Quick Answer
The correct answer is imputing missing values with the mean or median, along with removing rows that contain missing data. These two techniques are commonly used to handle missing data because they address the problem from opposite ends of the simplicity spectrum: deletion is straightforward when missingness is random and the dataset is large, while imputation preserves the dataset size by filling gaps with a central tendency measure. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of data preprocessing fundamentals, often appearing as a multiple-select item where you must distinguish between valid techniques and more advanced or inappropriate methods like ignoring missing values or using complex models. A common trap is assuming that dropping rows is always bad, but the exam emphasizes that it is acceptable when the missingness is random and the sample remains large. Memory tip: think “Delete or Fill” — if you cannot delete safely, fill with the mean or median to keep your data intact.
This AI0-001 practice question tests your understanding of ai concepts and foundations. 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 TWO techniques are commonly used to handle missing data in a dataset?
Feature scaling
Why wrong: Scaling does not address missing values.
One-hot encoding
Why wrong: Encoding is for categorical features, not missing values.
Remove rows with missing values
Simple deletion if missing data is minimal.
Impute with mean or median
Fills missing values with central tendency.
Principal component analysis (PCA)
Why wrong: PCA reduces dimensionality, not imputes missing data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Remove rows with missing values
Option C is correct because removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Feature scaling
Why it's wrong here
Scaling does not address missing values.
One-hot encoding
Why it's wrong here
Encoding is for categorical features, not missing values.
Remove rows with missing values
Why this is correct
Simple deletion if missing data is minimal.
Related concept
Read the scenario before looking for a memorised answer.
Impute with mean or median
Why this is correct
Fills missing values with central tendency.
Related concept
Read the scenario before looking for a memorised answer.
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality, not imputes missing data.
Common exam traps
CompTIA often tests the distinction between data preprocessing techniques that handle missing values versus those that transform or reduce features, so candidates may confuse feature scaling or PCA with missing data handling because they are all part of data preparation.
Detailed technical explanation
Under the hood, mean imputation replaces missing values with the arithmetic mean of the observed values for that feature, which preserves the sample mean but artificially reduces variance and can bias correlations. In real-world scenarios, such as medical datasets where missingness is often non-random, simple imputation can lead to misleading model performance, and more advanced methods like multiple imputation (e.g., using MICE) are preferred. The choice between removal and imputation depends on the missing data mechanism (MCAR, MAR, MNAR) and the proportion of missing values.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
The correct answer is: Remove rows with missing values — Option C is correct because removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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