The answer is to start by cleaning the data and performing exploratory data analysis. This is correct because data cleaning and exploratory data analysis are the foundational first steps in any analytical workflow; attempting to build a predictive model or analyze time series trends with incorrect driver IDs and 10% missing weather data would introduce significant bias and invalidate your conclusions. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding that data quality must be addressed before any modeling or statistical testing, and a common trap is jumping straight to advanced techniques like regression or time series decomposition without first ensuring the dataset is reliable. A strong memory tip is “Clean before you lean”—you cannot lean on your analysis until the data is clean and you have explored its patterns through EDA.
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.
You are a data analyst at a logistics company. The operations manager wants to reduce delivery delays. You have historical data including order date, delivery date, distance, weather conditions, and driver ID. Initial analysis shows that the average delivery time has increased over the past six months. You suspect that weather is a contributing factor, but you need to confirm. The company also wants to build a model to predict delivery times to better manage customer expectations. The data contains missing values for weather conditions in about 10% of records, and some driver IDs are incorrect. You have limited time and resources. What should you do 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis
Option B is correct because data cleaning and exploratory data analysis (EDA) are foundational steps before any modeling or time series work. With missing weather data (10%) and incorrect driver IDs, proceeding without cleaning would introduce bias and errors. EDA will reveal patterns, correlations, and data quality issues, enabling informed decisions on imputation and feature engineering for the predictive model.
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.
✗
Immediately focus on time series analysis to look for patterns
Why it's wrong here
Without cleaning, time series may be based on flawed data.
✓
Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis
Why this is correct
Cleaning ensures data integrity, and EDA guides modeling choices.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Collect more data to fill missing values
Why it's wrong here
Collecting more data is time-consuming and may not address existing quality issues.
✗
Build a predictive model using all available data after imputing missing weather data
Why it's wrong here
Imputing without investigating data quality first may introduce bias; driver ID errors also need fixing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that you can jump directly to modeling or advanced analysis without first ensuring data quality, ignoring the 'garbage in, garbage out' principle.
Detailed technical explanation
How to think about this question
In practice, data cleaning involves verifying driver IDs against a master reference table (e.g., using foreign key constraints) and handling missing weather data via methods like multiple imputation or using a separate 'missing' category for categorical variables. EDA includes univariate analysis (distributions, outliers) and bivariate analysis (correlation matrices, scatter plots) to assess relationships like distance vs. delivery time, which guides feature selection for the predictive model.
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.
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: Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis — Option B is correct because data cleaning and exploratory data analysis (EDA) are foundational steps before any modeling or time series work. With missing weather data (10%) and incorrect driver IDs, proceeding without cleaning would introduce bias and errors. EDA will reveal patterns, correlations, and data quality issues, enabling informed decisions on imputation and feature engineering for the predictive model.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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