MLS-C01 Distributed Training Practice Question
A company is training a deep learning model for object detection using Amazon SageMaker. The training job is taking too long. Which TWO actions can reduce training time?
⚠ Common exam trap
A common trap is confusing cost-saving techniques (like spot training) with performance-enhancing techniques (like distributed 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 distributed training with multiple GPUs
Distributed training (A) reduces wall-clock time by splitting the workload across multiple GPUs. Using a smaller batch size initially and increasing gradually (warm-up) (D) can help stabilize training and speed convergence by allowing the model to adjust more smoothly early on. Managed spot training (C) reduces cost, not training time, and may increase time due to interruptions. A larger instance (B) does not necessarily reduce training time if the bottleneck is GPU-related, and increasing the number of epochs (E) increases training time.
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 distributed training with multiple GPUs
Why this is correct
Correct. Distributed training with multiple GPUs (e.g., SageMaker data parallelism) splits the data or model across devices, reducing per-epoch time.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
Incorrect. Deep learning is GPU-bound; more vCPUs have minimal impact on training speed.
- ✗
Use SageMaker managed spot training
Why it's wrong here
Incorrect. SageMaker managed spot training saves cost but can increase training time due to interruptions.
- ✓
Use a smaller batch size initially and increase gradually (warm-up)
Why this is correct
Correct. A smaller initial batch size with gradual warm-up helps the model converge faster, reducing total training time.
- ✗
Increase the number of epochs
Why it's wrong here
Incorrect. Increasing epochs increases training time; it does not speed up the process.
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