DA0-002 Data Concepts and Environments Practice Question
A data architect is designing a system to store customer support tickets. The tickets are written in free-form text and include attachments such as screenshots and PDFs. The system must allow support agents to search for tickets by keywords within the text and attachments. The architect expects the volume of tickets to grow to millions and requires fast, full-text search capabilities. Which storage solution is most appropriate?
⚠ Common exam trap
The trap here is assuming that any database with indexing can handle full-text search, but only systems with an inverted index provide the tokenization and ranking required for efficient keyword queries.
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
✓
Document store with inverted index
A document store with an inverted index is purpose-built for full-text search, enabling fast keyword queries across millions of text documents and attachments. It supports text extraction from various file types and scales horizontally. Relational, key-value, and graph databases lack the specialized indexing and search features needed for efficient full-text retrieval at this scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Relational database with BLOB columns
Why it's wrong here
Relational databases can store text and binary large objects (BLOBs), but full-text search across BLOBs and attachments is not natively efficient. While some relational databases offer full-text indexing, it is often limited compared to dedicated search engines. Scaling to millions of tickets with keyword search across varied file types would require significant custom development and may not meet performance requirements.
- ✓
Document store with inverted index
Why this is correct
A document store with an inverted index, such as Elasticsearch, is optimized for full-text search across large volumes of text and metadata. It can index the content of tickets and extract text from attachments (via ingest pipelines) to enable keyword search. This architecture scales horizontally and provides fast, relevant search results, directly addressing the requirements.
- ✗
Graph database with full-text search plugin
Why it's wrong here
Graph databases are optimized for traversing relationships between entities, not for full-text search across large text corpora. While some graph databases offer full-text search plugins, their indexing capabilities are generally less mature and scalable than those of dedicated search engines. The primary strength of a graph database—relationship analysis—is not required for this keyword search scenario.
- ✗
Key-value store with secondary indexes
Why it's wrong here
Key-value stores are designed for rapid lookups by primary key, not for full-text search. Secondary indexes can support simple attribute queries, but they do not provide the tokenization, stemming, and relevance ranking needed for keyword search within free-form text and attachments. Implementing such features would be complex and inefficient compared to a dedicated search solution.
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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.