DP-900 Describe core data concepts Practice Question
A financial services firm runs a securities trading platform. Trades must be recorded atomically, with either all parts of a trade committing or none, and the committed records must survive a database node failure. The firm also needs enforcement of referential integrity between trade and account tables. Which data store characteristic best matches these requirements?
⚠ Common exam trap
The trap here is focusing on the word durable in isolation and matching it to any highly available store, when the full requirement set is atomic multi-part commitment plus referential integrity.
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
✓
Relational storage with ACID transactions and referential integrity constraints
The requirements describe ACID transaction guarantees plus referential integrity, which are core capabilities of relational storage such as Azure SQL Database. Eventual consistency, key-value stores, and columnar analytical engines each optimize for other priorities, such as availability, fast key lookups, or scan performance, and none provides the atomic, integrity-enforced writes the trading platform needs.
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 storage with ACID transactions and referential integrity constraints
Why this is correct
Atomicity, consistency, isolation, and durability, collectively ACID, guarantee that a trade either commits fully or not at all and survives node failure. Foreign key constraints enforce the referential integrity between trade and account tables. This combination is the defining strength of relational engines such as Azure SQL Database, which is why it matches the firm's requirements.
- ✗
Key-value storage with tunable consistency and no schema enforcement
Why it's wrong here
Key-value stores optimize fast lookups by key and generally do not enforce relationships between entities or guarantee multi-record atomicity the way relational engines do. Without schema enforcement, nothing prevents a trade from referencing a nonexistent account. This model fits caching and session data, not the strict transactional integrity the securities platform demands.
- ✗
Columnar analytical storage optimized for large scans and aggregations
Why it's wrong here
Columnar analytical stores are built for scanning and aggregating massive datasets, often with batch-oriented ingestion, and typically do not provide the row-level transactional guarantees or foreign key enforcement needed here. They are the right choice for reporting over historical trades, but not for the atomic, integrity-checked recording of each trade as it executes.
- ✗
Schema-on-read storage with eventual consistency across replicas
Why it's wrong here
Schema-on-read and eventual consistency suit flexible, distributed workloads where temporary divergence between replicas is acceptable. The trading platform requires that committed trades be immediately and durably consistent, and it needs declared relationships between tables. Eventual consistency could expose stale or partial views of trades, which is unacceptable for financial records, so this characteristic does not match.
Go deeper
Related to this question
Learn chapter
Relational Data Concepts
Key term
Azure SQL Database
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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