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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 affects compute resources, not which rows land in train or test partitions, so splits vary with any random seed or sampling change. It is tempting because consistent infrastructure supports reproducibility generally, but identical hardware cannot fix a non-deterministic split.
- ✗
Use the same hyperparameter values
Why it's wrong here
Hyperparameters govern model training behaviour, not how rows are partitioned into train and test sets, so splits still differ across runs. It is tempting because fixing hyperparameters is standard reproducibility practise, but it controls learning, not the sampling that defines the split.
- ✗
Use the same dataset version
Why it's wrong here
Pinning the dataset version fixes the underlying data, yet a random split re-executed on that same version still yields different partitions unless the seed or split configuration is also fixed. It is tempting because versioning is a genuine reproducibility control, but it governs inputs, not partitioning.
- ✓
Set a random seed in the training script
Why this is correct
Setting a fixed random seed makes the pseudo-random shuffling and splitting deterministic, so every experiment reproduces the identical train/test partition. This directly satisfies the reproducibility constraint across multiple SageMaker runs without altering the underlying data.
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