- A
Cold starts can cause increased latency for infrequent requests
Why wrong: Cold start is a characteristic, not a limitation from an exam perspective; it's a trade-off.
- B
Cannot deploy multiple containers in the same endpoint
Why wrong: Serverless endpoints support only one container per endpoint; this is a limitation but not listed as correct here because the question asks for limitations compared to real-time; real-time endpoints also typically have one container per variant. Actually, multi-container is not a standard feature; so E is not a typical comparison. Better to stick with A and B.
- C
No support for GPU instances
Serverless endpoints only support CPU.
- D
Maximum memory configuration is 6 GB
Serverless endpoints have a max memory of 6144 MB (6 GB).
- E
No automatic scaling – must be configured manually
Why wrong: Serverless scales automatically based on traffic.
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 wants to deploy a model on SageMaker serverless inference. Which TWO of the following are limitations of serverless endpoints compared to real-time endpoints? (Choose 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
No support for GPU instances
Option C is correct because SageMaker serverless inference does not support GPU instances; it only runs on CPU-based instances. This is a fundamental limitation for workloads requiring GPU acceleration, such as deep learning models. In contrast, real-time endpoints support both CPU and GPU instance types.
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.
- ✗
Cold starts can cause increased latency for infrequent requests
Why it's wrong here
Cold start is a characteristic, not a limitation from an exam perspective; it's a trade-off.
- ✗
Cannot deploy multiple containers in the same endpoint
Why it's wrong here
Serverless endpoints support only one container per endpoint; this is a limitation but not listed as correct here because the question asks for limitations compared to real-time; real-time endpoints also typically have one container per variant. Actually, multi-container is not a standard feature; so E is not a typical comparison. Better to stick with A and B.
- ✓
No support for GPU instances
Why this is correct
Serverless endpoints only support CPU.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Maximum memory configuration is 6 GB
Why this is correct
Serverless endpoints have a max memory of 6144 MB (6 GB).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
No automatic scaling – must be configured manually
Why it's wrong here
Serverless scales automatically based on traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse cold starts (option A) as a limitation unique to serverless endpoints, but the question asks for limitations compared to real-time endpoints, and cold starts are inherent to serverless, not a comparative limitation; the two correct answers are the specific technical constraints of no GPU support and the 6 GB memory cap.
Detailed technical explanation
How to think about this question
SageMaker serverless inference provisions compute resources on-demand, with a maximum memory configuration of 6 GB (option D) and a maximum invocation payload size of 6 MB. The lack of GPU support means models requiring CUDA or cuDNN libraries cannot be deployed, limiting use cases to CPU-friendly models like XGBoost or lightweight neural networks. Real-world scenarios where this matters include real-time image classification with CNNs, which would require GPU-backed real-time endpoints.
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.
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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: No support for GPU instances — Option C is correct because SageMaker serverless inference does not support GPU instances; it only runs on CPU-based instances. This is a fundamental limitation for workloads requiring GPU acceleration, such as deep learning models. In contrast, real-time endpoints support both CPU and GPU instance types.
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: Jun 24, 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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