- A
A. Azure Stream Analytics for real-time processing, Azure Data Lake Storage Gen2 for historical storage, and Azure Synapse Analytics for batch queries.
This combination correctly pairs a real-time stream processing engine (Stream Analytics) with a scalable data lake (Data Lake Storage) and an analytics service (Synapse Analytics) that can query the lake directly, minimizing data movement.
- B
B. Azure Data Factory for real-time processing, Azure Cosmos DB for historical storage, and Power BI for batch queries.
Why wrong: 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.
- C
C. Azure Functions for real-time processing, Azure Table Storage for historical storage, and Azure Analysis Services for batch queries.
Why wrong: 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
D. Azure Event Hubs for real-time processing, Azure SQL Database for historical storage, and Azure Machine Learning for batch queries.
Why wrong: 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.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
This combination correctly pairs a real-time stream processing engine (Stream Analytics) with a scalable data lake (Data Lake Storage) and an analytics service (Synapse Analytics) that can query the lake directly, minimizing data movement.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Trap categories for this question
Scenario analysis trap
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.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language to define continuous queries over time windows (e.g., tumbling, hopping, sliding) for real-time anomaly detection. Azure Synapse Analytics leverages a massively parallel processing (MPP) architecture to run complex aggregations directly on data stored in Azure Data Lake Storage Gen2 via PolyBase or external tables, avoiding costly data copying. This pattern is commonly called a 'lambda architecture' but with a simplified 'kappa' approach where the same data lake serves both real-time and batch layers.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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