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 Stream Analytics

  Why it's wrong here

  Azure Stream Analytics excels at real-time stream processing with continuous, windowed queries defined in a SQL-like language, but those jobs are long-running and operate on live input, not on ad-hoc interactive T-SQL over stored historical data. You cannot pause and run a quick external-table query with Stream Analytics; it lacks a general-purpose T-SQL engine for raw-data exploration. Therefore, it meets the streaming half of the requirement but fails the explicit ad-hoc T-SQL half.

  When this WOULD be correct

  If the question only required real-time anomaly detection on streaming data without the need for ad-hoc T-SQL queries on combined datasets, and the output could be directed to a separate storage for analysis, then Azure Stream Analytics would be the correct choice.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks can indeed process continuous sensor streams through Structured Streaming and allows interactive notebooks, but its query language is Spark SQL, not T-SQL. The question explicitly requires ad-hoc T-SQL, and although Databricks has a SQL warehouse for BI-style queries, it does not natively support T-SQL dialect or syntax. Moreover, its streaming ingestion requires cluster lifecycle management, making it less direct than Synapse's unified serverless experience.

  When this WOULD be correct

  Azure Databricks would be correct if the question required advanced machine learning model training on the combined dataset, or if the team needed to perform complex data transformations using Spark and Python/Scala, and T-SQL was not a requirement.

• ✓

  Azure Synapse Analytics

  Why this is correct

  Azure Synapse Analytics is correct because it unifies continuous data ingestion—via pipelines, Event Hubs, or streaming sources—with a full T-SQL query engine. You can create external tables over raw sensor data and run ad-hoc queries with dedicated SQL pools or the serverless SQL endpoint. This directly satisfies both the real-time ingestion and the requirement for ad-hoc T-SQL analysis, unlike the other services.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is a semantic modeling and reporting layer, not a stream-processing or raw-data query engine. It consumes already transformed data from sources like Azure Synapse or SQL Server and exposes it to clients via DAX, MDX, or Power BI, but it cannot directly ingest a continuous sensor stream or answer ad-hoc T-SQL queries on the landing data. Thus, while it may sit on top of the correct solution, it cannot perform the required ingestion or raw querying itself.

  When this WOULD be correct

  A question where the requirement is to create a tabular or multidimensional semantic model for business users to perform interactive analysis (e.g., with Excel or Power BI) on pre-aggregated data from a data warehouse, without needing real-time streaming or direct T-SQL access.

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?”

Go deeper

Related to this question