The answer is to replace the RNN cells with LSTM or GRU units, as these architectures are explicitly designed to solve vanishing gradients in RNNs. Unlike standard RNNs, which struggle to retain long-term dependencies because gradients shrink exponentially during backpropagation through time, LSTM and GRU units employ gating mechanisms—such as forget, input, and output gates—that control the flow of information and preserve gradient magnitude over many time steps. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of recurrent architectures and common training pitfalls; a frequent trap is confusing vanishing gradients with exploding gradients, where gradient clipping would be the correct fix. Remember that vanishing gradients kill long-term memory, while exploding gradients cause numerical overflow. A useful memory tip: “LSTM and GRU keep the gradient alive; clipping only saves it from a dive.”
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 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?
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 C is correct because LSTM and GRU are designed to mitigate vanishing gradients via gating mechanisms. Option A is wrong because gradient clipping addresses exploding gradients, not vanishing. Option B is wrong because increasing learning rate may cause instability. Option D is wrong because batch normalization helps with internal covariate shift but not specifically vanishing gradients.
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.
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 C is correct because LSTM and GRU are designed to mitigate vanishing gradients via gating mechanisms. Option A is wrong because gradient clipping addresses exploding gradients, not vanishing. Option B is wrong because increasing learning rate may cause instability. Option D is wrong because batch normalization helps with internal covariate shift but not specifically vanishing gradients.
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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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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