DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Stream Analytics as the primary platform for streaming data, overlooking that Synapse Analytics provides the unified query layer needed to combine streaming and batch data for both dashboards and historical reporting without additional services.
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 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.
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 Synapse Analytics
Why this is correct
Correct. Azure Synapse Analytics unifies data ingestion, processing, and analytics, supporting both streaming (via Event Hubs integration) and batch (via PolyBase or serverless SQL pool to query data lake directly). It provides near real-time and historical analytics capabilities.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics is a real-time event-processing engine that takes input from Event Hubs or IoT Hub and outputs to sinks like Power BI, but it isn't designed for interactive batch querying of your data lake. It has SQL-like syntax for windowed aggregates and alerts, but lacks the ability to perform complex, multi-table historical reports over years of sensor telemetry in a data lake unless you chain it to another analytics service. Therefore, by itself it addresses the real-time monitoring half of the factory use case, while leaving the batch-history requirement unmet.
When this WOULD be correct
Azure Stream Analytics would be correct if the question only required real-time processing of streaming sensor data from Event Hubs and outputting results to a dashboard or storage, without any need to combine with batch data or query data lake files directly using SQL.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory (ADF) is a cloud ETL/ELT orchestration service, not an analytical query engine. It can copy raw sensor data into Azure Data Lake or a Synapse SQL pool, and its Data Flows can perform transformations on Spark clusters, but ADF has no interactive T-SQL or DAX surface for you to run ad hoc analytics. It also lacks dedicated endpoints for dashboarding; your BI tool would still need a downstream compute service like Synapse to answer queries, so using ADF alone would not satisfy the company's analytical needs.
When this WOULD be correct
A question asking for a service to orchestrate data movement from Event Hubs and Data Lake Storage into a data warehouse or analytics system, without requiring real-time querying or SQL-on-lake capabilities.
- ✗
Azure HDInsight with Spark
Why it's wrong here
Azure HDInsight with Spark is a managed big-data cluster service that can handle both streaming (Structured Streaming) and batch joins, but it is an infrastructure-centric PaaS option. You must provision, size, and patch clusters yourself, and there is no built-in serverless SQL/BI layer; to serve dashboards you'd need to set up Hive/Spark SQL, a thrift server, and connect via separate tools such as Power BI gateway. Synapse Analytics, by contrast, exposes serverless and dedicated SQL pools directly over the same data lake, avoiding that operational overhead.
When this WOULD be correct
A question where the requirement is to perform custom machine learning or complex ETL on large datasets using a managed Spark cluster, and the focus is on flexibility and programming (e.g., Python/Scala) rather than serverless SQL querying or unified analytics.
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.
✓Azure Synapse AnalyticsCorrect answer▾
Why this is correct
Correct. Azure Synapse Analytics unifies data ingestion, processing, and analytics, supporting both streaming (via Event Hubs integration) and batch (via PolyBase or serverless SQL pool to query data lake directly). It provides near real-time and historical analytics capabilities.
✗Azure Stream AnalyticsWrong answer — click to see why▾
Why this is wrong here
Azure Stream Analytics is designed for real-time stream processing but cannot directly query batch data in Data Lake Storage Gen2 using SQL without moving it, nor does it support combining streaming and batch data in a unified analytics platform for near real-time dashboards and historical reporting.
★ When this WOULD be the correct answer
Azure Stream Analytics would be correct if the question only required real-time processing of streaming sensor data from Event Hubs and outputting results to a dashboard or storage, without any need to combine with batch data or query data lake files directly using SQL.
Why candidates choose this
Candidates see 'streaming sensor data' and 'near real-time dashboards' and immediately think of Stream Analytics, overlooking the requirement to also handle batch inventory data and support SQL-based querying on the data lake without data movement.
✗Azure Data FactoryWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is an ETL and orchestration service, not an analytics platform. It cannot directly serve near real-time dashboards or support SQL queries on data lake data without moving it.
★ When this WOULD be the correct answer
A question asking for a service to orchestrate data movement from Event Hubs and Data Lake Storage into a data warehouse or analytics system, without requiring real-time querying or SQL-on-lake capabilities.
Why candidates choose this
Candidates may confuse Data Factory's data integration role with analytics, thinking it can both move and analyze data, especially when the scenario involves combining multiple data sources.
✗Azure HDInsight with SparkWrong answer — click to see why▾
Why this is wrong here
HDInsight with Spark requires provisioning and managing a cluster, and does not natively support querying data directly in Data Lake Storage Gen2 using serverless SQL without moving it, unlike Synapse's serverless SQL pool.
★ When this WOULD be the correct answer
A question where the requirement is to perform custom machine learning or complex ETL on large datasets using a managed Spark cluster, and the focus is on flexibility and programming (e.g., Python/Scala) rather than serverless SQL querying or unified analytics.
Why candidates choose this
Candidates may think Spark is the go-to for big data analytics and streaming, but overlook that Synapse provides a more integrated, serverless SQL experience for querying data lakes without cluster management.
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?”
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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.