The answer is deletion of rows with missing values and imputation with mean or median. Deletion, often called listwise deletion, removes any row containing a null value, which is straightforward but can reduce dataset size and introduce bias if missingness is not random. Imputation with mean or median replaces missing numerical entries with the central tendency of the observed data, preserving the dataset size and allowing the model to learn patterns without discarding information, though it may slightly reduce variance. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of practical data preprocessing trade-offs; a common trap is assuming deletion is always safe or that imputation with zero is standard. Remember the memory tip: “Drop or fill, don’t stand still”—when handling missing data in machine learning, you either drop the row or fill the gap with a central value.
AI0-001 Machine Learning and Deep Learning Practice Question
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 TWO techniques are commonly used to handle missing data in a machine learning dataset? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Imputation with mean or median
Imputation with mean or median is a standard technique for handling missing numerical data because it preserves the dataset size and avoids introducing bias from simply discarding rows. By replacing missing values with the central tendency of the observed data, the model can still learn patterns without losing information, though it may reduce variance slightly.
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.
✗
Normalization
Why it's wrong here
Normalization scales features to a range, not for missing data.
✓
Imputation with mean or median
Why this is correct
Replacing missing values with mean/median is a common imputation method.
Related concept
Read the scenario before looking for a memorised answer.
✓
Deletion of rows with missing values
Why this is correct
Removing rows with missing data is a straightforward approach when the missing rate is low.
Related concept
Read the scenario before looking for a memorised answer.
✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables to binary, not a missing data technique.
✗
Dimensionality reduction
Why it's wrong here
Dimensionality reduction reduces number of features, not for missing data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between data preprocessing techniques (like normalization and encoding) and actual missing data handling methods, so candidates mistakenly select normalization or one-hot encoding as solutions for missing values.
Detailed technical explanation
How to think about this question
Under the hood, mean/median imputation assumes the data is missing completely at random (MCAR) and can distort the distribution and relationships between features if the missingness is systematic. In real-world scenarios like sensor data with intermittent failures, mean imputation might mask anomalies, while deletion of rows (listwise deletion) is only safe when the missing data is a small fraction and MCAR, as it can otherwise introduce selection bias.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Imputation with mean or median — Imputation with mean or median is a standard technique for handling missing numerical data because it preserves the dataset size and avoids introducing bias from simply discarding rows. By replacing missing values with the central tendency of the observed data, the model can still learn patterns without losing information, though it may reduce variance slightly.
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.
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Question Discussion
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