- A
Normalize numerical features to a standard range
Why wrong: Normalization is useful for distance-based models but not always required; it is not as universally necessary as imputation and encoding.
- B
Impute missing values with the mean
Imputation handles missing data and is commonly done.
- C
Encode categorical variables using one-hot encoding
One-hot encoding converts categorical data into numerical format required by most algorithms.
- D
Remove all features with low variance
Why wrong: Low variance features may still be informative; removal is not a typical first step.
- E
Increase the number of features using PCA
Why wrong: PCA reduces dimensionality, not increases features.
Question 919 of 1,000
Essential Data Preprocessing Steps
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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.
A company is preparing a dataset for training a supervised machine learning model. The dataset contains missing values, outliers, and categorical features. Which two preprocessing steps are typically performed to prepare the data? (Choose two.)
Quick Answer
The answer is imputing missing values and encoding categorical variables using one-hot encoding. These two steps are essential because most machine learning algorithms cannot handle null entries or non-numeric text, so missing values must be filled through imputation, and categorical features must be converted into numerical format via one-hot encoding to avoid implying false ordinal relationships. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the foundational data preprocessing steps required before model training, often appearing in scenario-based items where a dataset has multiple issues. A common trap is choosing outlier removal instead of imputation, but remember that missing values must be addressed first to maintain dataset integrity. For memory, think “Fill and Flag”: fill missing values, then flag categories with binary columns.
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
✓
Impute missing values with the mean
Option B is correct because imputing missing values with the mean is a standard technique to handle incomplete data, ensuring the model can process all records without discarding potentially valuable information. Option C is correct because one-hot encoding converts categorical features into a binary vector representation, which is required by most machine learning algorithms that expect numerical input.
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.
- ✗
Normalize numerical features to a standard range
Why it's wrong here
Normalization is useful for distance-based models but not always required; it is not as universally necessary as imputation and encoding.
- ✓
Impute missing values with the mean
Why this is correct
Imputation handles missing data and is commonly done.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Encode categorical variables using one-hot encoding
Why this is correct
One-hot encoding converts categorical data into numerical format required by most algorithms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all features with low variance
Why it's wrong here
Low variance features may still be informative; removal is not a typical first step.
- ✗
Increase the number of features using PCA
Why it's wrong here
PCA reduces dimensionality, not increases features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between mandatory preprocessing steps (like handling missing values and encoding categories) and optional optimization techniques (like normalization or feature selection), leading candidates to select scaling or PCA as default steps when they are not universally required.
Detailed technical explanation
How to think about this question
Imputation with the mean preserves the sample size and avoids bias if data is missing completely at random (MCAR), but it can reduce variance and distort relationships if missingness is not random. One-hot encoding creates a binary column for each category, which can lead to the dummy variable trap (perfect multicollinearity) if all categories are included; typically, one category is dropped to avoid this. In practice, for high-cardinality categorical features, target encoding or embedding layers may be preferred over one-hot encoding to avoid excessive dimensionality.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute missing values with the mean — Option B is correct because imputing missing values with the mean is a standard technique to handle incomplete data, ensuring the model can process all records without discarding potentially valuable information. Option C is correct because one-hot encoding converts categorical features into a binary vector representation, which is required by most machine learning algorithms that expect numerical input.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
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