- A
Use SageMaker Inference Recommender to select the cheapest instance that meets latency
Inference Recommender benchmarks the model on different instances to find the optimal balance of cost and latency.
- B
Deploy the model on a serverless endpoint
Why wrong: Serverless endpoints may have cold starts and limited GPU support, potentially exceeding latency requirements.
- C
Enable auto-scaling to handle variable traffic
Why wrong: Auto-scaling manages traffic but does not directly optimize instance type cost.
- D
Use the largest GPU instance to ensure fast inference
Why wrong: Largest instance may exceed latency requirements but is not cost-effective.
Question 695 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Inference Recommender benchmarks the model on different instances to find the optimal balance of cost and latency.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy the model on a serverless endpoint
Why it's wrong here
Serverless endpoints may have cold starts and limited GPU support, potentially exceeding latency requirements.
- ✗
Enable auto-scaling to handle variable traffic
Why it's wrong here
Auto-scaling manages traffic but does not directly optimize instance type cost.
- ✗
Use the largest GPU instance to ensure fast inference
Why it's wrong here
Largest instance may exceed latency requirements but is not cost-effective.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception 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, and Inference Recommender is the correct tool for cost-latency trade-off analysis.
Detailed technical explanation
How to think about this question
SageMaker Inference Recommender uses a combination of synchronous and asynchronous load testing with configurable concurrency and payload sizes to simulate real-world traffic. It returns a recommendation report that includes p50, p90, and p99 latency metrics, along with cost-per-inference estimates. For foundation models, memory footprint and batch size are critical; Inference Recommender can also suggest optimal batch sizes and instance types like ml.g5.xlarge or ml.inf1.xlarge that balance cost and latency.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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