- A
The endpoint needs to have a smaller batch size configured in the real-time inference request.
Why wrong: InvokeEndpoint typically sends one record at a time; batch size is not the issue. The model itself is too large for the instance memory.
- B
The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge.
A 2 GB model plus runtime overhead (e.g., Java heap for XGBoost) can exceed 8 GB. Increasing instance memory resolves the out-of-memory error.
- C
The model is a Transformer model and requires a GPU instance; use ml.g4dn.xlarge instead.
Why wrong: XGBoost is not a Transformer model; it does not require GPU. The error is heap space, not compute.
- D
The SageMaker container is not compatible with XGBoost; switch to a framework container.
Why wrong: XGBoost is supported by the built-in SageMaker container; the issue is memory, not compatibility.
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. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 a SageMaker endpoint that was deployed successfully and is in service. However, when the team sends test inferences using the InvokeEndpoint API, they receive a 500 internal server error. The endpoint logs in CloudWatch show a stack trace indicating 'OutOfMemoryError: Java heap space'. The model is a large XGBoost model (2 GB) and the endpoint is using an ml.m5.large instance with 8 GB of memory. What is the MOST likely cause and solution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge.
The OutOfMemoryError in Java heap space indicates that the model (2 GB) plus the runtime overhead of the XGBoost container and Java-based inference code exceed the available memory on the ml.m5.large instance (8 GB total, but not all is available for the Java heap). The most direct fix is to use a larger instance type, such as ml.m5.xlarge (16 GB) or ml.m5.2xlarge, to provide sufficient heap space for the model and inference operations.
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.
- ✗
The endpoint needs to have a smaller batch size configured in the real-time inference request.
Why it's wrong here
InvokeEndpoint typically sends one record at a time; batch size is not the issue. The model itself is too large for the instance memory.
- ✓
The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge.
Why this is correct
A 2 GB model plus runtime overhead (e.g., Java heap for XGBoost) can exceed 8 GB. Increasing instance memory resolves the out-of-memory error.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model is a Transformer model and requires a GPU instance; use ml.g4dn.xlarge instead.
Why it's wrong here
XGBoost is not a Transformer model; it does not require GPU. The error is heap space, not compute.
- ✗
The SageMaker container is not compatible with XGBoost; switch to a framework container.
Why it's wrong here
XGBoost is supported by the built-in SageMaker container; the issue is memory, not compatibility.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may incorrectly attribute the OutOfMemoryError to batch size or container compatibility, rather than recognizing that the instance's memory is insufficient for the model size and Java heap overhead.
Detailed technical explanation
How to think about this question
SageMaker XGBoost containers run the model in a Java-based serving stack (e.g., using MxNet or SageMaker Inference Toolkit), which allocates a fixed Java heap size (typically 50-75% of instance memory). On an ml.m5.large with 8 GB, the Java heap might be limited to ~4-6 GB, which is insufficient for a 2 GB model plus serialization/deserialization overhead, leading to OutOfMemoryError. In practice, you should also consider enabling model parallelism or using a larger instance with more memory headroom for peak loads.
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: The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge. — The OutOfMemoryError in Java heap space indicates that the model (2 GB) plus the runtime overhead of the XGBoost container and Java-based inference code exceed the available memory on the ml.m5.large instance (8 GB total, but not all is available for the Java heap). The most direct fix is to use a larger instance type, such as ml.m5.xlarge (16 GB) or ml.m5.2xlarge, to provide sufficient heap space for the model and inference operations.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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