MLS-C01 Modeling Practice Question
A team is training a large deep learning model on Amazon SageMaker. The training job is taking too long and they want to reduce training time without changing the model architecture. Which action is MOST effective?
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 a GPU instance (e.g., p3.2xlarge) for training
Using a SageMaker managed training instance with GPU (e.g., p3.2xlarge) provides significant acceleration for deep learning models due to parallel processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a compute-optimized instance like c5.4xlarge
Why it's wrong here
CPU instances are slower for deep learning than GPU instances.
- ✓
Use a GPU instance (e.g., p3.2xlarge) for training
Why this is correct
GPUs dramatically speed up matrix operations common in deep learning.
- ✗
Increase the batch size and learning rate proportionally
Why it's wrong here
This may affect convergence and model quality.
- ✗
Use SageMaker Automatic Model Tuning with hyperparameter optimization
Why it's wrong here
Hyperparameter tuning finds better parameters but does not directly reduce training time.
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