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Machine Learning Implementation and Operations →easyMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?

A
Enable automatic hyperparameter tuning.
Why wrong: Hyperparameter tuning does not directly reduce memory usage.
B
Switch to distributed training across multiple instances.
Why wrong: Distributed training can increase memory usage per node.
C
Use a smaller instance type with less GPU memory.
Why wrong: Smaller instance has less memory, not more.
D
Reduce the batch size in the training script.
Smaller batch size reduces memory footprint.

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

✓

Reduce the batch size in the training script.

An OutOfMemoryError during GPU training indicates that the GPU's memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, as fewer samples and their corresponding activations are stored simultaneously. This is the most immediate and effective fix without changing the instance type or training architecture.

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.

✗
Enable automatic hyperparameter tuning.
Why it's wrong here
Hyperparameter tuning does not directly reduce memory usage.
✗
Switch to distributed training across multiple instances.
Why it's wrong here
Distributed training can increase memory usage per node.
✗
Use a smaller instance type with less GPU memory.
Why it's wrong here
Smaller instance has less memory, not more.
✓
Reduce the batch size in the training script.
Why this is correct
Smaller batch size reduces memory footprint.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The MLS-C01 exam often tests the misconception that scaling up hardware (distributed training) solves memory errors, but the correct approach is to reduce per-instance memory load, typically by lowering batch size.

Detailed technical explanation

How to think about this question

GPU memory is consumed by model parameters, optimizer states, and activations for each batch. Reducing batch size lowers the peak memory usage of activations, which scales linearly with batch size. In frameworks like PyTorch or TensorFlow, this is often the first diagnostic step before considering gradient accumulation or mixed-precision training, which can also help but require code changes beyond simple batch size adjustment.

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.

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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 MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Reduce the batch size in the training script. — An OutOfMemoryError during GPU training indicates that the GPU's memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, as fewer samples and their corresponding activations are stored simultaneously. This is the most immediate and effective fix without changing the instance type or training architecture.

What should I do if I get this MLS-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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This MLS-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 MLS-C01 exam.