MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a TensorFlow model on a single GPU instance. The training is taking too long. Which AWS service should be used to reduce training time by distributing the workload across multiple GPUs?
⚠ Common exam trap
The exam often tests the distinction between services that handle generic batch computing (AWS Batch) versus those specifically optimized for distributed machine learning training (Amazon SageMaker), leading candidates to mistakenly choose AWS Batch because they think 'batch' implies distributed processing.
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
✓
Amazon SageMaker
Amazon SageMaker provides built-in support for distributed training across multiple GPUs using its managed training infrastructure. By configuring a SageMaker training job with a 'distributed training' strategy (e.g., SageMaker's distributed data parallelism library), the TensorFlow model can automatically split the workload across multiple GPU instances, significantly reducing training time. SageMaker handles the underlying cluster orchestration, network setup, and fault tolerance, making it the correct choice for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker
Why this is correct
SageMaker provides built-in distributed training libraries for multi-GPU training.
- ✗
AWS Glue
Why it's wrong here
Glue is for ETL, not training.
- ✗
Amazon EMR
Why it's wrong here
EMR is for big data processing, not ML training.
- ✗
AWS Batch
Why it's wrong here
AWS Batch is not optimized for distributed GPU training.
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