DP-900 Practice Question: Identify considerations for relational data on Azure
A database designer wants to reduce data redundancy and improve data integrity by splitting a large table into multiple related tables based on functional dependencies. This process is known as:
⚠ Common exam trap
A common mix-up: candidates confuse normalization with partitioning, because both involve splitting tables, but partitioning is a physical storage optimization, not a logical design technique for reducing redundancy.
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
✓
Normalization
Normalization is the process of organizing a relational database into multiple related tables to reduce data redundancy and improve data integrity by eliminating functional dependencies that cause anomalies. This is a core concept in relational database design, directly aligning with the scenario described in the question.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Denormalization
Why it's wrong here
Denormalization is an intentional design technique that combines related tables or adds redundant columns to minimize costly JOIN operations, often to boost read performance in analytical systems. This process deliberately increases data redundancy rather than reducing it, which directly contradicts the stated goal of the database designer.
- ✓
Normalization
Why this is correct
Normalization is the formal process of decomposing large tables into smaller, related tables based on functional dependencies, with the goal of eliminating duplicate data and ensuring each fact is stored only once. By applying normal forms such as 1NF, 2NF, and 3NF, the designer removes update anomalies and ensures consistency, directly satisfying the requirement to reduce data redundancy.
- ✗
Partitioning
Why it's wrong here
Partitioning splits a table into smaller physical segments (e.g., by range, list, or hash) to improve manageability, backup granularity, and query performance via partition pruning. However, the logical rows and columns remain identical across partitions, so redundancy and dependency issues persist; partitioning only affects data storage and access, not the schema's inherent duplication.
- ✗
Indexing
Why it's wrong here
Indexing creates auxiliary lookup structures (e.g., B-trees) that accelerate query execution by reducing the number of disk pages read, but it does not change the logical schema or the data values stored. Redundancy and data integrity are functions of table design and dependencies, not of access-path optimization, so indexing cannot reduce duplicate data.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Table
A table is a structured collection of data organized into rows and columns, used in databases and spreadsheets to store and manage information efficiently.
Key term
Relational database
A relational database organizes data into tables with rows and columns, where each table relates to others using unique keys, allowing efficient storage, retrieval, and manipulation of structured information.
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