- A
Use a smaller batch size
Why wrong: Smaller batch sizes often lead to noisier gradients, which can lead to larger gradient magnitudes, potentially worsening exploding gradients.
- B
Reduce the learning rate
Reducing the learning rate decreases the size of weight updates, helping to prevent gradients from exploding.
- C
Increase the number of layers to absorb gradients
Why wrong: Adding layers typically increases model capacity but does not directly address exploding gradients; it may even exacerbate the problem.
- D
Increase the learning rate
Why wrong: Increasing the learning rate may worsen exploding gradients because it makes weight updates larger.
Question 97 of 1,000
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 machine learning engineer is using Amazon SageMaker Debugger to monitor a training job for a deep neural network. They receive a rule alert indicating 'exploding gradients'. Which action should they take to address this issue?
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 learning rate
Exploding gradients occur when gradients become too large, causing instability. Reducing the learning rate mitigates this. Increasing batch size can also help by smoothing gradients, but reducing learning rate is a direct solution.
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 smaller batch size
Why it's wrong here
Smaller batch sizes often lead to noisier gradients, which can lead to larger gradient magnitudes, potentially worsening exploding gradients.
- ✓
Reduce the learning rate
Why this is correct
Reducing the learning rate decreases the size of weight updates, helping to prevent gradients from exploding.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of layers to absorb gradients
Why it's wrong here
Adding layers typically increases model capacity but does not directly address exploding gradients; it may even exacerbate the problem.
- ✗
Increase the learning rate
Why it's wrong here
Increasing the learning rate may worsen exploding gradients because it makes weight updates larger.
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 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
Got this wrong? Here's your next step.
Identify which MLA-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 MLA-C01 question test?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the learning rate — Exploding gradients occur when gradients become too large, causing instability. Reducing the learning rate mitigates this. Increasing batch size can also help by smoothing gradients, but reducing learning rate is a direct solution.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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