- A
Gradient explosion; apply gradient clipping.
Gradient clipping limits the gradient magnitude.
- B
Overfitting; apply regularization.
Why wrong: Overfitting does not cause sudden spike.
- C
Learning rate too low; increase learning rate.
Why wrong: Low learning rate leads to slow convergence.
- D
Vanishing gradients; use ReLU activation.
Why wrong: Vanishing gradients cause plateau.
Question 738 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 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.
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
✓
Gradient explosion; apply gradient clipping.
A sudden spike in training loss after a period of decreasing loss is a classic symptom of gradient explosion, where gradients become excessively large during backpropagation, causing the model parameters to update erratically. Amazon SageMaker Debugger can monitor tensors and gradients in real time, and applying gradient clipping (e.g., via `max_grad_norm` in PyTorch or `clip_by_global_norm` in TensorFlow) directly addresses this by capping the gradient norm to prevent destabilizing updates.
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.
- ✓
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse a sudden loss spike with overfitting or learning rate issues, but the key differentiator is the abrupt, non-monotonic increase in training loss (not validation loss), which points to numerical instability from exploding gradients.
Detailed technical explanation
How to think about this question
Gradient explosion occurs when the norm of the gradient grows exponentially during backpropagation through deep networks, often due to large weight initializations or unstable architectures (e.g., RNNs without gradient clipping). SageMaker Debugger can be configured to emit a `LossNotDecreasing` or custom rule that triggers an alert when the gradient norm exceeds a threshold (e.g., > 1e10), allowing automated intervention via a training job hook. In practice, gradient clipping is applied per-batch, and the clipping threshold (e.g., 1.0) is a hyperparameter that balances stability and convergence speed.
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.
TExam Day Tips
- 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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Gradient explosion; apply gradient clipping. — A sudden spike in training loss after a period of decreasing loss is a classic symptom of gradient explosion, where gradients become excessively large during backpropagation, causing the model parameters to update erratically. Amazon SageMaker Debugger can monitor tensors and gradients in real time, and applying gradient clipping (e.g., via `max_grad_norm` in PyTorch or `clip_by_global_norm` in TensorFlow) directly addresses this by capping the gradient norm to prevent destabilizing updates.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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