DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is integrating data from two source systems into a single customer dataset. Source A uses a customer ID format like 'CUST-12345', while Source B uses '12345'. Additionally, Source A records dates in 'MM/DD/YYYY' format, while Source B uses 'YYYY-MM-DD'. Which two data preparation tasks are essential to ensure the integrated dataset is consistent and usable? (Choose two.)
⚠ Common exam trap
The trap here is focusing on data security or missing values instead of the format harmonization needed for integration.
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
✓
Convert all date values to a single standardized format.
The key challenges are inconsistent customer ID formats and date formats across sources. Standardizing both ensures records can be matched and time-based analysis is accurate. Encryption, missing value removal, and aggregation do not directly solve these format inconsistencies and may introduce other issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply encryption to all customer ID fields.
Why it's wrong here
Encryption is a security measure to protect sensitive data, but it does not resolve format inconsistencies. Encrypting IDs would actually hinder matching because encrypted values differ. The scenario focuses on integration consistency, not security, so encryption is not an essential data preparation task here.
- ✓
Convert all date values to a single standardized format.
Why this is correct
Converting dates to a uniform format ensures temporal comparisons and calculations are valid. Different date formats can lead to misinterpretation, sorting errors, and failed date functions. Standardizing dates is essential for consistent time-based analysis and reporting across the integrated dataset.
- ✓
Standardize the customer ID format across both sources.
Why this is correct
Standardizing customer IDs ensures that records from both sources can be accurately matched and deduplicated. Without a common format, joins or merges would fail or produce incorrect matches. This is a fundamental step in data integration to achieve consistency and enable reliable analysis across the combined dataset.
- ✗
Remove all records with missing values.
Why it's wrong here
Removing records with missing values may reduce data volume and introduce bias. While handling missing data is important, it is not specifically required to resolve format inconsistencies between the two sources. The core issues are ID and date standardization, not missing data.
- ✗
Aggregate the data by customer to reduce row count.
Why it's wrong here
Aggregation summarizes data and loses granularity, which is not necessary for resolving format differences. The goal is to integrate and standardize, not to summarize. Aggregating prematurely could obscure details needed for analysis and does not address the format inconsistencies.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.