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MLA-C01 Practice Question: A data scientist needs to ensure that the same…
A data scientist needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?
⚠ Common exam trap
Watch out — candidates often confuse environmental consistency (instance type, dataset version) with algorithmic determinism, overlooking that reproducibility of data splits requires explicit control of the random seed in code.
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
✓
Set a random seed in the training script
Setting a random seed in the training script ensures that the pseudo-random number generator used for splitting the dataset produces the same sequence of random indices across runs. This guarantees an identical train/test split regardless of instance type, hyperparameters, or dataset version, which is essential for reproducibility in SageMaker experiments.
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 the same SageMaker instance type
Why it's wrong here
Instance type does not affect data splitting.
- ✗
Use the same hyperparameter values
Why it's wrong here
Hyperparameters do not control data splitting.
- ✗
Use the same dataset version
Why it's wrong here
Dataset version ensures same data but not same split.
- ✓
Set a random seed in the training script
Why this is correct
Correct: Setting a random seed ensures reproducibility of random operations like data splits.
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