hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Refer to the exhibit
Exhibit
{
"TrainingJobName": "my-xgboost-job",
"HyperParameters": {
"num_round": "100",
"max_depth": "6",
"eta": "0.3",
"subsample": "0.8",
"colsample_bytree": "0.8",
"objective": "binary:logistic",
"eval_metric": "auc"
},
"InputDataConfig": [
{
"ChannelName": "train",
"DataSource": {
"S3DataSource": {
"S3Uri": "s3://my-bucket/train.csv",
"S3DataType": "S3Prefix"
}
}
},
{
"ChannelName": "validation",
"DataSource": {
"S3DataSource": {
"S3Uri": "s3://my-bucket/validation.csv",
"S3DataType": "S3Prefix"
}
}
}
],
"AlgorithmSpecification": {
"TrainingImage": "811284229777.dkr.ecr.us-west-2.amazonaws.com/xgboost:1.5-1",
"TrainingInputMode": "File"
},
"RoleArn": "arn:aws:iam::123456789012:role/SageMakerRole",
"OutputDataConfig": {
"S3OutputPath": "s3://my-bucket/output"
},
"ResourceConfig": {
"InstanceType": "ml.m5.xlarge",
"InstanceCount": 1,
"VolumeSizeInGB": 30
},
"StoppingCondition": {
"MaxRuntimeInSeconds": 86400
}
}Refer to the exhibit. A data scientist runs a SageMaker training job with the above configuration. The training completes but the model performance is poor. Which change to the hyperparameters is most likely to improve the model's AUC?
⚠ Common exam trap
The MLA-C01 exam often tests the misconception that increasing model complexity (depth, rounds, or sample usage) always improves performance, when in fact regularization techniques like lowering the learning rate are more effective for fixing poor AUC caused by overfitting.
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
✓
Decrease eta to 0.1
The training job uses XGBoost with default hyperparameters that likely cause overfitting or poor generalization. Decreasing eta (learning rate) to 0.1 slows down the learning process, allowing the model to converge more smoothly and reduce overfitting, which directly improves AUC on unseen data. This is a standard regularization technique in gradient boosting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase max_depth to 10
Why it's wrong here
Deeper trees increase model complexity and risk overfitting.
- ✗
Increase subsample to 1.0
Why it's wrong here
Using all samples per iteration reduces regularization and may cause overfitting.
- ✗
Increase num_round to 200
Why it's wrong here
More rounds with a high learning rate can lead to overfitting and poor generalization.
- ✓
Decrease eta to 0.1
Why this is correct
A lower learning rate improves generalization by taking smaller steps, often yielding better AUC.
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