The answer is to modify the architecture to include residual connections, also known as skip connections. This is correct because residual connections directly address the vanishing gradient problem by allowing gradients to flow unimpeded through the network via identity shortcuts, effectively bypassing the saturating activation functions that cause gradients to shrink exponentially in deep networks. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of practical deep learning optimization on SageMaker, where you must choose a targeted fix without major code rewrites—a common trap is confusing batch normalization (which stabilizes activations) with the gradient-preserving power of skip connections. Remember the mnemonic: “Residuals rescue gradients; ReLU revives neurons.”
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 research team is training a deep learning model for image classification using Amazon SageMaker. The model is a convolutional neural network (CNN) with 50 layers. The team uses a single ml.p3.2xlarge instance. After 10 hours of training, the model has not converged and the loss is decreasing very slowly. The team suspects vanishing gradients. They want to diagnose and fix the issue without significant code changes. Which action should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the architecture to include residual connections (skip connections)
Option A (use residual connections) directly addresses vanishing gradients. Option B (increase learning rate) may cause divergence. Option C (add more layers) worsens the problem. Option D (use batch normalization) helps but residual connections are more targeted for 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.
✗
Add more convolutional layers to increase model capacity
Why it's wrong here
More layers can worsen vanishing gradients.
✓
Modify the architecture to include residual connections (skip connections)
Why this is correct
Residual connections allow gradients to flow directly through the network.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use batch normalization after each convolutional layer
Why it's wrong here
Batch normalization helps but may not fully solve vanishing gradients in deep networks.
✗
Increase the learning rate by a factor of 10
Why it's wrong here
Higher learning rate may cause gradients to explode or loss to diverge.
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: Modify the architecture to include residual connections (skip connections) — Option A (use residual connections) directly addresses vanishing gradients. Option B (increase learning rate) may cause divergence. Option C (add more layers) worsens the problem. Option D (use batch normalization) helps but residual connections are more targeted for 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?
medium
A.Increase the learning rate
✓ B.Use ReLU activation functions in hidden layers
C.Switch activation functions from ReLU to sigmoid
✓ D.Add batch normalization layers
E.Remove dropout layers
Why B: ReLU activation functions help mitigate vanishing gradients because they output a constant gradient of 1 for positive inputs, preventing the gradient from shrinking as it propagates backward through many layers. This avoids the exponential decay of gradients that occurs with saturating activations like sigmoid or tanh, enabling effective training of deep networks.
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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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