MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a classification model. The training job fails with an error indicating that the algorithm requires a GPU but the instance type does not have one. The scientist used the built-in XGBoost algorithm. What should the scientist do to resolve the issue?
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
✓
Choose a CPU instance type for the training job
XGBoost does not require a GPU; it can run on CPU. The error may be due to using a GPU-only algorithm version or misconfiguration. The simplest solution is to choose a CPU instance type. Installing a GPU version is unnecessary. Changing algorithm is not needed. Using a larger CPU instance can help but is not required. Option A: Choose a CPU instance type is correct. Option B: Installing GPU version is not needed. Option C: Changing algorithm is unnecessary. Option D: Using a larger instance may not address the issue if the instance type is still GPU-only.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Choose a CPU instance type for the training job
Why this is correct
XGBoost can run on CPU; use CPU instance.
- ✗
Install a GPU-enabled version of XGBoost in the training container
Why it's wrong here
XGBoost does not require GPU.
- ✗
Change the algorithm to a deep learning algorithm
Why it's wrong here
XGBoost is fine for the task.
- ✗
Use a larger GPU instance type
Why it's wrong here
Using a GPU instance is unnecessary.
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