MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a custom image classification model using a PyTorch script. The training job runs successfully but the model accuracy is lower than expected. The scientist wants to debug the training process by inspecting gradients and layer outputs. Which SageMaker feature should be used to capture this internal state during training?
⚠ Common exam trap
A common mix-up: candidates confuse SageMaker Debugger (for internal state like gradients) with SageMaker Experiments (for external metrics) or SageMaker Profiler (for system performance), because all three are debugging tools but serve distinct purposes.
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
✓
Use SageMaker Debugger to capture tensors and gradients.
SageMaker Debugger is specifically designed to capture internal model state such as tensors, gradients, and weights during training. It allows you to set rules to monitor for issues like vanishing gradients or overfitting, and to save these tensors for later analysis. This directly addresses the need to inspect gradients and layer outputs to diagnose low accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Experiments to track hyperparameters and metrics.
Why it's wrong here
Experiments track metadata, not internal tensors.
- ✓
Use SageMaker Debugger to capture tensors and gradients.
Why this is correct
SageMaker Debugger provides real-time monitoring of training metrics and internal state like gradients.
- ✗
Use SageMaker Profiler to profile system bottlenecks.
Why it's wrong here
Profiler focuses on resource utilization, not model internal state.
- ✗
Use SageMaker Model Monitor to detect data drift.
Why it's wrong here
Model Monitor is for post-deployment, not training debug.
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