MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker Debugger to monitor training jobs. The training loss is decreasing but then suddenly spikes. What is the most likely cause and how should it be addressed?
⚠ Common exam trap
A common mix-up: candidates confuse a sudden loss spike with overfitting or learning rate issues, but the key differentiator is the abrupt, non-monotonic increase in training loss (not validation loss), which points to numerical instability from exploding gradients.
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
✓
Gradient explosion; apply gradient clipping.
A sudden spike in training loss after a period of decreasing loss is a classic symptom of gradient explosion, where gradients become excessively large during backpropagation, causing the model parameters to update erratically. Amazon SageMaker Debugger can monitor tensors and gradients in real time, and applying gradient clipping (e.g., via `max_grad_norm` in PyTorch or `clip_by_global_norm` in TensorFlow) directly addresses this by capping the gradient norm to prevent destabilizing updates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Gradient explosion; apply gradient clipping.
Why this is correct
Gradient clipping limits the gradient magnitude.
- ✗
Overfitting; apply regularization.
Why it's wrong here
Overfitting does not cause sudden spike.
- ✗
Learning rate too low; increase learning rate.
Why it's wrong here
Low learning rate leads to slow convergence.
- ✗
Vanishing gradients; use ReLU activation.
Why it's wrong here
Vanishing gradients cause plateau.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Use SageMaker Experiments to track hyperparameters and metrics.
- ✓ B.Use SageMaker Debugger to capture tensors and gradients.
- C.Use SageMaker Profiler to profile system bottlenecks.
- D.Use SageMaker Model Monitor to detect data drift.
Why B: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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