MLS-C01 Modeling Practice Question

Which THREE factors should be considered when selecting the appropriate algorithm for a regression problem? (Choose 3.)

⚠ Common exam trap

AWS often tests the distinction between operational concerns (like training time or hardware) and core modeling factors, expecting candidates to recognize that irrelevant options (time of day, laptop color) are clear distractors while the three correct factors directly influence algorithm performance and business suitability.

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

✓

The number of features relative to the number of samples

The ratio of features to samples directly impacts model complexity and overfitting risk. In high-dimensional settings (e.g., p >> n), algorithms like linear regression may fail due to singular covariance matrices, while regularized methods (Ridge, Lasso) or tree-based models become necessary. This is a core consideration in the bias-variance tradeoff for regression problems.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
The number of features relative to the number of samples
Why this is correct
High-dimensional data may require regularization.
✓
The interpretability requirements of the business stakeholders
Why this is correct
Some algorithms (e.g., linear regression) are more interpretable than others.
✓
The presence of non-linear relationships in the data
Why this is correct
Non-linear data may need algorithms like decision trees or neural networks.
✗
The time of day the training will occur
Why it's wrong here
Irrelevant.
✗
The color of the data scientist's laptop
Why it's wrong here
Irrelevant.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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