- A
Reduce the model size by using fewer layers or smaller hidden dimensions.
Smaller models train faster but may lose some accuracy.
- B
Increase the learning rate by a factor of 10 to accelerate convergence.
Why wrong: Too high learning rate can cause divergence.
- C
Increase the batch size to its maximum possible value to utilize GPU memory fully.
Why wrong: Very large batch sizes can lead to poor generalization and slower convergence.
- D
Use mixed precision training (FP16) to reduce memory and speed up matrix operations.
Mixed precision uses half-precision where possible, speeding up training.
- E
Use SageMaker's distributed data parallelism across multiple instances.
Distributed training reduces wall-clock time significantly.
Question 671 of 1,755
MLS-C01 Modeling Practice Question
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 using Amazon SageMaker to train a deep learning model for natural language processing. The training job is taking too long to converge. The data scientist wants to speed up training without significantly sacrificing model accuracy. Which THREE strategies should the data scientist consider? (Choose three.)
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 model size by using fewer layers or smaller hidden dimensions.
Options A, D, and E are correct. Reducing the model size (A) decreases computational requirements and speeds up training. Mixed precision training (D) uses FP16 to reduce memory usage and accelerate matrix operations on GPUs. Distributed data parallelism (E) allows training across multiple instances, significantly reducing training time. Option B (increasing learning rate by a factor of 10) is likely too aggressive and can cause divergence. Option C (increasing batch size to maximum) may slow convergence due to reduced gradient noise and can cause memory issues.
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 model size by using fewer layers or smaller hidden dimensions.
Why this is correct
Smaller models train faster but may lose some accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the learning rate by a factor of 10 to accelerate convergence.
Why it's wrong here
Too high learning rate can cause divergence.
- ✗
Increase the batch size to its maximum possible value to utilize GPU memory fully.
Why it's wrong here
Very large batch sizes can lead to poor generalization and slower convergence.
- ✓
Use mixed precision training (FP16) to reduce memory and speed up matrix operations.
Why this is correct
Mixed precision uses half-precision where possible, speeding up training.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use SageMaker's distributed data parallelism across multiple instances.
Why this is correct
Distributed training reduces wall-clock time significantly.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Reduce the model size by using fewer layers or smaller hidden dimensions. — Options A, D, and E are correct. Reducing the model size (A) decreases computational requirements and speeds up training. Mixed precision training (D) uses FP16 to reduce memory usage and accelerate matrix operations on GPUs. Distributed data parallelism (E) allows training across multiple instances, significantly reducing training time. Option B (increasing learning rate by a factor of 10) is likely too aggressive and can cause divergence. Option C (increasing batch size to maximum) may slow convergence due to reduced gradient noise and can cause memory issues.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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