MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long and the scientist wants to reduce the training time by distributing the workload across multiple GPUs. Which SageMaker feature should be used to achieve this?
⚠ Common exam trap
Test-takers frequently confuse hyperparameter tuning (which runs multiple independent training jobs) with distributed training (which splits a single training job across multiple GPUs), leading them to select options C or D.
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's distributed training libraries
SageMaker's distributed training libraries provide built-in optimized implementations of data parallelism and model parallelism, enabling efficient distribution of deep learning workloads across multiple GPUs with minimal code changes. This directly addresses the need to reduce training time by leveraging multiple GPUs in a SageMaker training job.
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's distributed training libraries
Why this is correct
SageMaker provides built-in distributed training libraries that can split the workload across multiple GPUs.
- ✗
Use Amazon EMR to distribute the training
Why it's wrong here
Amazon EMR is for big data processing using Hadoop/Spark, not for deep learning training.
- ✗
Use SageMaker Automatic Model Tuning
Why it's wrong here
Automatic Model Tuning is also for hyperparameter optimization, not distributed training.
- ✗
Use SageMaker Hyperparameter Tuning
Why it's wrong here
Hyperparameter Tuning optimizes hyperparameters but does not distribute training across GPUs.
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