MLS-C01 Modeling Practice Question
A machine learning engineer is training a deep learning model on Amazon SageMaker. The training job is taking a long time. Which THREE actions can reduce training time? (Choose 3.)
⚠ Common exam trap
Test-takers frequently confuse hyperparameter tuning (which runs many jobs) with a technique that speeds up a single training job, or they mistakenly think reducing batch size always improves speed, ignoring the negative impact on convergence and hardware 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
✓
Use SageMaker managed spot training
A is correct because SageMaker managed spot training leverages spare AWS EC2 capacity at a significantly lower cost, but more importantly, it can reduce training time by allowing you to use larger or more instances for the same budget. Spot instances can be interrupted, but SageMaker automatically resumes training from the last checkpoint, making this a viable speed-up strategy for fault-tolerant deep learning jobs.
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 SageMaker managed spot training
Why this is correct
Spot instances can reduce cost and training time if interruptions are tolerated.
- ✓
Use SageMaker managed warm pools to reuse the training environment
Why this is correct
Warm pools reduce cold start time.
- ✓
Use SageMaker distributed training (data parallelism)
Why this is correct
Distributed training across multiple instances reduces wall clock time.
- ✗
Use a smaller batch size
Why it's wrong here
Smaller batch sizes can increase training time due to more updates.
- ✗
Use SageMaker hyperparameter tuning jobs
Why it's wrong here
Tuning finds optimal hyperparameters but does not directly reduce training time.
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