- A
Azure Stream Analytics
Why wrong: Azure Stream Analytics is a real-time event processing engine but does not support complex batch queries on historical data. It would need to be combined with another service for batch analytics, which contradicts the requirement for a single service.
- B
Azure Data Lake Storage Gen2
Why wrong: Azure Data Lake Storage Gen2 is a storage service, not a compute service. It provides a scalable data lake but requires separate compute (e.g., Azure Synapse or Azure Databricks) to perform analytics, so it does not meet the 'single service' requirement.
- C
Azure Synapse Analytics
Azure Synapse Analytics is a unified analytics platform that supports both real-time stream processing (via pipelines and Spark streaming) and batch analytics with T-SQL and Apache Spark, all within a single service. It provides a unified query language and minimizes architecture complexity.
- D
Azure HDInsight
Why wrong: Azure HDInsight is a managed big data platform that supports various open-source frameworks like Hadoop, Spark, and Kafka. While it can handle both batch and streaming, it does not provide a single unified query language; it often requires using multiple components (e.g., Spark for streaming, Hive for batch) and more management effort.
Quick Answer
The answer is Azure Synapse Analytics. This service is the correct choice because it unifies real-time stream processing and large-scale batch analytics within a single platform, using either T-SQL or Spark SQL as a common query language—directly addressing the need to analyze live IoT sensor data for anomaly alerts while also running complex historical queries on months of equipment data without juggling separate tools. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of Synapse’s core value proposition: converging data integration, enterprise data warehousing, and big data analytics. A common trap is confusing Azure Stream Analytics (which handles streaming only) or Azure Data Lake Storage (a storage layer, not a query engine). Remember the memory tip: “Synapse stitches streams and stores under one SQL roof.”
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 uses IoT sensors to collect temperature and vibration data from machinery. They need to analyze the streaming data in real time to detect anomalies and trigger alerts. Additionally, they need to run complex historical queries on months of sensor data to identify equipment failure patterns. They want a single Azure service that can handle both real-time stream processing and large-scale batch analytics using a unified query language, minimizing the need for separate technologies. Which Azure service should they use?
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
Azure Synapse Analytics
Azure Synapse Analytics is the correct choice because it provides a unified platform that combines both real-time stream processing (via Synapse Pipelines and Spark Streaming) and large-scale batch analytics (via Synapse SQL and Spark) using a single query language (T-SQL or Spark SQL). This eliminates the need for separate technologies, directly addressing the requirement for a single service to handle both streaming and historical batch analysis.
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.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is a real-time event processing engine but does not support complex batch queries on historical data. It would need to be combined with another service for batch analytics, which contradicts the requirement for a single service.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is a storage service, not a compute service. It provides a scalable data lake but requires separate compute (e.g., Azure Synapse or Azure Databricks) to perform analytics, so it does not meet the 'single service' requirement.
- ✓
Azure Synapse Analytics
Why this is correct
Azure Synapse Analytics is a unified analytics platform that supports both real-time stream processing (via pipelines and Spark streaming) and batch analytics with T-SQL and Apache Spark, all within a single service. It provides a unified query language and minimizes architecture complexity.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure HDInsight
Why it's wrong here
Azure HDInsight is a managed big data platform that supports various open-source frameworks like Hadoop, Spark, and Kafka. While it can handle both batch and streaming, it does not provide a single unified query language; it often requires using multiple components (e.g., Spark for streaming, Hive for batch) and more management effort.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Stream Analytics as a one-stop solution for both streaming and batch, overlooking its lack of native batch analytics capabilities, while Azure Synapse Analytics is designed specifically to unify these workloads.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse Analytics leverages a distributed SQL engine (formerly SQL Data Warehouse) for batch queries and integrates with Apache Spark for real-time streaming via Structured Streaming, all managed through a single workspace. This allows users to query streaming data landing in a storage layer (e.g., ADLS Gen2) using T-SQL or Spark SQL without moving data between systems. A real-world scenario is a manufacturing plant using Synapse to run real-time anomaly detection on sensor data while simultaneously performing monthly failure pattern analysis on the same dataset, all within one service.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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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: Azure Synapse Analytics — Azure Synapse Analytics is the correct choice because it provides a unified platform that combines both real-time stream processing (via Synapse Pipelines and Spark Streaming) and large-scale batch analytics (via Synapse SQL and Spark) using a single query language (T-SQL or Spark SQL). This eliminates the need for separate technologies, directly addressing the requirement for a single service to handle both streaming and historical batch analysis.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
4 more ways this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A manufacturing company collects sensor data from factory equipment as a continuous stream of events ingested into Azure Event Hubs. Additionally, the company receives daily inventory CSV files uploaded to Azure Data Lake Storage Gen2. The analytics team needs to build near real-time dashboards that combine streaming sensor data with batch inventory data, and also support historical reporting by querying data directly in the data lake using SQL without moving it. Which Azure service should they choose as the primary analytics platform?
