MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company's ML model training on Amazon SageMaker is taking longer than expected. The training job uses a single ml.p3.2xlarge instance. Which change is most likely to reduce training time?
⚠ Common exam trap
A common mix-up: candidates confuse cost-saving techniques (like Spot Instances) with performance improvements, or mistakenly think that increasing storage or CPU cores will accelerate GPU-bound deep learning 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 GPU instances
The training job is bottlenecked by compute capacity, as a single ml.p3.2xlarge instance provides only one NVIDIA V100 GPU. Distributed training with multiple GPU instances (e.g., multiple ml.p3.2xlarge instances) enables data parallelism, splitting the workload across GPUs and significantly reducing wall-clock training time for large models or 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.
- ✗
Increase the instance's EBS volume size
Why it's wrong here
Does not speed up computation.
- ✓
Use distributed training with multiple GPU instances
Why this is correct
Parallelizes work across GPUs.
- ✗
Enable Managed Spot Training
Why it's wrong here
Reduces cost, not training time.
- ✗
Switch to a compute-optimized instance with more vCPUs
Why it's wrong here
GPU acceleration is key for deep learning.
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Written by Johnson Ajibi, MSc IT Security
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