The answer is the presence of non-linear relationships in the data, the ratio of features to samples, and the need for model interpretability. The ratio of features to samples is critical because when the number of features far exceeds the number of samples (p >> n), standard linear regression becomes unstable due to singular covariance matrices, forcing you toward regularized methods like Ridge or Lasso to control overfitting. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your grasp of the bias-variance tradeoff and practical algorithm selection for regression problems—a common trap is assuming linear regression works universally, ignoring high-dimensional or non-linear data. For a quick memory tip, remember the three Fs: Features-to-samples ratio, Functional form (linear vs. non-linear), and Final output interpretability.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE factors should be considered when selecting the appropriate algorithm for a regression problem? (Choose 3.)
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
The interpretability requirements of the business stakeholders
Why this is correct
Some algorithms (e.g., linear regression) are more interpretable than others.
Related concept
Read the scenario before looking for a memorised answer.
✓
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, the number of features relative to samples affects the rank of the design matrix in linear models; when p > n, ordinary least squares has no unique solution, necessitating regularization (L1/L2). Non-linear relationships require algorithms like decision trees, random forests, or kernel methods (e.g., SVR with RBF kernel) to capture interactions without manual feature engineering. Interpretability trade-offs are critical in regulated industries (e.g., healthcare, finance) where stakeholders demand explainable coefficients or feature importances, favoring linear models or shallow trees over black-box ensembles.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The number of features relative to the number of samples — Option A is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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