MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company uses SageMaker JumpStart to deploy a foundation model for a summarization task. They want to minimize costs while still meeting a latency requirement of under 2 seconds. Which option should they consider?
⚠ Common exam trap
A common misconception is that serverless endpoints are always the cheapest option, but for latency-sensitive workloads with large models, the cold-start overhead and lack of guaranteed compute resources make them unsuitable. Inference Recommender is the correct tool for cost-latency trade-off analysis.
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
✓
Use SageMaker Inference Recommender to select the cheapest instance that meets latency
SageMaker Inference Recommender runs load tests against your model on various instance types and provides latency and cost metrics. By selecting the cheapest instance that still meets the sub-2-second latency requirement, you directly minimize cost while satisfying the performance constraint. This is the most systematic and cost-effective approach for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use SageMaker Inference Recommender to select the cheapest instance that meets latency
Why this is correct
SageMaker Inference Recommender runs automatic load tests across instance types and returns the cheapest instance satisfying the sub-2-second latency constraint, directly optimising the cost-versus-latency trade-off. Manual instance selection risks over-provisioning or breaching latency, so this satisfies both the cost-minimisation and latency requirements in the stem.
- ✗
Deploy the model on a serverless endpoint
Why it's wrong here
Serverless endpoints cold-start and queue requests, so sub-two-second latency is not guaranteed for a foundation model. It is tempting because serverless bills per invocation and scales to zero, making it the right choice for intermittent, latency-tolerant summarisation where cost minimisation outweighs response-time guarantees.
- ✗
Enable auto-scaling to handle variable traffic
Why it's wrong here
Auto-scaling adjusts instance count to traffic volume; it does not reduce the per-request latency of a single inference, so the two-second requirement is unaddressed. It is tempting because auto-scaling genuinely cuts cost under variable load, and would be correct where traffic fluctuates and the latency target is already met by the instance type.
- ✗
Use the largest GPU instance to ensure fast inference
Why it's wrong here
A larger GPU instance raises hourly cost without addressing the latency target, since summarisation latency depends on model size, token count and endpoint configuration rather than raw GPU capacity alone. It is tempting because oversized instances do reduce per-token inference time, and would suit throughput-heavy batch workloads where latency dominates and cost is secondary.
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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.