hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: Which THREE steps should be taken to optimize a…
Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)
⚠ Common exam trap
Test-takers frequently confuse storage optimization (EBS) or inference features (batch transform) with training optimization, failing to recognize that distributed training performance hinges on compute, memory, and inter-node communication, not disk I/O or post-training steps.
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 with high bandwidth and memory (e.g., ml.p4d.24xlarge).
GPU instances like ml.p4d.24xlarge provide high-bandwidth GPU memory and NVLink inter-GPU connectivity, which are essential for large-scale distributed training. These instances reduce communication bottlenecks and allow larger batch sizes, directly improving throughput and model convergence speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Attach multiple EBS volumes with throughput provisioning.
Why it's wrong here
EBS enhancements affect storage I/O, not network communication.
- ✓
Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).
Why this is correct
GPU instances are necessary for large model training.
- ✗
Enable batch transform for offline inference after training.
Why it's wrong here
Batch transform is for inference, not training optimization.
- ✓
Use Elastic Fabric Adapter (EFA) for low-latency inter-node communication.
Why this is correct
EFA improves network performance for distributed deep learning.
- ✓
Select the appropriate distributed training strategy (e.g., Horovod, SageMaker data parallel, or model parallel).
Why this is correct
Choosing the right strategy maximizes efficiency.
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