- A
Azure Stream Analytics for real-time analysis and Azure Data Factory for batch aggregation
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.
- B
Azure Databricks for both real-time analysis and batch aggregation
Why wrong: While Databricks can handle both, it requires more complex setup for real-time streaming and is not as simple to integrate directly with Event Hubs as Stream Analytics. The question implies a desire for minimal effort.
- C
Azure Stream Analytics for both real-time analysis and batch aggregation
Why wrong: Stream Analytics is designed for continuous streaming queries, not for scheduled batch processing of historical data. It cannot easily perform nightly batch aggregation on stored data.
- D
Azure Data Factory for real-time analysis and Azure Databricks for batch aggregation
Why wrong: Data Factory is not a real-time processing engine; it handles scheduled or event-driven batch operations. Using it for real-time sentiment analysis would not meet the low-latency requirement.
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 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?
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Databricks for both real-time analysis and batch aggregation
Why it's wrong here
While Databricks can handle both, it requires more complex setup for real-time streaming and is not as simple to integrate directly with Event Hubs as Stream Analytics. The question implies a desire for minimal effort.
- ✗
Azure Stream Analytics for both real-time analysis and batch aggregation
Why it's wrong here
Stream Analytics is designed for continuous streaming queries, not for scheduled batch processing of historical data. It cannot easily perform nightly batch aggregation on stored data.
- ✗
Azure Data Factory for real-time analysis and Azure Databricks for batch aggregation
Why it's wrong here
Data Factory is not a real-time processing engine; it handles scheduled or event-driven batch operations. Using it for real-time sentiment analysis would not meet the low-latency requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language to define continuous queries over streaming data, with built-in support for temporal windows (e.g., Tumbling, Hopping, Sliding) that enable real-time aggregation. Azure Data Factory can trigger pipelines on a schedule or event, using activities like Copy Data or Data Flow to transform and move data from sources like Event Hubs or SQL Database to Azure Data Lake Storage, making it ideal for batch historical loads. In practice, the combination allows the company to decouple real-time dashboarding from historical analytics, optimizing cost and performance for each workload.
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
Got this wrong? Here's your next step.
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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 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.
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.
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Last reviewed: Jun 11, 2026
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.
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