MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model. Training is taking longer than expected. The scientist notices that the training job is using a single instance type with limited GPU memory. Which action will MOST likely reduce training time?
⚠ Common exam trap
A common mix-up: candidates confuse cost optimization (Spot Training) with performance optimization, or they assume that algorithmic improvements (batch normalization) can compensate for hardware limitations, when the real fix is scaling out compute resources.
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
✓
Configure the training job to use distributed data parallelism across multiple instances.
The training job is bottlenecked by limited GPU memory on a single instance. Distributed data parallelism splits the dataset across multiple instances, each processing a subset of the data in parallel, which directly reduces wall-clock training time by leveraging aggregate GPU memory and compute. This is the most effective action to address the stated problem of slow training due to limited GPU memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the training job to use distributed data parallelism across multiple instances.
Why this is correct
Distributed data parallelism splits the dataset across multiple GPUs/instances, reducing per-worker memory and training time.
- ✗
Use SageMaker Managed Spot Training to lower cost.
Why it's wrong here
Spot Training reduces cost but can increase total training time due to interruptions.
- ✗
Use batch normalization layers.
Why it's wrong here
Batch normalization improves convergence but does not significantly reduce training time.
- ✗
Enable SageMaker Debugger for real-time monitoring.
Why it's wrong here
SageMaker Debugger monitors training but does not reduce training time.
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