DP-900 Practice Question: Identify considerations for relational data on Azure
A database designer is creating a relational database for a library system. Each book can have multiple authors, and each author may have written many books. To avoid data redundancy, the designer creates a separate Authors table and a BookAuthors junction table. This process of organizing data to reduce redundancy and improve integrity is called:
⚠ Common exam trap
A common mix-up: candidates confuse normalization with denormalization, mistakenly thinking that splitting tables to reduce redundancy is a form of denormalization, when in fact it is the core definition of normalization.
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 data in a relational database to reduce redundancy and improve data integrity by dividing large tables into smaller, related tables and defining relationships between them. In this scenario, creating separate Authors and BookAuthors junction tables eliminates the redundancy of storing author information multiple times for each book, which is a classic example of normalization (specifically achieving third normal form). This directly supports the relational database goal of minimizing duplicate data and ensuring consistency.
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 a design technique that deliberately combines tables or duplicates columns to reduce the number of joins needed for read-heavy workloads, intentionally adding redundant data. This increases storage costs and introduces the risk of update anomalies and data inconsistency, which is the exact opposite of the described goal. Since the designer wants to eliminate redundancy and use a junction table for a many-to-many relationship, denormalization would not be appropriate.
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Indexing
Why it's wrong here
Indexing involves creating auxiliary data structures such as B-trees or hash indexes to accelerate query performance by enabling rapid row lookups without full table scans. It is a physical optimization that does not alter the logical schema, remove duplicated data, or introduce junction tables. While a unique index can enforce data integrity, indexes inherently add overhead and cannot address redundancy; therefore, indexing is not the correct solution for this design requirement.
- ✓
Normalization
Why this is correct
Normalization is the systematic process of structuring a relational database into separate, related tables to eliminate data redundancy, reduce update anomalies, and enforce data integrity. It progresses through normal forms—starting with 1NF for atomic values, then 2NF and 3NF for partial and transitive dependencies—which collectively guide table decomposition. For many-to-many relationships, normalization specifically calls for a junction (associative) table containing foreign keys from both parent tables, making this the correct answer for the designer's goal.
- ✗
Partitioning
Why it's wrong here
Partitioning splits a large table into smaller physical or logical segments, such as range, list, or hash partitions, to improve query performance, manageability, and data lifecycle management. It does not affect the logical schema or consolidate duplicated data across tables, so it cannot reduce redundancy. Partitioning also does nothing to represent many-to-many relationships, making it a physical storage strategy rather than a normalization technique.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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