DP-900 Describe an analytics workload on Azure Practice Question
A marketing company ingests streaming data from social media feeds into Azure Event Hubs. They want to perform real-time sentiment analysis on the data and store the results in Azure SQL Database for immediate dashboarding. They also need to aggregate the raw data over longer time windows and store it in Azure Data Lake Storage for historical trend analysis. Which combination of Azure services should they use for the two processing paths?
⚠ Common exam trap
Test-takers frequently assume a single service like Stream Analytics or Databricks can handle both real-time and batch processing equally well, but the exam expects you to recognize that Stream Analytics excels at real-time streaming while Data Factory is the appropriate managed service for scheduled batch aggregation in a cost-effective, serverless manner.
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 for real-time analysis and Azure Data Factory for batch aggregation
Azure Stream Analytics is ideal for real-time sentiment analysis on streaming data from Event Hubs, as it can process data in-motion with low latency and output directly to Azure SQL Database for immediate dashboarding. Azure Data Factory is the correct choice for batch aggregation over longer time windows, as it can orchestrate and execute periodic data movement and transformation jobs to load aggregated data into Azure Data Lake Storage for historical analysis.
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 for real-time analysis and Azure Data Factory for batch aggregation
Why this is correct
Azure Stream Analytics handles real-time processing and outputs to SQL Database. Azure Data Factory can schedule batch pipelines to read raw data from Event Hubs (or captured data) and aggregate it into Azure Data Lake Storage.
- ✗
Azure Databricks for both real-time analysis and batch aggregation
Why it's wrong here
Azure Databricks, whilst capable of stream processing, introduces more operational overhead and potential latency for the immediate real-time sentiment analysis from Event Hubs directly to Azure SQL Database compared to a fully managed, serverless stream processing service. It is, however, highly suitable for the complex batch aggregation and historical trend analysis to Data Lake Storage, as well as advanced analytics and machine learning tasks, making it a strong choice for the second processing path.
When this WOULD be correct
If the question required complex machine learning model training or advanced transformations on streaming data, and batch aggregation for historical analysis, Azure Databricks would be the correct choice for both paths.
- ✗
Azure Stream Analytics for both real-time analysis and batch aggregation
Why it's wrong here
Azure Stream Analytics is a fully managed stream processing engine that executes continuous, long-running queries over data in motion, using temporal windows like tumbling, hopping, and sliding. It cannot be scheduled to run a nightly batch aggregation over historical data already stored in a data lake or database; batch jobs require a scheduling and orchestration service such as Azure Data Factory or a compute engine like Azure Databricks. Attempting to use Stream Analytics for batch aggregation would mean rewriting streaming queries with fixed window logic and manually triggering them, which is neither cost-effective nor supported for ad-hoc, snapshot-style historical analysis.
- ✗
Azure Data Factory for real-time analysis and Azure Databricks for batch aggregation
Why it's wrong here
Azure Data Factory is an orchestration and ETL service built for scheduled, event-driven data movement and transformation pipelines, but it does not have a built-in stream processing engine to analyze data as it arrives in Event Hubs. Using Data Factory for real-time sentiment analysis would introduce latency because its trigger granularity is minutes and it copies or transforms data in batches rather than applying continuous queries. While Azure Databricks is very capable of batch aggregation and complex analytics to Data Lake Storage, the real-time requirement is unmet, making this pairing incorrect; the low-latency path needs Azure Stream Analytics or Databricks Structured Streaming directly from Event Hubs.
When this WOULD be correct
If the question required complex batch transformations (e.g., machine learning models on historical data) and real-time processing needed custom logic (e.g., using Spark Structured Streaming), then Azure Databricks for batch and Azure Stream Analytics for real-time would be correct. However, here the batch aggregation is simple and better served by Azure Data Factory.
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 Analytics for real-time analysis and Azure Data Factory for batch aggregationCorrect answer▾
Why this is correct
Azure Stream Analytics handles real-time processing and outputs to SQL Database. Azure Data Factory can schedule batch pipelines to read raw data from Event Hubs (or captured data) and aggregate it into Azure Data Lake Storage.
✗Azure Databricks for both real-time analysis and batch aggregationWrong answer — click to see why▾
Why this is wrong here
Azure Databricks is not optimized for continuous real-time streaming analytics on Event Hubs; it is better suited for complex batch processing and interactive analytics, not low-latency sentiment analysis.
★ When this WOULD be the correct answer
If the question required complex machine learning model training or advanced transformations on streaming data, and batch aggregation for historical analysis, Azure Databricks would be the correct choice for both paths.
Why candidates choose this
Candidates may think Databricks is a one-size-fits-all solution for both real-time and batch processing due to its Spark-based streaming capabilities, overlooking that Stream Analytics is simpler and more cost-effective for straightforward real-time ETL.
✗Azure Data Factory for real-time analysis and Azure Databricks for batch aggregationWrong answer — click to see why▾
Why this is wrong here
Azure Data Factory is not designed for real-time stream processing; it is an orchestration and ETL service for batch data movement. Azure Databricks can handle batch aggregation but is not the optimal choice for the simple batch aggregation described here, whereas Azure Data Factory is better suited for scheduled batch pipelines to Azure Data Lake Storage.
★ When this WOULD be the correct answer
If the question required complex batch transformations (e.g., machine learning models on historical data) and real-time processing needed custom logic (e.g., using Spark Structured Streaming), then Azure Databricks for batch and Azure Stream Analytics for real-time would be correct. However, here the batch aggregation is simple and better served by Azure Data Factory.
Why candidates choose this
Candidates may think Azure Databricks is a one-size-fits-all solution for both real-time and batch analytics, and they may underestimate the simplicity of using Azure Data Factory for straightforward batch aggregation to Data Lake Storage.
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
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.