DA0-002 Data Acquisition and Preparation Practice Question
A data team is ingesting JSON event data from a mobile application into a columnar warehouse. Each event has a nested array of product objects, and analysts frequently need to report on individual products within those events. The team wants to avoid repeated manual parsing in every query. Which approach best prepares the data for efficient product-level analysis?
⚠ Common exam trap
The trap here is assuming modern warehouses make nested JSON queries so convenient that no load-time transformation is needed, when repeated parsing still harms performance and consistency.
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
✓
Flatten the nested product array into a separate table with one row per product and a foreign key back to the event, during the load process.
The requirement is reusable, efficient product-level analysis from nested JSON. Shredding the array into a child table during load normalizes the structure, lets the columnar warehouse store each product as a row, and removes repeated parsing from reports. Storing raw JSON, querying nested arrays each time, or converting to positional text all defer or damage the preparation work.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the entire event to a single delimited text field and split it into columns by position.
Why it's wrong here
Flattening nested JSON into positional delimited text loses the explicit relationships between events and products and breaks whenever the array length or field order changes. It also discards type information. This approach creates brittle parsing and does not provide a reliable product-level structure for analysis.
- ✗
Keep the nested array as-is and rely on the warehouse's native semi-structured query functions for every report.
Why it's wrong here
Native semi-structured functions can read nested arrays, but using them in every report duplicates effort and may prevent efficient columnar pruning of individual products. It does not create a stable, reusable product-level structure. The team explicitly wants to avoid repeated manual parsing, so leaving the array nested fails the stated goal.
- ✓
Flatten the nested product array into a separate table with one row per product and a foreign key back to the event, during the load process.
Why this is correct
Shredding the nested array into a child table normalizes the data so each product becomes a queryable row, which columnar warehouses handle efficiently. Analysts can then join or aggregate without repeating parsing logic. This approach also supports indexing and consistent semantics, directly meeting the requirement for efficient product-level reporting.
- ✗
Store the raw JSON as a single string column and parse it at query time using string functions.
Why it's wrong here
Parsing JSON strings at query time forces every report to repeat fragile string logic, prevents the warehouse from optimizing access to nested fields, and is slow on large datasets. It also makes schema changes break many queries at once. This pushes preparation work onto analysts instead of resolving it during the load.
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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
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