- A
Irrelevant features
Adding irrelevant features can introduce noise and increase training error in linear regression.
- B
Underfitting
Why wrong: Underfitting would show high error regardless of feature count; adding features might help.
- C
Multicollinearity
Why wrong: Multicollinearity affects coefficient stability but doesn't always increase training error.
- D
Overfitting
Why wrong: Overfitting typically reduces training error while increasing validation error.
Question 313 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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.
An ML engineer is training a linear regression model and notices that adding more features increases training error. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Irrelevant features
Adding more features increases training error because irrelevant features introduce noise that the model tries to fit, degrading its ability to capture the true underlying pattern. In linear regression, irrelevant features can cause the model to learn spurious correlations, increasing the residual sum of squares (RSS) on the training data. This is a classic sign of feature pollution, not overfitting or underfitting.
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.
- ✓
Irrelevant features
Why this is correct
Adding irrelevant features can introduce noise and increase training error in linear regression.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high error regardless of feature count; adding features might help.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity affects coefficient stability but doesn't always increase training error.
- ✗
Overfitting
Why it's wrong here
Overfitting typically reduces training error while increasing validation error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The AWS AI Practitioner exam often tests the misconception that adding features always reduces training error due to overfitting, but the trap here is that irrelevant features can actually increase training error by introducing noise that the model cannot ignore.
Trap categories for this question
Command / output trap
Underfitting would show high error regardless of feature count; adding features might help.
Detailed technical explanation
How to think about this question
Irrelevant features add dimensions that are uncorrelated with the target, increasing the model's variance without reducing bias. In ordinary least squares (OLS) regression, the training error (mean squared error) is minimized by the normal equation, but irrelevant features can cause the model to fit noise, raising the RSS. A real-world scenario is adding random noise columns to a housing price dataset, which forces the model to allocate weight to meaningless predictors, inflating training error.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Irrelevant features — Adding more features increases training error because irrelevant features introduce noise that the model tries to fit, degrading its ability to capture the true underlying pattern. In linear regression, irrelevant features can cause the model to learn spurious correlations, increasing the residual sum of squares (RSS) on the training data. This is a classic sign of feature pollution, not overfitting or underfitting.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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