DA0-002 Data Concepts and Environments Practice Question
A data architect is evaluating storage engines for a new analytics platform. The platform must support storing large volumes of structured historical sales data and must efficiently handle queries that aggregate a few columns across billions of rows. The architect is considering column-oriented storage. Which two characteristics accurately describe column-oriented storage in this context? (Choose two.)
⚠ Common exam trap
The trap here is assuming that column-oriented storage improves all workloads equally, when its advantages apply to analytical scans and compression while row-oriented engines remain superior for frequent single-row writes.
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
✓
It typically achieves better data compression than row-oriented storage for repetitive column values
Column-oriented storage groups values by column, which enables efficient compression and allows queries to read only the columns they need. These two properties directly support aggregating a few columns across billions of rows. The remaining statements describe row-oriented behavior, overstate write performance, or misrepresent indexing requirements, so they do not accurately characterize column-oriented storage for this analytics platform.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It requires reading all columns of a row even when only one column is needed for a query
Why it's wrong here
Column-oriented storage does the opposite: it reads only the columns referenced by a query, which is a primary advantage for analytical workloads. Reading all columns would negate the performance benefit. This statement describes a limitation of row-oriented storage rather than a characteristic of column-oriented storage.
- ✓
It typically achieves better data compression than row-oriented storage for repetitive column values
Why this is correct
Because values within a column share the same data type and often repeat or fall within narrow ranges, column-oriented storage can apply dictionary, run-length, and delta encoding effectively. This yields higher compression than row-oriented formats, where heterogeneous row values limit encoding options. Reduced storage and I/O directly benefit large-scale analytical scans.
- ✗
It is optimized for high-frequency single-row inserts and updates typical of transactional workloads
Why it's wrong here
Column-oriented storage is not optimized for frequent single-row writes because inserting or updating a row touches many separate column segments, causing write amplification. Transactional workloads with high-frequency row-level modifications are better served by row-oriented engines. This characteristic contradicts the analytical use case described.
- ✗
It eliminates the need for indexing because every column is stored in sorted order automatically
Why it's wrong here
Column-oriented storage does not automatically sort every column, and indexes or zone maps are still useful for filtering and point lookups. While min-max metadata can skip data blocks, it does not replace indexing strategies. Claiming that indexing becomes unnecessary overstates the technology and misrepresents how column stores operate.
- ✓
It stores each column's values contiguously, improving compression and scan efficiency for analytical aggregates
Why this is correct
Column-oriented storage physically groups values of the same column together, which enables high compression ratios because values share type and often similar ranges. Scanning a few columns across billions of rows reads only the relevant column data, avoiding full-row I/O. This directly supports efficient aggregation queries over historical sales data, making this characteristic accurate.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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