The answer is removing duplicate records, correcting inconsistent data, and handling missing values. These three steps are foundational to data cleaning because they directly address the most common data quality issues: duplicates inflate analysis results, inconsistencies like mixed date formats or capitalization errors distort patterns, and missing values can bias statistical models if not properly imputed or excluded. On the CompTIA Data+ DA0-001 exam, this question tests your ability to distinguish core cleaning actions from later transformation or validation steps—a common trap is confusing data profiling (which identifies issues) with cleaning itself. Remember the mnemonic “DIM” for Duplicates, Inconsistencies, and Missing values: if a step doesn’t fix one of these three, it’s likely not a cleaning step.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Correcting inconsistent data
Correcting inconsistent data (Option A) is a core data cleaning step because it ensures that values follow a consistent format, such as standardizing date formats (e.g., 'MM/DD/YYYY' vs 'DD-MM-YYYY') or fixing capitalization (e.g., 'USA' vs 'usa'). This process directly addresses data quality issues that arise from human entry errors or system differences, making the dataset reliable for analysis.
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.
✓
Correcting inconsistent data
Why this is correct
Standardizing formats and fixing typos are cleaning tasks.
Related concept
Read the scenario before looking for a memorised answer.
✗
Normalization
Why it's wrong here
Normalization scales data, part of transformation, not cleaning.
✓
Handling missing values
Why this is correct
Missing values need to be imputed or removed to avoid bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
Feature engineering
Why it's wrong here
Feature engineering creates new variables, part of preprocessing, not cleaning.
✓
Removing duplicate records
Why this is correct
Duplicates can skew analysis and must be addressed.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse data cleaning with data transformation or feature engineering, leading them to select normalization or feature engineering as cleaning steps, when in fact cleaning strictly addresses data quality issues like consistency, completeness, and uniqueness.
Detailed technical explanation
How to think about this question
Data cleaning typically involves handling missing values (e.g., imputation with mean/median or deletion), removing duplicates (e.g., using DISTINCT in SQL or drop_duplicates() in pandas), and correcting inconsistencies (e.g., standardizing categorical labels like 'NY' vs 'New York'). Under the hood, these steps reduce bias and noise in the dataset, directly impacting downstream statistical analysis or machine learning model accuracy, as even a single duplicate row can skew aggregation results like averages or counts.
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 DA0-001 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Correcting inconsistent data — Correcting inconsistent data (Option A) is a core data cleaning step because it ensures that values follow a consistent format, such as standardizing date formats (e.g., 'MM/DD/YYYY' vs 'DD-MM-YYYY') or fixing capitalization (e.g., 'USA' vs 'usa'). This process directly addresses data quality issues that arise from human entry errors or system differences, making the dataset reliable for analysis.
What should I do if I get this DA0-001 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 →
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 DA0-001 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 DA0-001 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.