Quick Answer
The answer is to use a larger GPU instance type, such as the p3.16xlarge, because it directly reduces deep learning training time by providing significantly more GPU memory, CUDA cores, and memory bandwidth. This hardware upgrade allows you to increase batch sizes and execute matrix operations in more efficient parallel passes, which accelerates both the forward and backward propagation steps during training. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how GPU architecture—specifically memory and core count—affects training throughput, often appearing as a distractor against options like switching to a CPU instance or reducing the dataset size. A common trap is assuming that simply adding more instances (distributed training) is always faster, but for a single model that fits on one GPU, a larger instance like p3.16xlarge avoids inter-node communication overhead. Memory tip: think “bigger GPU, bigger batches, faster matches”—more CUDA cores mean more parallel computations per second.
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 uses Amazon SageMaker to train a deep learning model on a GPU instance. The training job is taking too long. Which action would MOST likely reduce training time?
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.
Reduce the mini-batch size
Why wrong: Smaller batch size can make training slower per epoch.
Use distributed data parallelism across multiple smaller instances
Why wrong: Distributed training may have overhead and not always reduce time.
Use a larger GPU instance type, such as p3.16xlarge
More powerful GPU accelerates training.
Reduce the number of epochs
Why wrong: Reducing epochs may degrade model accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a larger GPU instance type, such as p3.16xlarge
Option C is correct because using a larger GPU instance like p3.16xlarge provides significantly more GPU memory, CUDA cores, and memory bandwidth, which allows for larger batch sizes and more efficient parallel processing of matrix operations. This directly reduces training time for deep learning models by enabling faster forward and backward passes through the network, especially when the model is large enough to fully utilize the additional GPU resources.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Reduce the mini-batch size
Why it's wrong here
Smaller batch size can make training slower per epoch.
Use distributed data parallelism across multiple smaller instances
Why it's wrong here
Distributed training may have overhead and not always reduce time.
Use a larger GPU instance type, such as p3.16xlarge
Why this is correct
More powerful GPU accelerates training.
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.
Reduce the number of epochs
Why it's wrong here
Reducing epochs may degrade model accuracy.
Common exam traps
The trap here is that candidates often confuse reducing mini-batch size (Option A) with improving training speed, but in GPU-accelerated deep learning, larger batch sizes better utilize GPU parallelism and reduce the number of iterations, making a larger instance the more effective solution.
Detailed technical explanation
Under the hood, larger GPU instances like p3.16xlarge use NVIDIA V100 GPUs with Tensor Cores that accelerate mixed-precision training (FP16), which can double throughput compared to FP32. The increased memory bandwidth (900 GB/s on V100 vs. 300 GB/s on K80) allows faster data movement between GPU memory and compute units, reducing the time spent on memory-bound operations like batch normalization and activation functions. In practice, for models like ResNet-50 or BERT, scaling to a p3.16xlarge can reduce training time by 3-5x compared to a p3.2xlarge, assuming the model fits in GPU memory.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use a larger GPU instance type, such as p3.16xlarge — Option C is correct because using a larger GPU instance like p3.16xlarge provides significantly more GPU memory, CUDA cores, and memory bandwidth, which allows for larger batch sizes and more efficient parallel processing of matrix operations. This directly reduces training time for deep learning models by enabling faster forward and backward passes through the network, especially when the model is large enough to fully utilize the additional GPU resources.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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