This MLS-C01 practice question tests your understanding of modeling. 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 is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The model is an ensemble of 5 gradient boosted trees (XGBoost), each 200 MB. Which deployment strategy is MOST suitable?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker multi-model endpoint on a compute-optimized instance.
Option D is correct because using a single multi-model endpoint with multiple models behind a load balancer provides scalability and low latency. Option A is wrong because a single model may not handle throughput. Option B is wrong because Lambda has execution time limits and cold starts. Option C is wrong because CPU is slower than GPU for this use case.
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 AWS Lambda to invoke each model sequentially.
Why it's wrong here
Lambda has latency and timeout limitations.
✗
Deploy each model as a separate SageMaker endpoint and use a load balancer.
Why it's wrong here
Multiple endpoints increase cost and complexity.
✗
Deploy the ensemble on a single GPU instance with large batch processing.
Why it's wrong here
GPU not needed for tree-based models; batching adds latency.
✓
Use SageMaker multi-model endpoint on a compute-optimized instance.
Why this is correct
Multi-model endpoints reduce overhead and scale well.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker multi-model endpoint on a compute-optimized instance. — Option D is correct because using a single multi-model endpoint with multiple models behind a load balancer provides scalability and low latency. Option A is wrong because a single model may not handle throughput. Option B is wrong because Lambda has execution time limits and cold starts. Option C is wrong because CPU is slower than GPU for this use case.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-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 MLS-C01 exam.
Question Discussion
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