Quick Answer
The correct answer is Amazon SageMaker Serverless Inference and AWS Lambda, as both can deploy trained models for serverless inference. SageMaker Serverless Inference is purpose-built for ML models, automatically scaling compute resources based on request traffic and eliminating the need to manage underlying infrastructure, while AWS Lambda can serve smaller models that fit within its memory and execution time limits, making it a viable but constrained option. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between truly serverless services and those requiring manual scaling or provisioning—common traps include selecting SageMaker Batch Transform (which is batch, not real-time) or ECS/EC2 (which are not serverless). A helpful memory tip: think of SageMaker Serverless as the “auto-scaling ML specialist” and Lambda as the “general-purpose lightweight” option—if the model fits in Lambda’s 10 GB memory and 15-minute timeout, it works; otherwise, reach for SageMaker Serverless.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
Which TWO AWS services can be used to deploy a trained model for serverless inference? (Select TWO.)
AWS Lambda with a container image
Serverless compute for small models.
Amazon SageMaker Serverless Inference
Serverless, auto-scaling.
Amazon SageMaker batch transform
Why wrong: Batch, not real-time serverless.
Amazon Elastic Container Service (ECS) with Fargate
Why wrong: Fargate is serverless but not primarily for ML inference.
Amazon EC2 instances
Why wrong: Not serverless.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Lambda with a container image
SageMaker Serverless Inference automatically scales. Lambda can serve models if they fit within its limits. Option C is wrong because SageMaker batch transform is not serverless real-time. Option D is wrong because ECS is not serverless (requires management). Option E is wrong because EC2 is not serverless.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Lambda with a container image
Why this is correct
Serverless compute for small models.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Serverless Inference
Why this is correct
Serverless, auto-scaling.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker batch transform
Why it's wrong here
Batch, not real-time serverless.
Amazon Elastic Container Service (ECS) with Fargate
Why it's wrong here
Fargate is serverless but not primarily for ML inference.
Amazon EC2 instances
Why it's wrong here
Not serverless.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
Machine Learning Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
Machine Learning Implementation and Operations practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Lambda with a container image — SageMaker Serverless Inference automatically scales. Lambda can serve models if they fit within its limits. Option C is wrong because SageMaker batch transform is not serverless real-time. Option D is wrong because ECS is not serverless (requires management). Option E is wrong because EC2 is not serverless.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO AWS services can be used to deploy a machine learning model for serverless inference? (Choose 2.)
easyWhy A: SageMaker Serverless Inference (Option A) and AWS Lambda (Option C) both support serverless inference. Option B (Amazon ECS) requires managing clusters. Option D (Amazon EMR) is for big data. Option E (AWS Batch) is for batch computing.
Last reviewed: Jun 20, 2026
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.