MLS-C01 Modeling Practice Question
A company is using SageMaker to train a deep learning model with TensorFlow. The training job is running on an ml.p3.16xlarge instance. The data scientist wants to maximize GPU utilization. Which configuration 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
✓
Use Pipe mode or Fast File mode with a large batch size that fits in GPU memory.
To maximize GPU utilization on an ml.p3.16xlarge instance, the data pipeline must keep GPUs busy. Option D is correct because Pipe mode or Fast File mode reduce I/O bottlenecks by streaming data directly to the GPU, and a large batch size that fits in GPU memory ensures efficient parallel processing. Option A (single GPU, more epochs) wastes GPU resources by not using all available GPUs. Option B (CPU instance for training) is counterproductive for GPU utilization. Option C (File mode, small batch size) may cause GPU idle time due to I/O bottlenecks and underutilization.
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 a single GPU and increase the number of epochs.
Why it's wrong here
Single GPU does not utilize multiple GPUs on the instance.
- ✗
Use a CPU-only instance for training and then deploy on GPU.
Why it's wrong here
Training on CPU would be slow; goal is to maximize GPU utilization.
- ✗
Use File mode input and a small batch size.
Why it's wrong here
Small batch size underutilizes GPU; file mode may have I/O delays.
- ✓
Use Pipe mode or Fast File mode with a large batch size that fits in GPU memory.
Why this is correct
Pipe mode streams data efficiently; large batch size maximizes GPU compute.
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