- A
Azure Stream Analytics for real-time processing and Azure Data Factory for daily batch aggregation
Correct. Stream Analytics handles real-time complex event processing. Data Factory can orchestrate serverless batch transformations and load data into Data Lake Storage.
- B
Azure Functions for real-time processing and Azure Databricks for daily batch aggregation
Why wrong: Incorrect. Azure Functions is not ideal for high-throughput streaming with complex temporal queries; it's more suited for individual event processing. Databricks is not serverless (requires a cluster) and may overcomplicate the solution.
- C
Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation
Why wrong: Incorrect. Azure Batch is for parallel compute workloads, not designed for serverless data transformation and orchestration of daily schedules.
- D
Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation
Why wrong: Incorrect. Data Lake Analytics is a batch processing service (U-SQL), not suitable for real-time streaming.
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 retail company ingests clickstream data from its e-commerce website into Azure Event Hubs. They need to detect customer journey patterns in real time within seconds and also prepare aggregated data for daily trend reports stored in Azure Data Lake Storage Gen2. The real-time processing must handle high throughput and support complex temporal queries like sessionization. The daily aggregation should be cost-effective and use serverless compute. Which combination of Azure services should they use?
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 processing and Azure Data Factory for daily batch aggregation
Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.
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 processing and Azure Data Factory for daily batch aggregation
Why this is correct
Correct. Stream Analytics handles real-time complex event processing. Data Factory can orchestrate serverless batch transformations and load data into Data Lake Storage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Functions for real-time processing and Azure Databricks for daily batch aggregation
Why it's wrong here
Incorrect. Azure Functions is not ideal for high-throughput streaming with complex temporal queries; it's more suited for individual event processing. Databricks is not serverless (requires a cluster) and may overcomplicate the solution.
- ✗
Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation
Why it's wrong here
Incorrect. Azure Batch is for parallel compute workloads, not designed for serverless data transformation and orchestration of daily schedules.
- ✗
Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation
Why it's wrong here
Incorrect. Data Lake Analytics is a batch processing service (U-SQL), not suitable for real-time streaming.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing Azure Functions (serverless compute) with Azure Stream Analytics (dedicated stream processing) for real-time analytics, and assuming Azure Batch (parallel job execution) is equivalent to Azure Data Factory (orchestrated data integration) for batch aggregation, leading candidates to overlook the specific requirements for high-throughput temporal queries and serverless cost-effectiveness.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language with temporal windows (e.g., hopping, sliding, session windows) to perform sessionization on clickstream data, automatically managing checkpointing and exactly-once semantics for fault tolerance. Azure Data Factory's serverless SQL Server Integration Services (SSIS) or Mapping Data Flows can execute daily aggregations on Data Lake Storage Gen2 without provisioning compute, scaling to petabyte-scale data while minimizing costs through auto-pause and auto-resume.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 processing and Azure Data Factory for daily batch aggregation — Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.
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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