- A
Reduce the batch size to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.
Reducing batch size lowers GPU memory usage, and gradient accumulation allows the model to see the same number of samples per update without increasing memory.
- B
Enable SageMaker Managed Spot Training to reduce costs and use the savings to upgrade to a ml.p3.8xlarge instance.
Why wrong: This increases instance costs (even with spot savings) and does not address the immediate out-of-memory error without additional configuration.
- C
Switch to a CPU-based instance like ml.c5.2xlarge to avoid GPU memory constraints.
Why wrong: CPU instances are much slower for deep learning training and would extend training time significantly, potentially exceeding time budgets.
- D
Reduce the maximum sequence length to 128 tokens to lower memory consumption.
Why wrong: Reducing sequence length may cause loss of important context in reviews, degrading model accuracy.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 machine learning engineer is developing a text classification model using Amazon SageMaker. The dataset consists of 1 million customer reviews, with labels indicating sentiment (positive, negative, neutral). The engineer uses a pre-trained BERT model from the Hugging Face Model Hub and fine-tunes it on the dataset using SageMaker's Hugging Face estimator with a ml.p3.2xlarge instance. After 2 hours of training, the training job fails with a 'ResourceExhaustedError: CUDA out of memory' error. The error occurs during the forward pass of the first epoch. The engineer confirms that the batch size is set to 32, the maximum sequence length is 512 tokens, and the dataset is stored in a S3 bucket in the same AWS region. The engineer needs to complete fine-tuning without increasing instance costs. Which course of action should the engineer take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.
Option A is correct because reducing the batch size to 8 directly lowers GPU memory usage per forward pass, and enabling gradient accumulation with 4 steps allows the model to simulate the original effective batch size of 32 (8 × 4 = 32) without increasing memory footprint. This approach resolves the CUDA out-of-memory error while keeping the same instance type (ml.p3.2xlarge) and without incurring additional costs.
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.
- ✓
Reduce the batch size to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.
Why this is correct
Reducing batch size lowers GPU memory usage, and gradient accumulation allows the model to see the same number of samples per update without increasing memory.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable SageMaker Managed Spot Training to reduce costs and use the savings to upgrade to a ml.p3.8xlarge instance.
Why it's wrong here
This increases instance costs (even with spot savings) and does not address the immediate out-of-memory error without additional configuration.
- ✗
Switch to a CPU-based instance like ml.c5.2xlarge to avoid GPU memory constraints.
Why it's wrong here
CPU instances are much slower for deep learning training and would extend training time significantly, potentially exceeding time budgets.
- ✗
Reduce the maximum sequence length to 128 tokens to lower memory consumption.
Why it's wrong here
Reducing sequence length may cause loss of important context in reviews, degrading model accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think reducing sequence length (Option D) is the simplest fix, but they overlook that it can severely impact model performance for sentiment analysis on long reviews, while gradient accumulation (Option A) is the standard technique to handle GPU memory limits without sacrificing batch size or accuracy.
Detailed technical explanation
How to think about this question
Gradient accumulation works by performing multiple forward/backward passes with small batches, accumulating gradients without updating weights until the specified number of steps is reached. This effectively simulates a larger batch size while keeping peak GPU memory usage low, as only the small batch's activations are stored at a time. In practice, for BERT-base with 512 tokens and batch size 32 on a p3.2xlarge (16 GB GPU memory), the memory is often exceeded due to attention matrix storage; reducing batch size to 8 and accumulating over 4 steps keeps the effective batch size at 32 without memory overflow.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — 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 to 8 and enable gradient accumulation with 4 steps to maintain effective batch size. — Option A is correct because reducing the batch size to 8 directly lowers GPU memory usage per forward pass, and enabling gradient accumulation with 4 steps allows the model to simulate the original effective batch size of 32 (8 × 4 = 32) without increasing memory footprint. This approach resolves the CUDA out-of-memory error while keeping the same instance type (ml.p3.2xlarge) and without incurring additional costs.
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: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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