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 continuous real-time processing using a SQL-like query language over live event streams, but its query scope is bounded to streaming input and side-by-side reference data. It lacks an engine for interactive ad-hoc T-SQL against historical files in the data lake, so a user who needs to combine live sensor outputs with months of batch data in a single T-SQL query would have to stage data elsewhere. Thus, for this scenario's simultaneous streaming and on-demand analytical requirements, it falls short of Azure Synapse Analytics.

  When this WOULD be correct

  A question where the only requirement is real-time processing of streaming data (e.g., alerting on temperature thresholds) without any need for ad-hoc T-SQL queries or combining with historical batch data. For example: 'Which service should be used to filter and aggregate IoT sensor data in real-time before sending to a dashboard?'

• ✓

  Azure Synapse Analytics

  Why this is correct

  Azure Synapse Analytics unifies big data and data warehousing. It can ingest streaming data via Event Hubs, query both streaming and batch data using T-SQL across the data lake, and support dashboarding, making it the best fit for both real-time and ad-hoc requirements.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks is excellent for data engineering and machine learning with Python, Scala, and SQL. However, it does not provide native T-SQL support and is less suited for users who need a traditional T-SQL query experience.

  When this WOULD be correct

  A data science team needs to build a machine learning model on historical sensor data stored in Data Lake Storage, and then deploy the model for real-time predictions on streaming data from Event Hubs. Azure Databricks would be the correct choice because it provides collaborative notebooks, Spark-based processing, and MLflow for model management.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is a semantic modeling and BI engine that exposes pre-built tabular models via MDX/DAX, not a data storage or query engine for raw data. It cannot ingest real-time sensor events directly, nor does it support ad-hoc T-SQL against data lake files; all data must first be processed and loaded into a model designed by a developer. This makes it unsuitable as the primary service for streaming ingestion and flexible historical querying.

  When this WOULD be correct

  A question where the requirement is to create a semantic model for interactive reporting and analysis on pre-aggregated, historical data from a data warehouse, with no need for real-time streaming or direct T-SQL queries. For example: 'A company needs to build a tabular model for Power BI reports from an existing Azure SQL Data Warehouse.'

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?”

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