- A
Train a baseline model
Why wrong: Training a model is after preprocessing.
- B
Encode categorical variables
Categorical variables need to be converted to numeric.
- C
Normalize numerical features
Normalization ensures features have similar scales.
- D
Handle missing data
Missing data must be addressed to avoid errors.
- E
Select final features
Why wrong: Feature selection is part of model training, not preprocessing.
Question 420 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 team is preparing a dataset for a classification task. Which THREE data preprocessing steps should they perform? (Select THREE.)
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
✓
Encode categorical variables
Encoding categorical variables is essential because most machine learning algorithms require numerical input. Techniques like one-hot encoding or label encoding transform non-numeric categories into a format that algorithms can process, preventing the model from misinterpreting ordinal relationships where none exist.
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.
- ✗
Train a baseline model
Why it's wrong here
Training a model is after preprocessing.
- ✓
Encode categorical variables
Why this is correct
Categorical variables need to be converted to numeric.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Normalize numerical features
Why this is correct
Normalization ensures features have similar scales.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Handle missing data
Why this is correct
Missing data must be addressed to avoid errors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Select final features
Why it's wrong here
Feature selection is part of model training, not preprocessing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS AI Practitioner exams often test the distinction between preprocessing steps and later modeling or evaluation steps, so the trap here is confusing 'train a baseline model' or 'select final features' as preprocessing when they belong to the model development or feature selection phases.
Detailed technical explanation
How to think about this question
Normalization (e.g., min-max scaling) rescales features to a fixed range, typically [0,1], which prevents features with larger magnitudes from dominating distance-based algorithms like k-NN or gradient descent convergence. Handling missing data involves imputation (mean, median, or model-based) or deletion, as many algorithms cannot process NaN values and ignoring them can introduce bias. Under the hood, encoding categorical variables with one-hot creates binary columns for each category, avoiding the false ordinal assumption that label encoding might introduce.
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.
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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: Encode categorical variables — Encoding categorical variables is essential because most machine learning algorithms require numerical input. Techniques like one-hot encoding or label encoding transform non-numeric categories into a format that algorithms can process, preventing the model from misinterpreting ordinal relationships where none exist.
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.
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
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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