MLA-C01 ML Model Development Practice Question
A data scientist is training an XGBoost model on a large tabular dataset using SageMaker. The training job is taking too long. The scientist wants to reduce training time while maintaining model quality. Which action should the scientist take?
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 distributed data parallelism across multiple instances
Using SageMaker's managed spot training can significantly reduce cost, but it may cause interruptions. The best approach to reduce training time is to use distributed data parallelism with multiple instances. Increasing instance type can also speed up training, but distributed training is more scalable. Using Hyperband is for hyperparameter tuning, not for reducing training time directly. Converting to a different algorithm is not necessary.
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 distributed data parallelism across multiple instances
Why this is correct
Distributed data parallelism speeds up training by splitting data across multiple instances.
- ✗
Enable SageMaker managed spot training
Why it's wrong here
Spot training reduces cost, not necessarily training time, and may cause interruptions.
- ✗
Switch to Hyperband for hyperparameter tuning
Why it's wrong here
Hyperband is for tuning, not for reducing training time of a given job.
- ✗
Convert the XGBoost model to a Linear Learner model
Why it's wrong here
Changing the algorithm may not maintain model quality and is not a direct solution to reduce training time.
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