Quick Answer
The answer is gradient explosion, and the correct fix is to apply gradient clipping. A sudden spike in training loss after a period of steady decrease is the classic signature of a gradient explosion, where large weight updates destabilize the model, causing the loss to skyrocket. Gradient clipping works by capping the norm of the gradients during backpropagation, preventing these runaway updates and restoring stable training. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish gradient explosion from other common training issues: a learning rate that is too low causes slow convergence, not spikes; overfitting shows decreasing training loss but increasing validation loss; and vanishing gradients cause the loss to plateau. A common trap is confusing the sudden spike with overfitting, but remember that overfitting affects validation loss, not training loss. Memory tip: think of a rocket—if it suddenly shoots up and explodes, you need to clip its fuel (gradients) to keep it stable.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 Debugger to monitor training jobs. The training loss is decreasing but then suddenly spikes. What is the most likely cause and how should it be addressed?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Gradient explosion; apply gradient clipping.
Gradient clipping limits the gradient magnitude.
Overfitting; apply regularization.
Why wrong: Overfitting does not cause sudden spike.
Learning rate too low; increase learning rate.
Why wrong: Low learning rate leads to slow convergence.
Vanishing gradients; use ReLU activation.
Why wrong: Vanishing gradients cause plateau.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Gradient explosion; apply gradient clipping.
Option A is correct because a sudden spike in loss after decreasing often indicates a gradient explosion. Gradient clipping prevents this. Option B is wrong because learning rate that is too low causes slow convergence, not spikes. Option C is wrong because overfitting shows decreasing training loss but increasing validation loss. Option D is wrong because vanishing gradients cause loss to plateau, not spike.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Gradient explosion; apply gradient clipping.
Why this is correct
Gradient clipping limits the gradient magnitude.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Overfitting; apply regularization.
Why it's wrong here
Overfitting does not cause sudden spike.
Learning rate too low; increase learning rate.
Why it's wrong here
Low learning rate leads to slow convergence.
Vanishing gradients; use ReLU activation.
Why it's wrong here
Vanishing gradients cause plateau.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Gradient explosion; apply gradient clipping. — Option A is correct because a sudden spike in loss after decreasing often indicates a gradient explosion. Gradient clipping prevents this. Option B is wrong because learning rate that is too low causes slow convergence, not spikes. Option C is wrong because overfitting shows decreasing training loss but increasing validation loss. Option D is wrong because vanishing gradients cause loss to plateau, not spike.
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.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
Same concept, more angles
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 using Amazon SageMaker to train a custom image classification model using a PyTorch script. The training job runs successfully but the model accuracy is lower than expected. The scientist wants to debug the training process by inspecting gradients and layer outputs. Which SageMaker feature should be used to capture this internal state during training?
mediumWhy B: SageMaker Debugger captures internal model state such as gradients and tensors during training, enabling analysis and debugging. SageMaker Profiler (B) focuses on system performance, not model internals. SageMaker Experiments (C) tracks trials and metrics but does not capture internal state. SageMaker Model Monitor (D) detects data drift after deployment.
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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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