AIF-C01 Fundamentals of AI and ML Practice Question
A team trains a model using Amazon SageMaker built-in XGBoost. After training, they want to evaluate feature importance. Which SageMaker feature allows them to view this?
⚠ Common exam trap
Candidates often confuse SageMaker Experiments' tracking of training metrics (like accuracy or loss) with the ability to view model-specific internals like feature importance, which is a Debugger capability.
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
✓
SageMaker Debugger
SageMaker Debugger provides built-in monitoring and visualization capabilities, including the ability to capture feature importance metrics (e.g., gain, cover, weight) from XGBoost training jobs. It automatically saves these metrics to Amazon S3 and allows you to view them through the SageMaker Studio Debugger dashboard or by querying the saved tensors, enabling direct evaluation of feature importance without additional custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker Debugger
Why this is correct
Debugger can capture internal model states like feature importance.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments track and compare training runs, not feature importance.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model building but does not provide built-in feature importance visualization.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects drift in deployed models, not training-time metrics.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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