This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy a single model endpoint using a GPU instance and enable autoscaling.
Option B is correct because a single-model endpoint on a GPU instance provides the low-latency, high-throughput inference required for real-time fraud detection. GPU instances accelerate linear learner inference by parallelizing matrix operations, enabling sub-100 ms predictions for individual transactions. Autoscaling ensures the endpoint can handle traffic spikes without degrading latency.
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 a multi-model endpoint with CPU instances.
Why it's wrong here
Multi-model endpoints may have model loading latency.
✓
Deploy a single model endpoint using a GPU instance and enable autoscaling.
Why this is correct
GPU instance can process individual transactions fast, autoscaling handles traffic.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a batch transform job scheduled every minute.
Why it's wrong here
Batch transform is not real-time.
✗
Deploy using SageMaker Serverless Inference.
Why it's wrong here
Serverless may have cold start latency and not guarantee 100 ms.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose multi-model endpoints (Option A) thinking they reduce cost, but they overlook the cold-start latency penalty for large models, which violates the strict 100 ms requirement.
Detailed technical explanation
How to think about this question
Under the hood, GPU instances like ml.p3.2xlarge use CUDA cores to perform parallel floating-point operations, which linear models exploit for fast dot-product calculations. Autoscaling with SageMaker uses target tracking policies based on invocation metrics (e.g., ApproximateBacklogPerInstance) to pre-provision capacity, avoiding latency spikes during traffic bursts. In real-world fraud detection, a 100 ms SLA is critical because delays can cause transaction timeouts or user abandonment.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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: Deploy a single model endpoint using a GPU instance and enable autoscaling. — Option B is correct because a single-model endpoint on a GPU instance provides the low-latency, high-throughput inference required for real-time fraud detection. GPU instances accelerate linear learner inference by parallelizing matrix operations, enabling sub-100 ms predictions for individual transactions. Autoscaling ensures the endpoint can handle traffic spikes without degrading latency.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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