The answer is to delete rows with missing values if the missing rate is low and to use predictive imputation to estimate missing values. These two actions represent the core missing data handling techniques: deletion for minimal data loss when the missing rate is low, and predictive imputation for preserving data integrity by modeling relationships between features. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between simple, safe deletions and more sophisticated imputation methods that avoid bias when data is not missing completely at random. A common trap is choosing mean or median imputation over predictive imputation, but the exam emphasizes that predictive models capture complex patterns that simpler methods miss. Remember the memory tip: “Low loss, delete; complex gaps, predict.”
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 data scientist is cleaning a dataset. Which TWO actions are appropriate for handling missing data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a predictive model to estimate missing values.
Option B is correct because using a predictive model to estimate missing values is a sophisticated imputation technique that leverages relationships between features to fill gaps, preserving data integrity and avoiding bias. This approach is particularly useful when data is not missing completely at random, as it can capture complex patterns that simpler methods miss.
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.
✗
Ignore missing values and train the model directly.
Why it's wrong here
Most models cannot handle missing values directly.
✓
Use a predictive model to estimate missing values.
Why this is correct
Predictive imputation uses relationships in data, a valid advanced method.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with the mean of the entire dataset.
Why it's wrong here
Mean imputation can distort distributions and is not always appropriate.
✓
Delete rows with missing values if the missing rate is low.
Why this is correct
If missing rate is low and data is MCAR, deletion is acceptable.
Related concept
Read the scenario before looking for a memorised answer.
✗
Replace missing values with the most frequent value always.
Why it's wrong here
Replacing with mode is not universally appropriate and may introduce bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that simple imputation methods like mean or mode are always safe, when in fact they can introduce bias and distort the dataset, making predictive imputation or deletion of rows with low missing rates more appropriate depending on the context.
Detailed technical explanation
How to think about this question
Predictive imputation, such as using k-nearest neighbors or regression models, estimates missing values by training on observed data and applying the model to predict gaps, which can maintain feature correlations better than mean imputation. In real-world scenarios like healthcare datasets, where missing lab values may depend on patient demographics, this method reduces bias compared to simple imputation. However, it requires careful validation to avoid overfitting and assumes the missing mechanism is at least missing at random.
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.
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a predictive model to estimate missing values. — Option B is correct because using a predictive model to estimate missing values is a sophisticated imputation technique that leverages relationships between features to fill gaps, preserving data integrity and avoiding bias. This approach is particularly useful when data is not missing completely at random, as it can capture complex patterns that simpler methods miss.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?
easy
A.Mean imputation
✓ B.Median imputation
C.Mode imputation
D.Remove rows with missing values
Why B: Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.
Last reviewed: Jun 30, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.