MLS-C01 Modeling Practice Question
A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?
⚠ Common exam trap
AWS often tests the misconception that increasing batch size always speeds up training, but candidates overlook the memory constraints and potential negative impact on model accuracy, while also confusing smaller instance types as a cost-saving measure that inadvertently slows training.
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 GPU instances instead of CPU instances
GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.
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 GPU instances instead of CPU instances
Why this is correct
GPUs accelerate deep learning computations.
- ✓
Use distributed training across multiple instances
Why this is correct
Distributed training parallelizes the workload, reducing time.
- ✓
Use Pipe mode to stream data from S3
Why this is correct
Pipe mode in SageMaker streams training data directly from S3 without first downloading it to the training instance’s local storage, eliminating the I/O bottleneck caused by full dataset copying. This reduces disk-write latency and storage provisioning overhead, directly addressing the stem’s constraint of prolonged training time by enabling the neural network to begin processing batches sooner.
- ✗
Increase the batch size
Why it's wrong here
Larger batch sizes can speed up training but may affect model convergence; not always beneficial.
- ✗
Use a smaller instance type
Why it's wrong here
Smaller instances have less compute power, increasing training time.
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