- A
Deletion of rows with missing values
Listwise deletion removes incomplete records; a basic approach.
- B
Autoencoder reconstruction
Why wrong: Autoencoders can impute but are advanced and not standard for routine missing data.
- C
Mean imputation
Replacing missing values with the column mean is a common simple imputation method.
- D
Principal Component Analysis
Why wrong: PCA reduces dimensionality and does not handle missing values directly.
- E
Using a separate category for missing values
Treating 'missing' as a distinct category is used for categorical data.
Question 729 of 1,000
Handling Missing Data — Imputation and Deletion Techniques | CompTIA AI+ Explained
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.
Which THREE of the following are techniques for handling missing data in machine learning?
Quick Answer
The answer is mean imputation, deletion, and using a separate category for missing values. These three techniques are considered standard because they directly address gaps in data without introducing complex model-based assumptions: mean imputation fills missing entries with the column average to preserve sample size, deletion removes incomplete rows to avoid bias, and flagging missing values adds an indicator column to let the model learn from the absence itself. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of practical data preprocessing versus advanced or unrelated methods—common traps include confusing autoencoder imputation (a deep learning approach not covered as a standard technique) with these basics, or mistaking PCA, which reduces dimensions, for a missing data handler. A quick memory tip: think “Delete, Fill, Flag” for the three core actions, and remember that any technique involving dimensionality reduction or neural networks is likely outside the standard scope.
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
✓
Deletion of rows with missing values
Option A is correct because deleting rows with missing values is a straightforward technique for handling missing data, often used when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This method avoids introducing bias from imputation but can lead to loss of valuable information if too many rows are removed.
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.
- ✓
Deletion of rows with missing values
Why this is correct
Listwise deletion removes incomplete records; a basic approach.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Autoencoder reconstruction
Why it's wrong here
Autoencoders can impute but are advanced and not standard for routine missing data.
- ✓
Mean imputation
Why this is correct
Replacing missing values with the column mean is a common simple imputation method.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Principal Component Analysis
Why it's wrong here
PCA reduces dimensionality and does not handle missing values directly.
- ✓
Using a separate category for missing values
Why this is correct
Treating 'missing' as a distinct category is used for categorical data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The CompTIA AI exam often tests the distinction between techniques that directly handle missing data versus those that are preprocessing or modeling steps that assume complete data, leading candidates to mistakenly select PCA or autoencoder reconstruction as missing data methods.
Detailed technical explanation
How to think about this question
Mean imputation (Option C) replaces missing values with the mean of the observed values for that feature, which preserves the sample size but can reduce variance and distort relationships between variables. Using a separate category for missing values (Option E) is common for categorical data, where missingness is treated as its own level, allowing the model to learn patterns associated with missing entries. In practice, the choice of technique depends on the missing data mechanism (MCAR, MAR, MNAR) and the model's robustness to missingness.
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
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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: Deletion of rows with missing values — Option A is correct because deleting rows with missing values is a straightforward technique for handling missing data, often used when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This method avoids introducing bias from imputation but can lead to loss of valuable information if too many rows are removed.
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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