DA0-002 Data Concepts and Environments Practice Question
A logistics company collects GPS telemetry from delivery trucks. Each reading includes a truck identifier, a timestamp, latitude, and longitude, and the fleet generates roughly 500 million readings per day. Analysts mostly run aggregate queries such as average speed per route over the past 90 days, and they rarely update individual readings. Which storage approach best fits this workload?
⚠ Common exam trap
The trap here is assuming that adding indexes to a row-oriented transactional database will make large aggregate scans efficient, when the fundamental row layout still forces reading every column.
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
✓
A columnar analytical store that compresses and scans only the columns referenced by aggregate queries
The workload is dominated by large aggregate scans over historical telemetry with few updates, which suits a columnar analytical store. Columnar storage reads only the referenced columns and compresses repetitive values, cutting I/O dramatically compared with row-oriented storage, while the rare updates do not conflict with the columnar write pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A row-oriented transactional database with secondary indexes on truck identifier and timestamp
Why it's wrong here
Row-oriented transactional databases store complete rows together, so an aggregate query must read all columns even when only speed and route are needed. At 500 million readings per day, this generates enormous I/O and index maintenance overhead. The workload is analytical rather than transactional, so this design would be inefficient and expensive.
- ✗
A key-value cache that holds the most recent reading for each truck identifier
Why it's wrong here
A key-value cache is optimized for low-latency lookups of individual keys, not for scanning and aggregating 90 days of historical telemetry. It would only hold the latest reading per truck and lacks the analytical query engine needed for average speed per route. The company needs historical aggregation, not point lookups.
- ✓
A columnar analytical store that compresses and scans only the columns referenced by aggregate queries
Why this is correct
Columnar stores keep values of each column together, so an aggregate over speed and route reads only those columns instead of entire rows. This drastically reduces I/O for the 500 million daily readings and compresses repetitive timestamp and identifier values well. Since individual readings are rarely updated, the write pattern is a good match for a columnar analytical workload.
- ✗
A graph database that models trucks, routes, and readings as nodes and relationships
Why it's wrong here
Graph databases excel at traversing relationships between entities, such as finding shortest paths or fraud rings. This scenario needs large-scale aggregation over numeric telemetry, not relationship traversal. Storing 500 million readings as nodes would add unnecessary complexity and would not provide the columnar scan and compression benefits that aggregate queries require.
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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
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.