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 on Amazon SageMaker and wants to reduce the training time. Which TWO actions would help achieve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use distributed training across multiple instances.
Distributed training across multiple instances (Option B) reduces training time by parallelizing the workload across multiple compute nodes, leveraging data parallelism or model parallelism to process larger batches or model partitions simultaneously. This is particularly effective for deep learning models where the dataset or model size exceeds the capacity of a single instance, as it scales throughput linearly with the number of instances under ideal conditions.
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 data augmentation.
Why it's wrong here
Augmentation increases data size, likely increasing training time.
✓
Use distributed training across multiple instances.
Why this is correct
Distributed training parallelizes computation, reducing wall-clock time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Automatic Model Tuning.
Why it's wrong here
Tuning explores multiple hyperparameter combinations, increasing total training time.
✓
Use a GPU-based instance type.
Why this is correct
GPUs accelerate deep learning training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Managed Spot Training.
Why it's wrong here
Spot training reduces cost but may not reduce time; it can cause interruptions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between cost-saving techniques (like Spot Training) and performance-enhancing techniques (like distributed training or GPU instances), leading candidates to mistakenly select Spot Training as a way to reduce training time when it only reduces cost.
Detailed technical explanation
How to think about this question
Distributed training in SageMaker uses the Horovod or Parameter Server frameworks with MPI (Message Passing Interface) for communication, often leveraging NVIDIA NCCL for GPU-to-GPU transfers. Under the hood, data parallelism splits the mini-batch across workers, and gradient synchronization via all-reduce operations (e.g., ring all-reduce) can become a bottleneck if network bandwidth is insufficient, making instance types with high-throughput networking (e.g., Elastic Fabric Adapter) critical for scaling efficiency. In real-world scenarios, using a GPU-based instance (Option D) like p3.2xlarge with a single GPU can reduce training time by 10-50x compared to CPU, but distributed training across multiple GPU instances can further reduce time for large models like ResNet-50 or BERT.
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.
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 distributed training across multiple instances. — Distributed training across multiple instances (Option B) reduces training time by parallelizing the workload across multiple compute nodes, leveraging data parallelism or model parallelism to process larger batches or model partitions simultaneously. This is particularly effective for deep learning models where the dataset or model size exceeds the capacity of a single instance, as it scales throughput linearly with the number of instances under ideal conditions.
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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