MLS-C01 Distributed Training (Data Parallelism) Practice Question
A company is using SageMaker to train a linear regression model on a dataset that fits into memory on a single instance. The training job is taking longer than expected. The data scientist wants to reduce training time without changing the algorithm. Which approach is most effective?
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's distributed training with multiple instances.
SageMaker's distributed training with multiple instances performs data parallelism, splitting the dataset across instances to train concurrently, which reduces training time for data that fits in memory. Option A is wrong because disabling automatic model tuning (hyperparameter tuning) does not speed up training; it only stops searching for optimal hyperparameters. Option B is wrong because while a larger instance with more vCPUs can help, distributed training scales better and is more cost-effective for this scenario. Option D is wrong because reducing the number of epochs would likely underfit the model, hurting accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable automatic model tuning.
Why it's wrong here
Not related to training time.
- ✗
Use a larger instance type with more vCPUs.
Why it's wrong here
May not scale linearly and is costlier.
- ✓
Use SageMaker's distributed training with multiple instances.
Why this is correct
Parallel processing reduces training time.
- ✗
Reduce the number of epochs.
Why it's wrong here
Would reduce accuracy.
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