- A
Azure Stream Analytics for real-time detection; Azure Data Factory for daily aggregation
Stream Analytics is purpose-built for real-time stream processing, enabling low-latency fraud detection. Data Factory can orchestrate and schedule the daily batch pipeline to aggregate data, possibly using Azure Databricks or SQL.
- B
Azure Databricks for both real-time detection and daily aggregation
Why wrong: Databricks can handle both real-time and batch processing, but for real-time detection of simple patterns, Stream Analytics is simpler to set up and more cost-effective. Databricks is better suited for complex transformations and advanced analytics.
- C
Azure Synapse Analytics for real-time detection; Azure Blob Storage for daily aggregation
Why wrong: Azure Synapse Analytics is for analytics and data warehousing, not for real-time stream processing. Blob Storage is a storage service, not a processing service for aggregation.
- D
Azure Functions for real-time detection; Azure SQL Database for daily aggregation
Why wrong: Azure Functions can process events, but for high-throughput clickstream data, scaling can be complex and costly. SQL Database can store aggregated data, but it does not perform the aggregation itself; you would need additional logic.
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 company receives real-time clickstream data from its website via Azure Event Hubs. They need to detect fraudulent clicks within seconds and also produce daily aggregate reports of visitor statistics for historical analysis. Which combination of Azure services should they use for the real-time detection and the daily aggregation, respectively?
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 detection; Azure Data Factory for daily aggregation
Azure Stream Analytics is purpose-built for real-time stream processing, making it ideal for detecting fraudulent clicks within seconds from Event Hubs. Azure Data Factory is a cloud-based ETL service that can orchestrate and execute daily aggregation jobs on historical data, such as producing visitor statistics reports from stored clickstream data.
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 detection; Azure Data Factory for daily aggregation
Why this is correct
Stream Analytics is purpose-built for real-time stream processing, enabling low-latency fraud detection. Data Factory can orchestrate and schedule the daily batch pipeline to aggregate data, possibly using Azure Databricks or SQL.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Databricks for both real-time detection and daily aggregation
Why it's wrong here
Databricks can handle both real-time and batch processing, but for real-time detection of simple patterns, Stream Analytics is simpler to set up and more cost-effective. Databricks is better suited for complex transformations and advanced analytics.
- ✗
Azure Synapse Analytics for real-time detection; Azure Blob Storage for daily aggregation
Why it's wrong here
Azure Synapse Analytics is for analytics and data warehousing, not for real-time stream processing. Blob Storage is a storage service, not a processing service for aggregation.
- ✗
Azure Functions for real-time detection; Azure SQL Database for daily aggregation
Why it's wrong here
Azure Functions can process events, but for high-throughput clickstream data, scaling can be complex and costly. SQL Database can store aggregated data, but it does not perform the aggregation itself; you would need additional logic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Stream Analytics with Azure Databricks or Azure Functions for real-time processing, or think that Azure Blob Storage alone can perform aggregation, when in fact the question tests the specific pairing of a stream-processing service with a batch orchestration service.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language with temporal windows (e.g., TumblingWindow, HoppingWindow) to detect patterns like sudden spikes in clicks from a single IP, enabling sub-second fraud detection. Azure Data Factory can schedule pipelines to run daily, using Mapping Data Flows or Copy Activity to aggregate raw clickstream data from Blob Storage or Event Hubs capture into summary tables in Azure Synapse or SQL Database, handling large volumes efficiently without manual coding.
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 detection; Azure Data Factory for daily aggregation — Azure Stream Analytics is purpose-built for real-time stream processing, making it ideal for detecting fraudulent clicks within seconds from Event Hubs. Azure Data Factory is a cloud-based ETL service that can orchestrate and execute daily aggregation jobs on historical data, such as producing visitor statistics reports from stored clickstream data.
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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