- A
Deploy each model on a separate real-time endpoint
Why wrong: Separate endpoints for 200 models would be expensive due to many idle instances.
- B
Use a single multi-model endpoint (MME) on an ml.c5.large instance
MME hosts many models on shared instances, reducing cost while maintaining low latency for small models.
- C
Use a multi-container endpoint with one container per model
Why wrong: Multi-container endpoints are for running different containers per request, not for hosting many models; they also have a container limit.
- D
Use serverless inference for each model
Why wrong: Serverless has a per-model invoke limit and may incur cold starts; for 200 models, managing separate serverless endpoints is complex and not cost-optimal.
Question 544 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 has 200 small models (each ~100 MB) that serve different customers. They want to minimize costs while keeping low latency for each customer. Which SageMaker deployment approach is MOST suitable?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Use a single multi-model endpoint (MME) on an ml.c5.large instance
A single multi-model endpoint (MME) on an ml.c5.large instance is the most suitable because it allows you to host up to 200 small models (each ~100 MB) on a single endpoint, dynamically loading and unloading models from Amazon EBS or Amazon EFS based on inference requests. This minimizes costs by sharing a single instance across all models while maintaining low latency for each customer, as the models are small enough to be cached in memory and loaded quickly on demand.
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.
- ✗
Deploy each model on a separate real-time endpoint
Why it's wrong here
Separate endpoints for 200 models would be expensive due to many idle instances.
- ✓
Use a single multi-model endpoint (MME) on an ml.c5.large instance
Why this is correct
MME hosts many models on shared instances, reducing cost while maintaining low latency for small models.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a multi-container endpoint with one container per model
Why it's wrong here
Multi-container endpoints are for running different containers per request, not for hosting many models; they also have a container limit.
- ✗
Use serverless inference for each model
Why it's wrong here
Serverless has a per-model invoke limit and may incur cold starts; for 200 models, managing separate serverless endpoints is complex and not cost-optimal.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that multi-container endpoints are equivalent to multi-model endpoints, but the trap here is that multi-container endpoints are for chaining containers (e.g., pre-processing + inference) rather than hosting many independent models, leading candidates to overcomplicate the solution.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MME uses a model cache on the instance's local storage (EBS or EFS) and loads models into memory on demand via the SageMaker Inference Toolkit, which handles model routing based on the 'TargetModel' header in the request. For small models (~100 MB), the cache can hold multiple models simultaneously, reducing cold start latency to milliseconds, and the ml.c5.large instance (2 vCPU, 4 GB memory) can easily serve 200 models with proper memory management. A real-world scenario is a SaaS company serving per-customer ML models (e.g., recommendation or fraud detection) where each model is small and traffic is sporadic, making MME cost-effective by consolidating inference without sacrificing 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 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.
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.
- →
Deployment and Orchestration of ML Workflows — study guide chapter
Learn the concepts, then practise the questions
- →
Deployment and Orchestration of ML Workflows practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a single multi-model endpoint (MME) on an ml.c5.large instance — A single multi-model endpoint (MME) on an ml.c5.large instance is the most suitable because it allows you to host up to 200 small models (each ~100 MB) on a single endpoint, dynamically loading and unloading models from Amazon EBS or Amazon EFS based on inference requests. This minimizes costs by sharing a single instance across all models while maintaining low latency for each customer, as the models are small enough to be cached in memory and loaded quickly on demand.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 →
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.
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.