- A
SageMaker Batch Transform
Why wrong: Batch Transform is for asynchronous, batch processing, not real-time.
- B
SageMaker endpoints
Endpoints are designed for real-time inference with automatic scaling and low latency.
- C
SageMaker Hyperparameter Tuning
Why wrong: Hyperparameter Tuning is for training, not serving predictions.
- D
AWS Lambda with model packaged
Why wrong: Lambda can be used but has constraints like package size and cold starts, not optimized for ML inference.
Question 679 of 1,000
mediumMultiple ChoiceObjective-mapped
Choosing SageMaker Endpoints for Low-Latency Predictions
This MLA-C01 practice question tests your understanding of a trained model needs to be deployed for…. 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 trained model needs to be deployed for real-time inference with low latency. Which AWS service is best suited for this?
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
✓
SageMaker endpoints
SageMaker endpoints are designed for real-time inference by provisioning persistent, auto-scaled HTTPS endpoints that return predictions with millisecond latency. They support automatic scaling, A/B testing, and can be deployed behind a VPC for low-latency access, making them the ideal choice for serving a trained model in production.
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.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform is for asynchronous, batch processing, not real-time.
- ✓
SageMaker endpoints
Why this is correct
Endpoints are designed for real-time inference with automatic scaling and low latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Hyperparameter Tuning
Why it's wrong here
Hyperparameter Tuning is for training, not serving predictions.
- ✗
AWS Lambda with model packaged
Why it's wrong here
Lambda can be used but has constraints like package size and cold starts, not optimized for ML inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between batch and real-time inference, and the trap here is that candidates confuse SageMaker Batch Transform (which processes data in bulk) with a real-time serving solution, or they overestimate Lambda's ability to handle large model payloads and sustained low-latency requests.
Detailed technical explanation
How to think about this question
SageMaker endpoints use a load-balanced fleet of ML instances behind an auto-scaling group, with built-in health checks and automatic rollback on deployment failures. Under the hood, the endpoint invokes a Docker container that runs your inference code, and you can enable data capture for monitoring and model retraining. A real-world scenario is a fraud detection system that must score each transaction in under 100 ms; SageMaker endpoints can achieve this by using GPU instances and optimized TensorFlow Serving or TorchServe containers.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: SageMaker endpoints — SageMaker endpoints are designed for real-time inference by provisioning persistent, auto-scaled HTTPS endpoints that return predictions with millisecond latency. They support automatic scaling, A/B testing, and can be deployed behind a VPC for low-latency access, making them the ideal choice for serving a trained model in production.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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