DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company deploys IoT sensors on equipment in a factory. They need to monitor sensor data in real time to detect anomalies and trigger immediate alerts. They also need to store years of historical sensor data for monthly capacity planning reports that involve complex aggregations. The company wants a cost-effective solution that minimizes data movement between storage and compute. Which combination of Azure services should they use for real-time processing and historical batch analytics?
⚠ Common exam trap
A common mix-up: candidates confuse data ingestion services (like Event Hubs) with real-time processing engines (like Stream Analytics), or they pick a database like Cosmos DB or SQL Database for historical storage without considering cost and aggregation performance at scale.
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
✓
A. Azure Stream Analytics for real-time processing, Azure Data Lake Storage Gen2 for historical storage, and Azure Synapse Analytics for batch queries.
Azure Stream Analytics is purpose-built for real-time processing of streaming data from IoT sensors, enabling immediate anomaly detection and alerting. Azure Data Lake Storage Gen2 provides cost-effective, scalable storage for years of historical sensor data, while Azure Synapse Analytics (formerly SQL Data Warehouse) can run complex aggregations directly against that data without moving it, minimizing data movement and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A. Azure Stream Analytics for real-time processing, Azure Data Lake Storage Gen2 for historical storage, and Azure Synapse Analytics for batch queries.
Why this is correct
A is correct because it forms a complete, scalable IoT analytics pipeline. Azure Stream Analytics is purpose-built for real-time stream processing with built-in windowing functions and SQL-like syntax, making it ideal for live insight on sensor data. Azure Data Lake Storage Gen2 provides a hierarchical, cost-effective storage layer for massive volumes of raw and transformed IoT telemetry, supporting Parquet and Delta formats for efficient downstream querying. Azure Synapse Analytics can query that lake directly using serverless SQL or dedicated pools, enabling complex batch analytics without forcing data movement or duplication.
- ✗
B. Azure Data Factory for real-time processing, Azure Cosmos DB for historical storage, and Power BI for batch queries.
Why it's wrong here
Azure Data Factory is an orchestration tool, not a real-time stream processor. Cosmos DB is a transactional database and not cost-effective for large-scale historical storage. Power BI is a visualization tool, not a batch query engine.
When this WOULD be correct
A company needs to ingest data from multiple sources, transform it using a visual interface, and store it in a globally distributed, low-latency database for real-time dashboards. They also need to visualize the data in Power BI. In this scenario, Azure Data Factory for orchestration, Cosmos DB for operational storage, and Power BI for reporting would be appropriate.
- ✗
C. Azure Functions for real-time processing, Azure Table Storage for historical storage, and Azure Analysis Services for batch queries.
Why it's wrong here
Azure Functions can handle stream processing for low-throughput scenarios but lacks built-in support for windowed aggregations and exactly-once semantics common in industrial streaming. Azure Table Storage is a key-value store not suited for complex analytical queries. Azure Analysis Services requires data to be loaded into a model, incurring additional data movement.
- ✗
D. Azure Event Hubs for real-time processing, Azure SQL Database for historical storage, and Azure Machine Learning for batch queries.
Why it's wrong here
Event Hubs is a data ingestion service, not a processing engine. Azure SQL Database is not cost-effective for storing terabytes to petabytes of historical data and has limited capacity for complex aggregations. Azure Machine Learning is for predictive modeling, not general-purpose batch analytics.
When this WOULD be correct
A scenario requiring high-throughput event ingestion (e.g., millions of events per second) with downstream real-time analytics in Azure Stream Analytics, combined with a relational historical store for transactional queries and a need for predictive analytics on historical data.
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.
✓A. Azure Stream Analytics for real-time processing, Azure Data Lake Storage Gen2 for historical storage, and Azure Synapse Analytics for batch queries.Correct answer▾
Why this is correct
A is correct because it forms a complete, scalable IoT analytics pipeline. Azure Stream Analytics is purpose-built for real-time stream processing with built-in windowing functions and SQL-like syntax, making it ideal for live insight on sensor data. Azure Data Lake Storage Gen2 provides a hierarchical, cost-effective storage layer for massive volumes of raw and transformed IoT telemetry, supporting Parquet and Delta formats for efficient downstream querying. Azure Synapse Analytics can query that lake directly using serverless SQL or dedicated pools, enabling complex batch analytics without forcing data movement or duplication.
✗B. Azure Data Factory for real-time processing, Azure Cosmos DB for historical storage, and Power BI for batch queries.Wrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is not a real-time processing service; it's an ETL and orchestration tool. Azure Cosmos DB is not optimized for cost-effective storage of years of historical data for complex aggregations, and Power BI is a visualization tool, not a batch query engine for complex aggregations.
★ When this WOULD be the correct answer
A company needs to ingest data from multiple sources, transform it using a visual interface, and store it in a globally distributed, low-latency database for real-time dashboards. They also need to visualize the data in Power BI. In this scenario, Azure Data Factory for orchestration, Cosmos DB for operational storage, and Power BI for reporting would be appropriate.
Why candidates choose this
Candidates may think Data Factory can process real-time data because it can handle streaming data with mapping data flows, and they may associate Cosmos DB with any 'big data' storage due to its scalability, overlooking cost and analytical query performance.
✗D. Azure Event Hubs for real-time processing, Azure SQL Database for historical storage, and Azure Machine Learning for batch queries.Wrong answer — click to see why▾
Why this is wrong here
Azure Event Hubs is for data ingestion, not real-time processing; Azure SQL Database is not cost-effective for large-scale historical storage with complex aggregations; Azure Machine Learning is for predictive modeling, not batch querying.
★ When this WOULD be the correct answer
A scenario requiring high-throughput event ingestion (e.g., millions of events per second) with downstream real-time analytics in Azure Stream Analytics, combined with a relational historical store for transactional queries and a need for predictive analytics on historical data.
Why candidates choose this
Candidates may confuse Event Hubs' ingestion capability with real-time processing, and think Azure SQL Database is suitable for all storage needs, while Azure Machine Learning seems advanced for analytics.
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
Learn chapter
Data Roles and Core Concepts
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.