mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker to host a…
A company uses Amazon SageMaker to host a real-time inference endpoint for a fraud detection model. The endpoint is deployed with three instances of ml.m5.large. The model processes each request in about 200 ms. Lately, users report occasional timeouts (requests taking >5 seconds). The team suspects model drift or data skew. What is the MOST likely cause and solution?
⚠ Common exam trap
Candidates often confuse model drift (accuracy degradation) with performance degradation (latency increase), leading them to choose retraining (Option D) instead of investigating input preprocessing changes.
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 recent change increased the average input size, causing longer inference time; investigate input preprocessing.
The symptom of occasional timeouts (>5 seconds) on a model that normally processes requests in ~200 ms suggests that a recent change in input data characteristics (e.g., larger payloads or more complex features) is causing sporadic latency spikes. Investigating input preprocessing can identify if data skew or increased input size is overwhelming the model's inference path, which is a common monitoring concern in SageMaker real-time endpoints.
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 instances are under-provisioned; switch to ml.m5.xlarge instances.
Why it's wrong here
Upscaling instances may help throughput but not necessarily per-request latency if the issue is input size.
- ✓
A recent change increased the average input size, causing longer inference time; investigate input preprocessing.
Why this is correct
Larger inputs can increase inference latency significantly.
- ✗
The endpoint is experiencing too many concurrent requests; add more instances.
Why it's wrong here
Concurrent requests cause queuing, but the symptom is per-request timeout, not consistently high latency.
- ✗
Model drift caused the model to become computationally heavier; retrain the model.
Why it's wrong here
Model drift affects prediction accuracy, not computation time.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 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 MLA-C01 exam.