DA0-002 Data Concepts and Environments Practice Question
A mid-sized e-commerce company stores customer data in a relational database. The database has a table named 'Customers' with columns: CustomerID (primary key), FirstName, LastName, Email, Phone, Address, City, State, ZipCode, and SignUpDate. The company is migrating to a new CRM system that requires a denormalized structure for performance reasons. The new system expects a single table 'CustomerDetails' with columns: CustomerID, FullName (concatenation of first and last name), ContactInfo (JSON object containing email, phone, and address), SignUpDate, and Region (derived from state). The data analyst must design an ETL process to transform the data. During a test run, the analyst notices that some records have missing Phone or Address values. Which of the following is the best approach to handle missing data in the ContactInfo JSON object?
⚠ Common exam trap
A common mix-up: candidates confuse 'handling missing data' with 'filling in missing data,' leading them to choose placeholder strings (B or D) instead of preserving the null representation that JSON natively supports.
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
✓
Include the missing fields as null in the JSON object.
Representing missing fields as null in the JSON object preserves the data structure and allows downstream systems to explicitly handle null values. This approach maintains data integrity without discarding records or introducing ambiguous placeholder strings that could be misinterpreted as actual data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Exclude any record with missing Phone or Address from the migration.
Why it's wrong here
Excluding records with missing Phone or Address discards otherwise valid customers, breaking the migration's completeness requirement; the JSON object can hold absent fields without dropping the row. It is tempting because exclusion is a legitimate technique when a mandatory column cannot be null, such as a NOT NULL foreign key.
- ✗
Set missing values to an empty string in the JSON object.
Why it's wrong here
An empty string is a valid JSON value, so downstream systems cannot distinguish 'no phone supplied' from a genuinely blank entry, corrupting analytics and contact logic. It is tempting because empty-string substitution is standard when a target column is NOT NULL and no null representation is permitted.
- ✓
Include the missing fields as null in the JSON object.
Why this is correct
Retaining missing fields as explicit nulls preserves the JSON schema and key structure, so downstream consumers can distinguish absent values from omitted keys. This satisfies the denormalised ContactInfo requirement without fabricating data or dropping otherwise valid customer records.
- ✗
Replace missing values with 'N/A' string.
Why it's wrong here
Substituting 'N/A' strings fabricates contact data and breaks JSON consumers expecting null or absent keys. It is tempting as a quick way to keep the JSON schema uniform, but null (or omitting the key) preserves the distinction between missing and present values for downstream CRM validation.
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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.