DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Many exam-takers choose Azure Stream Analytics because it is the most obvious service for streaming data, but they overlook the requirement for standard T-SQL queries on combined datasets without data movement, which Stream Analytics cannot fulfill as it uses its own query language and cannot directly join with static data in Data Lake Storage Gen2 using T-SQL.
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
✓
B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool
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.
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
Why it's wrong here
Incorrect. While Azure Stream Analytics can process streaming data and output to various sinks, it does not directly support joining streaming data with batch data or allow ad-hoc T-SQL queries on the combined dataset from the data lake.
When this WOULD be correct
Azure Stream Analytics would be correct if the question required real-time processing of streaming data only, with output to a sink like Power BI or Azure SQL Database, and did not require joining with static historical data or T-SQL querying of combined datasets.
- ✓
B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool
Why this is correct
Correct. Synapse Pipelines can orchestrate the ingestion of both streaming (Event Hubs) and batch (Data Lake) data. The data can be stored in the lake, and the serverless SQL pool can query it using T-SQL, providing a unified analytics surface without moving data. This meets all requirements.
- ✗
C) Azure SQL Database
Why it's wrong here
Incorrect. Azure SQL Database is a relational database designed for OLTP workloads. It cannot directly query data stored in Azure Data Lake Storage Gen2 or join streaming data from Event Hubs without complex external tables or data movement.
When this WOULD be correct
A company needs a fully managed relational database for OLTP workloads with high availability and built-in intelligence, such as an e-commerce platform requiring transactional consistency for order processing.
- ✗
D) Azure Databricks
Why it's wrong here
Incorrect. Azure Databricks is a Spark-based analytics platform that can process streaming and batch data, but it does not offer native T-SQL query capability. Users would need to use Spark SQL or Python/Scala, not standard T-SQL as required.
When this WOULD be correct
Azure Databricks would be correct if the question required advanced machine learning on the combined dataset, or if the analytics team preferred using Spark-based languages (Python, Scala, SQL) and needed collaborative notebooks for data science workflows.
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.
✓B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL poolCorrect answer▾
Why this is correct
Correct. Synapse Pipelines can orchestrate the ingestion of both streaming (Event Hubs) and batch (Data Lake) data. The data can be stored in the lake, and the serverless SQL pool can query it using T-SQL, providing a unified analytics surface without moving data. This meets all requirements.
✗A) 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 join streaming data with static data in Azure Data Lake Storage Gen2 using T-SQL without moving data. It lacks the serverless SQL pool capability for querying historical data in place.
★ When this WOULD be the correct answer
Azure Stream Analytics would be correct if the question required real-time processing of streaming data only, with output to a sink like Power BI or Azure SQL Database, and did not require joining with static historical data or T-SQL querying of combined datasets.
Why candidates choose this
Candidates may choose Stream Analytics because it is a well-known service for processing streaming data from Event Hubs, and they might overlook the requirement to join with historical data and query via T-SQL without data movement.
✗C) Azure SQL DatabaseWrong answer — click to see why▾
Why this is wrong here
Azure SQL Database cannot query data in Azure Data Lake Storage Gen2 directly without moving it, and it does not natively support joining streaming data from Event Hubs in near real-time without additional services like Stream Analytics.
★ When this WOULD be the correct answer
A company needs a fully managed relational database for OLTP workloads with high availability and built-in intelligence, such as an e-commerce platform requiring transactional consistency for order processing.
Why candidates choose this
Candidates may assume that T-SQL support implies a traditional SQL database, overlooking the requirement to query data in place without moving it and to handle streaming data.
✗D) Azure DatabricksWrong answer — click to see why▾
Why this is wrong here
Azure Databricks does not support querying data in place with standard T-SQL; it primarily uses Spark SQL or Python. The requirement for analysts to use T-SQL without moving data is better met by Azure Synapse serverless SQL pool.
★ When this WOULD be the correct answer
Azure Databricks would be correct if the question required advanced machine learning on the combined dataset, or if the analytics team preferred using Spark-based languages (Python, Scala, SQL) and needed collaborative notebooks for data science workflows.
Why candidates choose this
Candidates may associate Databricks with real-time and batch processing on large datasets, overlooking the specific T-SQL requirement. Its strong data engineering capabilities make it seem suitable for joining streaming and historical data.
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 |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Dataset
A dataset is a collection of related data, usually in a structured format, that can be used for analysis, training models, or reporting in Azure data services.
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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.