DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company has a streaming data pipeline that ingests sensor data from factory equipment into Azure Event Hubs. The data must be prepared for reporting by cleaning invalid records, removing duplicates, and aggregating readings into 5-minute windows. The transformed data needs to be stored in a columnar format in a data lake to support efficient querying by data analysts using SQL. Which Azure service should perform the data transformation and loading?
⚠ Common exam trap
Test-takers frequently confuse Azure Data Factory or Synapse Pipelines as suitable for streaming transformations because they see 'pipeline' or 'data movement' keywords, but these services are batch-oriented and cannot perform real-time windowed aggregations directly from Event Hubs.
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 Stream Analytics
Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, directly consuming data from Azure Event Hubs, performing transformations like cleaning invalid records, removing duplicates, and aggregating over tumbling windows (e.g., 5-minute windows), and outputting the results in a columnar format (e.g., Parquet) to Azure Data Lake Storage. This aligns perfectly with the requirement for a low-latency, continuous transformation pipeline without needing additional orchestration or compute clusters.
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 Data Factory
Why it's wrong here
Azure Data Factory is fundamentally a batch data integration service that moves and transforms data on a schedule or via triggers. Even when configured with Event Hubs as a source, it performs bounded reads to ingest data in discrete chunks, not the continuous, low-latency processing that a streaming pipeline requires. It also omits native support for event ordering and sliding windows, making it unsuitable for real-time analytics and a poor fit for this use case compared to a dedicated stream processor.
When this WOULD be correct
A question where the requirement is to orchestrate a scheduled batch ETL pipeline that moves data from on-premises SQL Server to Azure Blob Storage, with transformations performed by a separate compute service like Azure Databricks or HDInsight.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a powerful analytics platform that can process streaming data, but it requires more configuration and manual coding (e.g., using Structured Streaming) compared to a dedicated service like Stream Analytics for this specific use case of simple transformations and output to a data lake.
When this WOULD be correct
A question where the data transformation requires complex custom logic (e.g., machine learning model scoring, advanced data wrangling with Python/Scala) and the output needs to be stored in Delta Lake format for further interactive analytics. For example: 'A data science team needs to perform real-time anomaly detection on sensor data using a custom ML model and store results in Delta Lake.'
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a serverless real-time analytics service that can ingest data from Event Hubs, perform time-windowed aggregations, clean data, and output to Azure Data Lake Storage in the desired columnar format. It is the most straightforward and cost-effective choice for this streaming ETL scenario.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Azure Synapse Pipelines is not a real-time streaming engine; it is an orchestration layer for managing data integration workflows. While it can copy data to and from Event Hubs, the interaction is a controlled, batch-oriented pull of data rather than a continuous, push-based event stream. It lacks built-in constructs for time-windowed aggregations or stateful event processing, so it would require additional services to achieve the same streaming ETL functionality as Stream Analytics.
When this WOULD be correct
A company needs to orchestrate and schedule complex ETL workflows that move and transform data from multiple on-premises and cloud sources into Azure Synapse Analytics for enterprise data warehousing, with transformations executed in Spark notebooks or SQL scripts.
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 Stream AnalyticsCorrect answer▾
Why this is correct
Azure Stream Analytics is a serverless real-time analytics service that can ingest data from Event Hubs, perform time-windowed aggregations, clean data, and output to Azure Data Lake Storage in the desired columnar format. It is the most straightforward and cost-effective choice for this streaming ETL scenario.
✗Azure Data FactoryWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is primarily an orchestration and data movement service, not a real-time stream processing engine. It lacks native capabilities for windowed aggregations, deduplication, and cleaning of streaming data before loading into a data lake.
★ When this WOULD be the correct answer
A question where the requirement is to orchestrate a scheduled batch ETL pipeline that moves data from on-premises SQL Server to Azure Blob Storage, with transformations performed by a separate compute service like Azure Databricks or HDInsight.
Why candidates choose this
Candidates may confuse Data Factory's data movement and transformation capabilities (e.g., Mapping Data Flows) with real-time stream processing, or assume it can handle streaming data because it supports Event Hubs as a source.
✗Azure DatabricksWrong answer — click to see why▾
Why this is wrong here
Azure Databricks is a general-purpose analytics platform for batch and streaming, but for this specific requirement of cleaning, deduplicating, and aggregating streaming data in 5-minute windows with direct output to columnar storage in a data lake, Azure Stream Analytics provides a simpler, fully managed service optimized for real-time stream processing without the overhead of cluster management.
★ When this WOULD be the correct answer
A question where the data transformation requires complex custom logic (e.g., machine learning model scoring, advanced data wrangling with Python/Scala) and the output needs to be stored in Delta Lake format for further interactive analytics. For example: 'A data science team needs to perform real-time anomaly detection on sensor data using a custom ML model and store results in Delta Lake.'
Why candidates choose this
Candidates may associate Databricks with big data processing and streaming (Spark Structured Streaming) and overlook that Azure Stream Analytics is purpose-built for real-time data transformation with built-in windowing and deduplication, making it more appropriate for this straightforward ETL pipeline.
✗Azure Synapse PipelinesWrong answer — click to see why▾
Why this is wrong here
Azure Synapse Pipelines is designed for orchestrating data movement and transformation in batch scenarios, not for real-time streaming transformations like cleaning, deduplication, and windowed aggregation on live sensor data.
★ When this WOULD be the correct answer
A company needs to orchestrate and schedule complex ETL workflows that move and transform data from multiple on-premises and cloud sources into Azure Synapse Analytics for enterprise data warehousing, with transformations executed in Spark notebooks or SQL scripts.
Why candidates choose this
Candidates may confuse Synapse Pipelines with a streaming service because it integrates with Spark and can handle some streaming workloads, but its primary strength is batch orchestration, not real-time stream processing.
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
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.
Key term
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
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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.