MLS-C01 Modeling Practice Question
A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?
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
✓
Increase the prefetch buffer size in the tf.data pipeline.
Increasing the prefetch buffer size in the tf.data pipeline allows the CPU to prepare batches in advance while the GPU is computing, reducing idle time and improving GPU utilization. Option A (increase learning rate) does not affect data throughput. Option C (reduce batch size) can decrease utilization as it reduces the amount of work per GPU step. Option D (increase number of CPU instances) addresses CPU capacity but the bottleneck is often data pipeline, not CPU count; increasing instances may not help. Option E (use smaller images) reduces computation per image but may not improve utilization percentage if the pipeline is the bottleneck.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate to converge faster.
Why it's wrong here
Learning rate does not affect data throughput or GPU utilization.
- ✓
Increase the prefetch buffer size in the tf.data pipeline.
Why this is correct
Prefetching overlaps CPU data preparation with GPU computation, improving GPU utilization.
- ✗
Reduce the batch size to speed up each step.
Why it's wrong here
Smaller batch size can reduce utilization as GPU processes fewer elements per step.
- ✗
Increase the number of CPU instances in the training job.
Why it's wrong here
SageMaker manages instances; adding more CPU instances may not improve data pipeline within a single instance.
- ✗
Use smaller image sizes to reduce computation.
Why it's wrong here
Smaller images reduce compute per image but may not increase utilization; can even decrease utilization if data loading becomes bottleneck.
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