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 building a real-time fraud detection system using Amazon SageMaker. The model must have low latency (under 10ms) and high throughput (thousands of predictions per second). The team has trained a gradient boosting model using XGBoost. Which SageMaker inference option 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
✓
Deploy the model on a SageMaker real-time endpoint with a multi-model endpoint.
A multi-model endpoint (MME) on SageMaker is the most suitable option because it allows you to host multiple XGBoost models on a single endpoint, sharing the underlying instance to maximize throughput and minimize latency. MMEs keep models loaded in memory and route requests to the correct model with sub-10ms overhead, meeting the low-latency and high-throughput requirements for real-time fraud detection.
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 asynchronous inference.
Why it's wrong here
Asynchronous inference is for near-real-time, not sub-10ms latency.
✓
Deploy the model on a SageMaker real-time endpoint with a multi-model endpoint.
Why this is correct
Multi-model endpoints optimize cost and latency for high throughput.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy the model on a SageMaker serverless endpoint.
Why it's wrong here
Serverless endpoints may have cold start latency and throughput limitations.
✗
Use a batch transform job.
Why it's wrong here
Batch transform is for offline predictions, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'real-time' with 'serverless' or 'asynchronous', failing to recognize that serverless endpoints introduce cold-start latency and throughput limits that break the sub-10ms and high-throughput requirements.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MMEs use a shared container that loads model artifacts from Amazon S3 into memory on demand, caching them for subsequent requests. This design reduces per-model overhead compared to separate endpoints, enabling high throughput by reusing the same inference container across multiple models. In a real-world fraud detection system, this allows the team to deploy multiple model versions or tenant-specific models without provisioning separate instances, optimizing cost while maintaining sub-10ms 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 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 the model on a SageMaker real-time endpoint with a multi-model endpoint. — A multi-model endpoint (MME) on SageMaker is the most suitable option because it allows you to host multiple XGBoost models on a single endpoint, sharing the underlying instance to maximize throughput and minimize latency. MMEs keep models loaded in memory and route requests to the correct model with sub-10ms overhead, meeting the low-latency and high-throughput requirements for real-time fraud detection.
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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