AIF-C01 Fundamentals of AI and ML Practice Question

A developer needs to preprocess a dataset consisting of customer reviews for sentiment analysis. Which text preprocessing technique is most likely to improve model accuracy?

⚠ Common exam trap

The AIF-C01 exam often tests the misconception that a single preprocessing step is sufficient, when in fact the combination of all three—stemming, stop word removal, and lowercasing—is standard practice for maximizing model accuracy in NLP tasks like sentiment analysis.

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

✓

All of the above

All three listed techniques—stemming, removing stop words, and lowercasing—are standard text preprocessing steps that collectively improve model accuracy for sentiment analysis. Stemming reduces words to root forms to consolidate similar meanings, removing stop words eliminates noise from high-frequency but low-information tokens, and lowercasing normalizes case variations. Together, they reduce the feature space and help the model focus on sentiment-bearing terms, leading to better generalization and accuracy.

Answer analysis

Option-by-option breakdown

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

✗
Stemming
Why it's wrong here
Stemming reduces words to root form but must be combined with others.
✓
All of the above
Why this is correct
Combining lowercasing, stop word removal, and stemming is a common and effective preprocessing pipeline.
✗
Removing stop words
Why it's wrong here
Stop word removal helps but works best with other techniques.
✗
Lowercasing
Why it's wrong here
Lowercasing alone is not as effective as combining multiple techniques.

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AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

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