DA0-002 Data Concepts and Environments Practice Question
A financial analytics team is building a data warehouse to support complex analytical queries on historical stock trades. The data volume is in terabytes, and queries frequently join multiple large tables and perform aggregations. The team needs a storage model that minimizes query latency for these read-heavy analytical workloads. Which data modeling approach is most appropriate?
⚠ Common exam trap
The trap here is assuming that a normalized schema is always best for data integrity, overlooking that analytical workloads require denormalized dimensional models for performance.
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
✓
Dimensional star schema
A dimensional star schema is designed for analytical workloads, using fact and dimension tables to minimize joins and optimize aggregations. It supports columnar storage and partitioning, which are critical for terabyte-scale read-heavy queries. Normalized OLTP, EAV, and flat files each introduce performance bottlenecks or lack the necessary optimizations for complex analytical queries on historical stock trades.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Highly normalized OLTP schema
Why it's wrong here
A highly normalized OLTP schema reduces redundancy and is optimized for transaction processing with frequent inserts and updates. However, it requires many joins to answer analytical queries, increasing latency and complexity for read-heavy aggregations. For terabyte-scale historical stock trades, this approach would lead to slow query performance and is not designed for the analytical workload described.
- ✓
Dimensional star schema
Why this is correct
A dimensional star schema organizes data into fact tables (e.g., trades) and denormalized dimension tables (e.g., date, stock, broker). This design reduces the number of joins, enables efficient aggregations, and is optimized for read-heavy analytical queries. It also supports columnar storage and partitioning, making it ideal for terabyte-scale historical trade analysis with minimal query latency.
- ✗
Entity-attribute-value (EAV) model
Why it's wrong here
EAV models store attributes as rows, which provides flexibility for sparse or evolving schemas but requires complex self-joins and pivoting to reconstruct records. This leads to poor query performance for aggregations and joins across large tables. For a read-heavy analytical workload on stock trades, EAV would introduce significant overhead and is not suited for terabyte-scale data warehousing.
- ✗
Flat file with no indexing
Why it's wrong here
A flat file without indexing lacks the structure and optimization needed for complex joins and aggregations. Scanning terabytes of data for each query would result in unacceptable latency. While flat files are simple for storage, they do not support efficient analytical processing, concurrency, or the query patterns required by the financial analytics team.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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