- A
Deploy the model on a multi-model endpoint and include pre-processing in the model code.
Why wrong: Multi-model endpoints host multiple models on the same instance but do not support chaining containers for pre-processing.
- B
Use a batch transform job with a pre-processing script.
Why wrong: Batch transform is for asynchronous batch predictions, not real-time inference.
- C
Package pre-processing and inference in a single container with a custom entry point.
Why wrong: While possible, this doesn't leverage SageMaker's pipeline optimization and may be harder to maintain.
- D
Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
An inference pipeline chains containers sequentially, allowing pre-processing to run once per request with low latency.
Quick Answer
The correct choice is to create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference. This option minimizes latency because the inference pipeline chains both containers within a single endpoint, ensuring the pre-processing step runs exactly once per request over the same HTTP connection, avoiding redundant data serialization or separate invocations. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of real-time inference architectures, specifically how to combine custom logic with model serving without adding network hops. A common trap is selecting a single multi-model endpoint or a batch transform job, but those either process requests in parallel without guaranteed ordering or introduce latency for real-time needs. Remember the memory tip: “Pipeline pairs pre-processing with prediction in one pass,” meaning the request flows through preprocessing then inference in a single endpoint, keeping latency low and logic atomic.
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 data science team has trained a PyTorch model using Amazon SageMaker and wants to deploy it with a custom inference container that includes a pre-processing step. The team needs to minimize latency and ensure the pre-processing runs only once per request. Which SageMaker real-time inference option should they use?
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
Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
Option D is correct because a SageMaker inference pipeline allows you to chain two containers in a single endpoint, where the first container handles pre-processing and the second runs inference. This ensures that pre-processing runs exactly once per request, minimizing latency by avoiding redundant processing and keeping the request within the same HTTP connection.
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 the model on a multi-model endpoint and include pre-processing in the model code.
Why it's wrong here
Multi-model endpoints host multiple models on the same instance but do not support chaining containers for pre-processing.
- ✗
Use a batch transform job with a pre-processing script.
Why it's wrong here
Batch transform is for asynchronous batch predictions, not real-time inference.
- ✗
Package pre-processing and inference in a single container with a custom entry point.
Why it's wrong here
While possible, this doesn't leverage SageMaker's pipeline optimization and may be harder to maintain.
- ✓
Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
Why this is correct
An inference pipeline chains containers sequentially, allowing pre-processing to run once per request with low latency.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between a single-container approach (Option C) and a multi-container pipeline (Option D), where candidates mistakenly think a single custom container is simpler and sufficient, but the pipeline is required to guarantee that pre-processing runs exactly once per request and to allow independent scaling or updates of the pre-processing logic.
Detailed technical explanation
How to think about this question
Under the hood, a SageMaker inference pipeline creates a single HTTPS endpoint that invokes containers in sequence, passing the request through each container's /invocations endpoint. The first container can perform pre-processing (e.g., tokenization, feature scaling) and output a transformed payload to the next container, ensuring the pre-processing runs exactly once per request without additional network hops. This is particularly useful for models that require complex input transformations, such as NLP models needing tokenization or image models requiring resizing and normalization, where separating concerns reduces coupling and improves maintainability.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference. — Option D is correct because a SageMaker inference pipeline allows you to chain two containers in a single endpoint, where the first container handles pre-processing and the second runs inference. This ensures that pre-processing runs exactly once per request, minimizing latency by avoiding redundant processing and keeping the request within the same HTTP connection.
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.
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Last reviewed: Jun 30, 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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