This MLA-C01 practice question tests your understanding of what will the debugger do with this configuration?. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Exhibit
Refer to the exhibit. You are configuring SageMaker Debugger for a training job. The following is part of the debugger configuration:
{
"DebugHookConfig": {
"CollectionConfigurations": [
{
"CollectionName": "gradients",
"Parameters": {
"save_interval": "500"
}
}
]
},
"DebugRules": [
{
"RuleConfigurationName": "LossNotDecreasing",
"RuleParameters": {
"rule_to_use": "LossNotDecreasing",
"save_interval": "500",
"patience": "10",
"threshold": "0.001"
}
}
]
}
What will the debugger do with this configuration?
Exhibit
Refer to the exhibit. You are configuring SageMaker Debugger for a training job. The following is part of the debugger configuration:
{
"DebugHookConfig": {
"CollectionConfigurations": [
{
"CollectionName": "gradients",
"Parameters": {
"save_interval": "500"
}
}
]
},
"DebugRules": [
{
"RuleConfigurationName": "LossNotDecreasing",
"RuleParameters": {
"rule_to_use": "LossNotDecreasing",
"save_interval": "500",
"patience": "10",
"threshold": "0.001"
}
}
]
}
A
It will only capture gradients and not run any rules because the rule name is misspelled.
Why wrong: The rule name 'LossNotDecreasing' is a valid built-in rule; the rule will execute as configured.
B
It will capture gradients every 10 steps and trigger a rule if loss does not decrease for 500 epochs.
Why wrong: The save_interval for gradients is 500, not 10, and patience is 10, not 500.
C
It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 10 steps with a threshold of 0.001.
The collection captures gradients every 500 steps; the rule parametrs (patience=10, threshold=0.001) define when to alert.
D
It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 500 iterations with a patience of 10.
Why wrong: Patience is 10, not 500 iterations. The patience parameter specifically sets the number of steps to wait before alerting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 10 steps with a threshold of 0.001.
Option C is correct because the debugger configuration uses `capture_gradient_every_n_steps=500` to capture gradients every 500 steps, and `trigger_rule_on_loss_not_decrease_for_n_steps=10` with a threshold of 0.001 to trigger a rule when loss does not decrease for 10 consecutive steps. The parameters are correctly interpreted: the first integer after `capture_gradient_every_n_steps` sets the step interval, and the second integer after `trigger_rule_on_loss_not_decrease_for_n_steps` sets the patience (number of steps without decrease) before triggering, with the threshold defining the minimum required decrease.
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.
✗
It will only capture gradients and not run any rules because the rule name is misspelled.
Why it's wrong here
The rule name 'LossNotDecreasing' is a valid built-in rule; the rule will execute as configured.
✗
It will capture gradients every 10 steps and trigger a rule if loss does not decrease for 500 epochs.
Why it's wrong here
The save_interval for gradients is 500, not 10, and patience is 10, not 500.
✓
It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 10 steps with a threshold of 0.001.
Why this is correct
The collection captures gradients every 500 steps; the rule parametrs (patience=10, threshold=0.001) define when to alert.
Related concept
Read the scenario before looking for a memorised answer.
✗
It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 500 iterations with a patience of 10.
Why it's wrong here
Patience is 10, not 500 iterations. The patience parameter specifically sets the number of steps to wait before alerting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall when interpreting AWS SageMaker Debugger configuration strings is swapping the step interval for gradient capture and the patience for the loss decrease rule, leading candidates to misread the numeric parameters.
Detailed technical explanation
How to think about this question
Under the hood, the debugger's `trigger_rule_on_loss_not_decrease_for_n_steps` parameter monitors the loss value at each step and compares the difference against the threshold (0.001). If the loss fails to decrease by at least the threshold for the specified number of consecutive steps (10), the rule fires. This is commonly used in training loops to detect plateaus early, allowing dynamic adjustments like learning rate reduction. In real-world scenarios, such as fine-tuning large language models, this prevents wasted compute by stopping or modifying training when convergence stalls.
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.
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: It will capture gradients every 500 steps and trigger a rule if loss does not decrease for 10 steps with a threshold of 0.001. — Option C is correct because the debugger configuration uses `capture_gradient_every_n_steps=500` to capture gradients every 500 steps, and `trigger_rule_on_loss_not_decrease_for_n_steps=10` with a threshold of 0.001 to trigger a rule when loss does not decrease for 10 consecutive steps. The parameters are correctly interpreted: the first integer after `capture_gradient_every_n_steps` sets the step interval, and the second integer after `trigger_rule_on_loss_not_decrease_for_n_steps` sets the patience (number of steps without decrease) before triggering, with the threshold defining the minimum required decrease.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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