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ML Solution Monitoring, Maintenance and Security →hardMultiple ChoiceObjective-mapped

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.

A
Use data parallelism with Horovod to distribute inference across GPUs.
Why wrong: Data parallelism is for training; inference requires model parallelism.
B
Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.
Hardware and software support for large model inference.
C
Optimize the model with SageMaker Neo to reduce its size.
Why wrong: Neo may not reduce size enough for LLMs; model parallelism is needed.
D
Deploy the model across multiple endpoints and use a load balancer.
Why wrong: Cannot split a single model across endpoints.

Full breakdown with real-world context →

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 SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.

Option B is correct because SageMaker's model parallelism library allows you to shard a large language model across multiple GPUs within a single instance, enabling inference for models that exceed a single GPU's memory. This approach minimizes latency by keeping all GPUs in a single instance with high-speed interconnects (e.g., NVLink), avoiding the network overhead of distributing across separate instances.

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

The trap here is that candidates confuse data parallelism (which replicates the model) with model parallelism (which shards the model), assuming any distributed approach works for large models, but only model parallelism solves the 'does not fit on a single GPU' constraint.

Detailed technical explanation

How to think about this question

SageMaker's model parallelism uses techniques like tensor parallelism and pipeline parallelism to split model layers and operations across GPUs. For LLMs, tensor parallelism partitions weight matrices across GPUs, reducing per-GPU memory usage while leveraging high-bandwidth intra-instance communication (e.g., NVIDIA NVLink or PCIe) to keep latency low. In real-world scenarios, this is critical for models like GPT-3 or LLaMA-70B, which require multiple GPUs even for inference.

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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.

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?

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 B is correct because SageMaker's model parallelism library allows you to shard a large language model across multiple GPUs within a single instance, enabling inference for models that exceed a single GPU's memory. This approach minimizes latency by keeping all GPUs in a single instance with high-speed interconnects (e.g., NVLink), avoiding the network overhead of distributing across separate instances.

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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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.