easyMultiple Choice
MLA-C01 Practice Question: A company deployed a machine learning model on an…
A company deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased latency?
⚠ Common exam trap
Candidates often confuse a gradual latency increase with a model size or code issue, but the key clue is the unchanged model and instance type, pointing to a scaling configuration problem rather than a static resource limitation.
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 SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
The gradual increase in latency during peak hours, with no change to the model or instance type, strongly indicates that the endpoint is not scaling out fast enough to handle increased traffic. SageMaker real-time endpoints rely on auto scaling policies to add instances based on metrics like invocation count or CPU utilization; if the scale-out step is too slow or the cooldown period is too long, requests queue up and latency rises. This matches the symptom of latency growing over weeks as traffic patterns evolve, rather than a sudden spike.
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 inference script is not using batch processing.
Why it's wrong here
Lack of batching produces stable per-request overhead, not latency that worsens over weeks and peaks with traffic. Batching helps throughput-bound workloads with many concurrent requests. The gradual, load-correlated pattern points to accumulating requests queueing behind constrained concurrency rather than script inefficiency.
- ✓
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
Why this is correct
With the model and instance unchanged, rising peak-hour latency points to insufficient capacity: auto scaling policies or cooldowns react too slowly to traffic growth, so requests queue behind a saturated endpoint instead of additional instances absorbing load.
- ✗
The model size is too large for the instance type.
Why it's wrong here
A fixed model on an unchanged instance cannot gradually outgrow that instance; size mismatches cause consistently high latency from deployment, not a weeks-long drift concentrated at peak hours. Larger instances suit models whose memory or compute demands exceed current capacity from the outset.
- ✗
The endpoint has data capture enabled, causing additional overhead.
Why it's wrong here
Data capture adds a consistent per-invocation overhead from the moment it is enabled, not a gradual rise appearing only during peak hours. It suits auditing and debugging payloads. The described pattern indicates growing concurrent request volume exhausting endpoint capacity, not capture overhead.
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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.