MLS-C01 Modeling Practice Question
Which TWO options are best practices for training machine learning models using SageMaker? (Choose TWO.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that 'more data is always better' (Option A) or that 'bigger instances are always faster' (Option D), when in reality best practices prioritize data integrity, cost efficiency, and appropriate resource scaling.
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 incremental training when you have new data that is similar to the original training data
SageMaker's incremental training allows you to continue training an existing model with new data that shares the same schema and feature space, without retraining from scratch. This is a best practice when you have a steady stream of similar data, as it saves time and compute resources while preserving previously learned patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the final model on the combined training and test sets to maximize data usage
Why it's wrong here
The test set should be kept separate to evaluate generalization.
- ✓
Use incremental training when you have new data that is similar to the original training data
Why this is correct
Incremental training saves time by starting from an existing model.
- ✓
Use SageMaker Managed Spot Training to reduce training costs
Why this is correct
Spot instances are cost-effective for fault-tolerant training.
- ✗
Always use the largest possible instance type to minimize training time
Why it's wrong here
Larger instances may lead to diminishing returns and higher costs.
- ✗
Always enable checkpointing to save the model after every epoch
Why it's wrong here
Checkpointing is optional and depends on the training needs.
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