Quick Answer
The answer is inconsistent data in total_charges, because the exhibit reveals mixed numeric formats like '1,234.56' and '1234.56' within the same column, which is a classic data quality issue that directly undermines logistic regression model training. Logistic regression algorithms require all feature values to be strictly numeric and uniformly typed; inconsistent data types such as these will cause parsing errors in Python’s scikit-learn or produce incorrect coefficient estimates. On the CompTIA Data+ DA0-001 exam, this scenario tests your ability to spot format inconsistencies before addressing missing values or outliers, as the exam often presents a table with mixed delimiters to distract you. A common trap is to focus on missing data first, but here the immediate blocker is the non-uniform numeric representation. Remember the memory tip: “Clean the comma before you code the model”—standardize all numeric strings to a consistent float type to avoid silent failures.
This DA0-001 practice question tests your understanding of analyzing and modeling data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Duplicate customer IDs
Why wrong: All IDs appear unique.
Missing values in total_charges
Why wrong: No missing values are present.
Inconsistent data in total_charges
The total_charges for the first customer is equal to monthly_charges, suggesting a calculation error.
Outliers in monthly_charges
Why wrong: Values like 99.65 are not extreme outliers.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Inconsistent data in total_charges
Option C is correct because the exhibit shows that the 'total_charges' column contains entries like '1,234.56' and '1234.56', which are inconsistent numeric formats. Logistic regression in Python (e.g., using scikit-learn) requires all feature values to be numeric and consistent; mixed formats will cause parsing errors or incorrect model training. The analyst must standardize these values to a uniform numeric type (e.g., float) before proceeding.
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.
Duplicate customer IDs
Why it's wrong here
All IDs appear unique.
Missing values in total_charges
Why it's wrong here
No missing values are present.
Inconsistent data in total_charges
Why this is correct
The total_charges for the first customer is equal to monthly_charges, suggesting a calculation error.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Outliers in monthly_charges
Why it's wrong here
Values like 99.65 are not extreme outliers.
Common exam traps
CompTIA often tests the distinction between data quality issues that prevent model execution (like inconsistent data types) versus issues that degrade model performance (like outliers or missing values), and candidates frequently overlook the former because they focus on statistical concerns rather than data preprocessing fundamentals.
Detailed technical explanation
Logistic regression models in scikit-learn's LogisticRegression class expect numeric input arrays (e.g., numpy float64). Inconsistent string formats like '1,234.56' vs '1234.56' will cause a ValueError when converting to float, halting model training. In real-world scenarios, such formatting issues often arise from data exported from different regional systems (e.g., US vs European number formats), and must be normalized using string replacement or locale-aware parsing before feature engineering.
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 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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Inconsistent data in total_charges — Option C is correct because the exhibit shows that the 'total_charges' column contains entries like '1,234.56' and '1234.56', which are inconsistent numeric formats. Logistic regression in Python (e.g., using scikit-learn) requires all feature values to be numeric and consistent; mixed formats will cause parsing errors or incorrect model training. The analyst must standardize these values to a uniform numeric type (e.g., float) before proceeding.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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