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Question 977 of 1,755

Modeling →hardMultiple ChoiceObjective-mapped

MLS-C01 Modeling Practice Question

This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?

A
Apply gradient clipping.
Why wrong: Gradient clipping prevents exploding gradients, not vanishing.
B
Replace the RNN cells with LSTM or GRU units.
LSTM/GRU have gating mechanisms that help preserve gradients over long sequences.
C
Add batch normalization layers.
Why wrong: Batch normalization helps with training stability but not specifically vanishing gradients.
D
Increase the learning rate.
Why wrong: Increasing learning rate may not solve vanishing gradients and can cause divergence.

Full breakdown with real-world context →

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

✓

Replace the RNN cells with LSTM or GRU units.

Option B is correct because LSTM and GRU units incorporate gating mechanisms (input, forget, output gates) that regulate the flow of gradients, effectively mitigating the vanishing gradient problem. Option A is wrong because gradient clipping is used to prevent exploding gradients, not vanishing gradients. Option C is wrong because batch normalization helps stabilize training by reducing internal covariate shift, but it does not specifically address vanishing gradients. Option D is wrong because increasing the learning rate may cause the training to diverge or become unstable, and it does not solve the vanishing gradient issue.

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.

✗
Apply gradient clipping.
Why it's wrong here
Gradient clipping prevents exploding gradients, not vanishing.
✓
Replace the RNN cells with LSTM or GRU units.
Why this is correct
LSTM/GRU have gating mechanisms that help preserve gradients over long sequences.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add batch normalization layers.
Why it's wrong here
Batch normalization helps with training stability but not specifically vanishing gradients.
✗
Increase the learning rate.
Why it's wrong here
Increasing learning rate may not solve vanishing gradients and can cause divergence.

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 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: Replace the RNN cells with LSTM or GRU units. — Option B is correct because LSTM and GRU units incorporate gating mechanisms (input, forget, output gates) that regulate the flow of gradients, effectively mitigating the vanishing gradient problem. Option A is wrong because gradient clipping is used to prevent exploding gradients, not vanishing gradients. Option C is wrong because batch normalization helps stabilize training by reducing internal covariate shift, but it does not specifically address vanishing gradients. Option D is wrong because increasing the learning rate may cause the training to diverge or become unstable, and it does not solve the vanishing gradient issue.

What should I do if I get this MLS-C01 question wrong?

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.