- A
AWS Lambda with a container image
Serverless compute for small models.
- B
Amazon SageMaker Serverless Inference
Serverless, auto-scaling.
- C
Amazon SageMaker batch transform
Why wrong: Batch, not real-time serverless.
- D
Amazon Elastic Container Service (ECS) with Fargate
Why wrong: Fargate is serverless but not primarily for ML inference.
- E
Amazon EC2 instances
Why wrong: Not serverless.
Question 938 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
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.)
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
✓
AWS Lambda with a container image
AWS Lambda with a container image allows you to package a trained model and its dependencies into a Docker container and deploy it as a serverless function. Lambda automatically scales the inference endpoint in response to incoming requests, and you pay only for the compute time consumed during inference, with no idle infrastructure costs.
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.
- ✓
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse serverless inference with batch processing or managed container services, mistakenly selecting SageMaker batch transform or ECS with Fargate because they think 'serverless' means any managed service, but the key requirement is automatic scaling to zero and pay-per-request billing.
Detailed technical explanation
How to think about this question
AWS Lambda with container images supports images up to 10 GB in size, allowing large model artifacts (e.g., PyTorch or TensorFlow models) to be included. The Lambda runtime initializes the model on first invocation (cold start), and subsequent invocations reuse the warm container, reducing latency. For high-throughput scenarios, you can configure provisioned concurrency to pre-warm a set of execution environments, balancing cost and performance.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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Machine Learning Implementation and Operations — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Lambda with a container image — AWS Lambda with a container image allows you to package a trained model and its dependencies into a Docker container and deploy it as a serverless function. Lambda automatically scales the inference endpoint in response to incoming requests, and you pay only for the compute time consumed during inference, with no idle infrastructure costs.
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.
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 →
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Last reviewed: Jul 4, 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.
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