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 learning model for object detection using Amazon SageMaker. The training job is using a single GPU instance and is taking too long. Which THREE actions can reduce training time? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable mixed precision training with FP16
Option B is correct because enabling mixed precision training with FP16 reduces memory usage and accelerates computation by using half-precision floating-point numbers where possible, which is particularly effective on NVIDIA GPUs with Tensor Cores (e.g., V100, A100). This can nearly double throughput for deep learning models without sacrificing model accuracy, as critical operations still use FP32 precision.
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 a CPU instance instead of GPU
Why it's wrong here
CPU instances are slower for training deep learning models compared to GPUs.
✓
Enable mixed precision training with FP16
Why this is correct
Mixed precision uses half-precision floats, speeding up computation and reducing memory usage.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a GPU instance with more GPUs, such as p3.16xlarge
Why this is correct
More GPUs allow more parallelism, reducing training time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the batch size
Why it's wrong here
Smaller batch size reduces memory per step but increases the number of steps, often leading to longer training time.
✓
Use distributed training across multiple instances
Why this is correct
Distributed training parallelizes the workload, reducing overall training time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse reducing batch size with speeding up training, but in practice, smaller batches increase the number of gradient updates and can lead to longer wall-clock time, especially on GPU instances where larger batches better utilize parallel hardware.
Detailed technical explanation
How to think about this question
Mixed precision training leverages NVIDIA's Automatic Mixed Precision (AMP) library, which dynamically casts tensors to FP16 for matrix multiplications and convolutions while maintaining FP32 master weights and loss scaling to prevent underflow. In practice, using FP16 on a p3 instance (V100 GPUs) can yield 2-3x speedup for object detection models like YOLO or Faster R-CNN, as Tensor Cores perform FP16 operations at twice the rate of FP32.
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: Enable mixed precision training with FP16 — Option B is correct because enabling mixed precision training with FP16 reduces memory usage and accelerates computation by using half-precision floating-point numbers where possible, which is particularly effective on NVIDIA GPUs with Tensor Cores (e.g., V100, A100). This can nearly double throughput for deep learning models without sacrificing model accuracy, as critical operations still use FP32 precision.
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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