Azure Data Fundamentals (DP-900) · 29% practice allocation

Azure DP-900: Describe core data concepts Practice Questions

5 free sample questions from the Describe core data concepts domain, with answers and explanations. The full DP-900 bank has 62 questions across 4 domains. Questions are scenario-based and written from the official Azure exam guide.

1.

Which Azure service is the MOST appropriate default choice for storing large amounts of unstructured objects — images, videos, backups, and log files?

  • A. Azure SQL Database
  • B. Azure Database for PostgreSQL
  • C. Azure Cosmos DB for NoSQL
  • D. Azure Blob Storage✓ Correct
Explanation

Blob storage is Azure's object store, built for massive volumes of unstructured files at low cost with tiering options. Azure SQL (A) and PostgreSQL (B) are relational engines — the wrong shape and cost profile for media files. Cosmos DB (C) stores JSON items with per-item size limits; it's for structured/semi-structured items, not media objects.

2.

A dataset consists of customer records where every record has exactly the same fields — ID, name, email, and signup date — stored in rows and columns with a fixed schema. How is this data BEST classified?

  • A. Unstructured data
  • B. Structured data✓ Correct
  • C. Semi-structured data
  • D. Streaming data
Explanation

Structured data adheres to a fixed schema where every entity has the same fields, naturally represented as tables of rows and columns. Semi-structured data (C) has organization (like JSON) but allows records to vary in shape. Unstructured data (A) has no enforced structure — documents, images, audio. Streaming (D) describes how data arrives, not its structure.

3.

Which TWO characteristics are typical of ANALYTICAL data workloads? (Select 2)

  • A. Thousands of small concurrent writes with row-level locking
  • B. Read-optimized storage, often denormalized or columnar✓ Correct
  • C. Single-record lookups as the dominant query pattern
  • D. Strict per-transaction atomicity as the primary design goal
  • E. Large volumes of historical data scanned and aggregated✓ Correct
Explanation

Analytical workloads aggregate large historical datasets (E) and favor read-optimized structures — denormalized models and columnar formats — for fast scans (B). Options A, C, and D describe transactional systems: many small writes, atomicity-first design, and key-based single-record access.

4.

Match the need to the correct Azure data store: the team wants a managed relational engine with near-100% SQL Server compatibility for a legacy app that uses cross-database queries and SQL Agent jobs, with minimal administration.

  • A. Azure Blob Storage
  • B. Azure Table Storage
  • C. Azure SQL Database (single database)
  • D. Azure SQL Managed Instance✓ Correct
Explanation

SQL Managed Instance offers near-complete SQL Server surface compatibility — including cross-database queries and SQL Agent — as a managed PaaS service, exactly for legacy migrations. Single-database Azure SQL (C) lacks some instance-scoped features like SQL Agent. Table Storage (B) is a NoSQL key-value store and Blob (A) an object store — neither is relational.

5.

A startup wants one database service that can serve JSON documents to a global user base with single-digit-millisecond latency and elastic scale, using an API model its MongoDB-trained developers already know. Which service should it evaluate?

  • A. Azure Database for MariaDB
  • B. Azure Cosmos DB✓ Correct
  • C. Azure SQL Managed Instance
  • D. Azure Files
Explanation

Global distribution, guaranteed low latency, elastic throughput, JSON documents, and a MongoDB-compatible API are Cosmos DB's signature combination. SQL MI (C) and MariaDB (A) are relational engines without that global/document model. Azure Files (D) serves SMB/NFS file shares — not a database at all.

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