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 data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use GPU instances instead of CPU instances
GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.
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 GPU instances instead of CPU instances
Why this is correct
GPUs accelerate deep learning computations.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use distributed training across multiple instances
Why this is correct
Distributed training parallelizes the workload, reducing time.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use Pipe mode to stream data from S3
Why this is correct
Pipe mode reduces I/O time by streaming data directly.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size
Why it's wrong here
Larger batch sizes can speed up training but may affect model convergence; not always beneficial.
✗
Use a smaller instance type
Why it's wrong here
Smaller instances have less compute power, increasing training time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that increasing batch size always speeds up training, but candidates overlook the memory constraints and potential negative impact on model accuracy, while also confusing smaller instance types as a cost-saving measure that inadvertently slows training.
Detailed technical explanation
How to think about this question
Under the hood, GPU instances leverage CUDA cores and tensor cores (on NVIDIA Volta and later architectures) to perform mixed-precision training (FP16) via NVIDIA's Apex or SageMaker's native support, which can double throughput. Distributed training across multiple instances uses Horovod or SageMaker's distributed data parallelism to shard the mini-batch across GPUs, synchronizing gradients via all-reduce algorithms (e.g., NCCL) to achieve near-linear scaling. Pipe mode streams training data directly from S3 into the algorithm without writing to disk, reducing I/O bottlenecks and allowing the GPU to stay saturated.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use GPU instances instead of CPU instances — GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.
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.
Question Discussion
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