DA0-002 Data Analysis Practice Question
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?
⚠ Common exam trap
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.
Answer choices
Why each option matters
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
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.
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
Time series analysis examines temporal patterns in a single ordered sequence, so it cannot confirm whether weather drives the delay increase or support prediction from distance and driver. It is tempting because delivery dates form a series, but the stated goal needs regression after cleaning the weather and driver 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 the incorrect driver IDs and deciding a treatment for the 10% missing weather values must precede any analysis, because exploratory data analysis and the predictive model both depend on accurate, complete inputs; with limited time, this directly satisfies the stem's constraint of confirming weather's role reliably.
- ✗
Collect more data to fill missing values
Why it's wrong here
Collecting more data addresses the 10% missing weather values but leaves the incorrect driver IDs unresolved and delays any finding, which conflicts with the limited time and resources stated. It is tempting because missing values weaken models, yet the first step is assessing and cleaning the existing data.
- ✗
Build a predictive model using all available data after imputing missing weather data
Why it's wrong here
Imputing weather and modelling immediately skips diagnosis of the incorrect driver IDs, so the model trains on corrupted records and cannot confirm weather's contribution. It is tempting because it appears to deliver the requested prediction quickly, but data quality assessment and cleaning must precede modelling.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 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 DA0-002 exam.