- A
Enable data capture for all requests to analyze usage patterns
Why wrong: Data capture incurs additional costs and does not directly minimize inference costs.
- B
Use SageMaker Savings Plans for discounted compute rates
Why wrong: Savings Plans require a 1-3 year commitment and are not a direct strategy for JumpStart deployments.
- C
Deploy the model on a single large instance to maximize throughput
Why wrong: A single large instance may be cost-inefficient and create a bottleneck.
- D
Enable auto-scaling with a target tracking policy based on Invocations per instance
Auto-scaling adjusts capacity to match demand, avoiding over-provisioning.
- E
Use SageMaker Inference Recommender to select the most cost-effective instance type
Inference Recommender provides instance recommendations based on the model and workload.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 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)
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
Enable auto-scaling with a target tracking policy based on Invocations per instance
Option D is correct because 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.
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.
- ✗
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.
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.
- ✓
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.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
SageMaker Inference Recommender runs load tests against your model and endpoint configuration to recommend the optimal instance type and number of instances, balancing cost and latency. Auto-scaling with a target tracking policy uses CloudWatch metrics (e.g., InvocationsPerInstance) to maintain a target utilization, scaling in/out proactively to handle traffic bursts without manual intervention. In practice, combining Inference Recommender with auto-scaling can reduce inference costs by up to 50% compared to static deployments.
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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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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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: Enable auto-scaling with a target tracking policy based on Invocations per instance — Option D is correct because 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.
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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