restart initiated for 'prod-ml-01'.
```

An operations team sees the log entries above for a production ML model. What is the MOST likely root cause of the latency spike?

⚠ Common exam trap

CompTIA often tests the distinction between gradual vs. sudden performance degradation patterns, where candidates mistakenly attribute a gradual latency increase to a transient resource contention event like a training job or network 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

A memory leak in the model serving container causing gradual slowdown.

The log entries show a gradual increase in latency over time, which is characteristic of a memory leak in the model serving container. As memory consumption grows, garbage collection pauses become more frequent and longer, eventually causing request processing to slow down. This pattern is distinct from a sudden spike caused by resource contention or network issues.

Answer analysis

Option-by-option breakdown

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

  • A scheduled training job consuming GPU resources on the same node.

    Why it's wrong here

    Training jobs are typically isolated from serving nodes.

  • A memory leak in the model serving container causing gradual slowdown.

    Why this is correct

    Memory leak can cause garbage collection overhead and increased latency.

  • A network outage between the model server and the client.

    Why it's wrong here

    Network outage would cause connection errors, not latency increase.

  • A bug in the model's preprocessing code causing incorrect predictions.

    Why it's wrong here

    Incorrect predictions do not directly cause latency spikes.

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Written by Johnson Ajibi, MSc IT Security

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