hard- ✓ A.Azure Synapse Analytics
- B.Azure Stream Analytics
- C.Azure Data Factory
- D.Azure HDInsight with Spark
Why A: Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest both real-time streaming data from Azure Event Hubs and batch data from Azure Data Lake Storage Gen2. Its SQL Serverless feature allows querying data directly in the data lake using T-SQL without moving it, enabling near real-time dashboards and historical reporting in a single service.
Variation 2. A manufacturing company ingests a continuous stream of sensor data from thousands of IoT devices into Azure Event Hubs. The company also stores historical equipment maintenance records in Azure SQL Database. The operations team needs to join the streaming sensor data with the historical maintenance records in near real-time to detect anomalies, and data scientists need to run ad-hoc T-SQL queries on the combined dataset for analysis. Which Azure service should they use as the primary analytics platform to meet both requirements?
hard- A.Azure Stream Analytics
- B.Azure Databricks
- ✓ C.Azure Synapse Analytics
- D.Azure Analysis Services
Why C: Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest streaming data from Azure Event Hubs via its built-in Spark pools or pipelines, and simultaneously query historical data in Azure SQL Database using T-SQL. This enables near real-time anomaly detection through streaming joins and ad-hoc T-SQL queries for data scientists, all within a single service without needing separate tools.
Variation 3. A manufacturing company ingests a continuous stream of sensor data from factory equipment into Azure Event Hubs. Additionally, historical maintenance data in CSV format is stored in Azure Data Lake Storage Gen2. The analytics team needs to join the streaming sensor data with the historical data in near real-time and enable analysts to query the combined dataset using standard T-SQL without moving the data. Which Azure service should they use as the primary analytics platform?
hard- A.A) Azure Stream Analytics
- ✓ B.B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool
- C.C) Azure SQL Database
- D.D) Azure Databricks
Why B: Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool is the correct choice because it can ingest streaming data from Event Hubs via pipelines, query historical CSV data in Data Lake Storage Gen2 directly using T-SQL without moving it, and join both datasets in near real-time using the serverless SQL pool's ability to reference external data sources. This meets the requirement for standard T-SQL queries on combined streaming and historical data without data movement.
Variation 4. You are the data engineer for a large retail company. The company has an existing on-premises SQL Server database with 10 years of transactional data. They want to move this data to Azure to enable advanced analytics using Azure Synapse Analytics. The data includes customer orders, product details, and inventory. The solution must minimize data movement and support both batch and real-time analytics. The company also wants to use Power BI for reporting. They have a limited budget and prefer a serverless option for compute. You are evaluating the following approaches: A) Use Azure Data Factory to copy all data to Azure Data Lake Storage Gen2, then use Azure Synapse Serverless SQL pool to query the data, and finally connect Power BI to the serverless SQL endpoint. B) Use Azure Database Migration Service to migrate the SQL Server database to Azure SQL Database, then use Azure Synapse Analytics with a dedicated SQL pool to perform analytics, and connect Power BI to the dedicated pool. C) Use Azure Data Factory to copy all data to Azure Blob Storage, then use Azure Stream Analytics to perform real-time analytics, and connect Power BI directly to Stream Analytics output. D) Use Azure Data Factory to copy historical data to Azure Data Lake Storage Gen2, use Azure Synapse Serverless SQL pool for batch analytics, and use Azure Event Hubs and Stream Analytics for real-time data, with Power BI connecting to both serverless SQL and Stream Analytics. Which approach best meets the requirements?
hard- A.Option A
- B.Option C
- C.Option B
- ✓ D.Option D
Why D: Option D best meets the requirements because it uses Azure Data Factory to copy historical data to Azure Data Lake Storage Gen2, enabling cost-effective storage and batch analytics via Azure Synapse Serverless SQL pool (serverless compute). It also incorporates Azure Event Hubs and Stream Analytics for real-time data ingestion and analytics, with Power BI connecting to both the serverless SQL endpoint and Stream Analytics output. This minimizes data movement by keeping data in the lake, supports both batch and real-time analytics, and uses a serverless option to stay within a limited budget.
Last reviewed: Jun 11, 2026
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