The correct configuration is to use SageMaker’s model parallelism library to shard the model across multiple GPUs in a single instance. This approach directly addresses the core challenge of deploying a large language model that exceeds a single GPU’s memory by distributing different layers or partitions of the model across multiple GPUs within the same instance, minimizing inter-instance communication and thus reducing inference latency. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of the distinction between model parallelism for inference and data parallelism for training, as well as the limitations of SageMaker Neo (which optimizes models but does not split them across GPUs) and multi-endpoint setups (which cannot share model shards). A common trap is confusing data parallelism with model parallelism—remember that data parallelism replicates the model across GPUs for training throughput, while model parallelism splits the model itself for memory-constrained inference. Memory tip: “Model splits, data repeats.”
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 team is deploying a model that requires GPU acceleration for inference. They are using an Amazon SageMaker real-time endpoint. The model is a large language model (LLM) that does not fit on a single GPU. Which configuration should they use to minimize latency while fitting the model?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.
Option C is correct because SageMaker supports model parallelism, allowing a model to be sharded across multiple GPUs in the same instance. Option A is wrong because SageMaker does not support multi-endpoint model parallelism. Option B is wrong because data parallelism is for training, not inference. Option D is wrong because SageMaker Neo is for optimization, not model parallelism across GPUs.
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.
✗
Use data parallelism with Horovod to distribute inference across GPUs.
Why it's wrong here
Data parallelism is for training; inference requires model parallelism.
✓
Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.
Why this is correct
Hardware and software support for large model inference.
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.
✗
Optimize the model with SageMaker Neo to reduce its size.
Why it's wrong here
Neo may not reduce size enough for LLMs; model parallelism is needed.
✗
Deploy the model across multiple endpoints and use a load balancer.
Why it's wrong here
Cannot split a single model across endpoints.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance. — Option C is correct because SageMaker supports model parallelism, allowing a model to be sharded across multiple GPUs in the same instance. Option A is wrong because SageMaker does not support multi-endpoint model parallelism. Option B is wrong because data parallelism is for training, not inference. Option D is wrong because SageMaker Neo is for optimization, not model parallelism across GPUs.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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