DP-900 Describe an analytics workload on Azure Practice 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.

• ✗

  Azure SQL Database

  Why it's wrong here

  Azure SQL Database is a fully managed relational database engine designed for transactional (OLTP) workloads, with strict schemas and row-based storage. It cannot store raw files of varying formats as needed in a lakehouse, nor does it support open file formats like Parquet or Delta for analytics at data lake scale. Using it as the primary storage layer would force you to model all data as tables, defeating the flexibility and cost-effectiveness of a lakehouse.

  When this WOULD be correct

  A company needs to migrate an on-premises SQL Server database to Azure with minimal changes, requiring a fully managed relational database that supports existing T-SQL queries, stored procedures, and high availability. The correct answer would be Azure SQL Database.

• ✗

  Azure Synapse Analytics

  Why it's wrong here

  Azure Synapse Analytics is a unified analytics service that provides serverless and dedicated SQL pools, data pipelines, and Spark-optimized compute for querying data in place. In a lakehouse architecture, Synapse acts as the compute and orchestration layer, but it reads and writes data stored in Azure Data Lake Storage Gen2 rather than serving as the storage medium itself. Selecting Synapse as the storage layer conflates the analytics engine with the underlying file repository, which is ADLS Gen2.

  When this WOULD be correct

  A company wants to run large-scale analytics and data warehousing workloads with T-SQL queries on structured and semi-structured data, and needs integrated data integration, big data analytics, and BI capabilities. In that case, Azure Synapse Analytics would be the correct answer as the analytics platform.

• ✓

  Azure Data Lake Storage Gen2

  Why this is correct

  Azure Data Lake Storage Gen2 combines a hierarchical namespace with scalable object storage, enabling it to hold both raw ingest and curated, analytics-ready data in any format (Parquet, Delta, CSV, etc.). This dual role as a unified storage layer makes it the foundational component of a lakehouse architecture, where the same files can serve BI, data science, and machine learning workloads. Its integration with Azure Synapse, Databricks, and Power BI further cements it as the correct answer for building a modern data warehouse on a lakehouse.

• ✗

  Azure Cosmos DB

  Why it's wrong here

  Azure Cosmos DB is a multi-model NoSQL database designed for globally distributed, low-latency transactional and IoT-driven workloads, with data stored in JSON-like documents and key-value pairs. It lacks a filesystem interface, hierarchical namespace, and the ability to store raw or curated analytical files in open formats such as Parquet or Delta. A lakehouse requires a data lake that can persist and query petabyte-scale files; Cosmos DB's query patterns and storage model are optimized for operational, not analytical, data.

  When this WOULD be correct

  A question asking for a globally distributed, multi-model database service to handle high-throughput, low-latency transactions for a real-time application (e.g., IoT telemetry, e-commerce cart) where schema flexibility and horizontal scaling are critical.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”