MLS-C01 Modeling Practice Question
A company uses SageMaker to train a large language model. The training job is taking too long. The data scientist wants to use distributed training with data parallelism. Which SageMaker feature should be used?
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 distributed training libraries
SageMaker's distributed training libraries support data parallelism, which allows training large models faster by splitting data across multiple GPUs. Option B (SageMaker Neo) is for model optimization and compilation, not for distributed training. Option C (SageMaker Processing) is for data preprocessing and postprocessing, not for training. Option D (SageMaker Debugger) is for monitoring and debugging training jobs, not for distributing training.
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 distributed training libraries
Why this is correct
SageMaker distributed training libraries provide built-in algorithms and utilities for data parallelism and model parallelism.
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SageMaker Neo
Why it's wrong here
SageMaker Neo is used for optimizing models for deployment on specific hardware platforms.
- ✗
SageMaker Processing
Why it's wrong here
SageMaker Processing is used for running data processing workloads such as feature engineering.
- ✗
SageMaker Debugger
Why it's wrong here
SageMaker Debugger is used to monitor training jobs and debug issues like vanishing gradients.
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