MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses SageMaker to train a model, but the training job fails due to insufficient memory. What is the most cost-effective way to resolve this?
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 a larger instance type with more memory
Increasing instance memory directly addresses the memory issue. Option B is wrong because Spot Instances do not provide additional memory; they are a pricing model. Option C is wrong because reducing batch size may not solve memory issues if the model itself is large, and it can affect training dynamics. Option D is wrong because distributed training adds complexity and cost, and may be overkill.
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 a larger instance type with more memory
Why this is correct
Using a larger instance type with more memory directly resolves insufficient memory.
- ✗
Use Spot Instances to reduce cost
Why it's wrong here
Spot Instances offer cost savings but do not provide more memory, so they don't fix the memory issue.
- ✗
Reduce the batch size in the training script
Why it's wrong here
Reducing batch size can lower memory usage per step but may not be sufficient if the model is large; it can also affect training stability.
- ✗
Switch to distributed training across multiple instances
Why it's wrong here
Distributed training adds complexity and cost; it may not be the most cost-effective solution for a memory issue that could be solved by a single larger instance.
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