MLS-C01 Modeling Practice Question
Exhibit
Refer to the exhibit.
```
{
"DataQualityCheckConfig": {
"DatasetFormat": {
"Csv": {
"Header": true
}
},
"KmsKeyId": "",
"S3OutputPath": "s3://bucket/datachecks/",
"LocalPath": "/opt/ml/processing/output"
},
"DataQualityJobInput": {
"EndpointInput": {
"EndpointName": "my-endpoint",
"LocalPath": "/opt/ml/processing/input",
"S3InputMode": "File",
"S3DataDistributionType": "FullyReplicated",
"InferenceAttribute": "predicted_label",
"ProbabilityAttribute": "probability",
"ProbabilityThresholdAttribute": "0.5",
"StartTimeOffset": "-PT1H",
"EndTimeOffset": "-PT0H"
}
}
}
```Refer to the exhibit. A data scientist is configuring SageMaker Model Monitor for data quality checks. The configuration above is used. What is the purpose of the `ProbabilityThresholdAttribute` set to "0.5"?
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
✓
It defines the probability threshold used to convert model output to binary predictions for monitoring
In SageMaker Model Monitor, the `ProbabilityThresholdAttribute` parameter is used for binary classification models to define the probability threshold for converting model output probabilities (e.g., 0.7) to binary predictions (0 or 1). This threshold is used to monitor drift in the distribution of predictions over time, not to set the endpoint inference threshold. Option D correctly identifies this purpose. Option A is incorrect because it does not filter input data; it only defines the threshold for converting probabilities to labels for monitoring. Option B is incorrect as it does not specify a sampling threshold; sampling is configured separately. Option C is incorrect because it does not set the accuracy metric threshold; accuracy is a separate metric.
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 filters the input data to only include predictions above the threshold
Why it's wrong here
The threshold is used for labeling, not filtering input.
- ✗
It specifies the threshold for sampling data for monitoring
Why it's wrong here
Sampling is controlled by other parameters.
- ✗
It sets the threshold for the accuracy metric
Why it's wrong here
Accuracy is not directly set by this parameter.
- ✓
It defines the probability threshold used to convert model output to binary predictions for monitoring
Why this is correct
This threshold is used to compute predicted labels for monitoring purposes.
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