MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company is deploying a foundation model using SageMaker JumpStart. They want to minimize inference costs while maintaining low latency. Which TWO strategies should they consider? (Select TWO)
⚠ Common exam trap
The AWS exam often tests the misconception that cost minimization is achieved solely through discount plans (Savings Plans) or instance size, rather than through dynamic scaling and right-sizing based on actual workload patterns.
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
✓
Enable auto-scaling with a target tracking policy based on Invocations per instance
Auto-scaling with a target tracking policy based on Invocations per instance dynamically adjusts the number of instances to match demand, ensuring you only pay for the compute capacity you need while maintaining low latency. This avoids over-provisioning and reduces idle costs, directly addressing the goal of minimizing inference costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable data capture for all requests to analyze usage patterns
Why it's wrong here
Data capture incurs additional costs and does not directly minimize inference costs.
- ✗
Use SageMaker Savings Plans for discounted compute rates
Why it's wrong here
Savings Plans require a 1-3 year commitment and are not a direct strategy for JumpStart deployments.
- ✗
Deploy the model on a single large instance to maximize throughput
Why it's wrong here
A single large instance may be cost-inefficient and create a bottleneck.
- ✓
Enable auto-scaling with a target tracking policy based on Invocations per instance
Why this is correct
Auto-scaling adjusts capacity to match demand, avoiding over-provisioning.
- ✓
Use SageMaker Inference Recommender to select the most cost-effective instance type
Why this is correct
Inference Recommender provides instance recommendations based on the model and workload.
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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.