Question 768 of 982

Describe an analytics workload on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is Azure Stream Analytics, the correct choice because it is purpose-built for near-real-time stream processing from Event Hubs, enabling you to aggregate sentiment scores over 5-minute tumbling windows and output the results directly to Azure Blob Storage without writing custom code. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of which Azure service handles real-time data ingestion and temporal aggregations, often appearing as a scenario where you must distinguish between batch processing tools like Azure Synapse Analytics and true stream processing engines. A common trap is choosing Azure Functions for its event-driven nature, but Stream Analytics provides built-in windowed functions and exactly-once delivery semantics specifically designed for this use case. Remember the memory tip: “Stream for streams, Synapse for batches” — if the question mentions time windows and live data from Event Hubs, always think Stream Analytics first.

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 team wants to analyze social media sentiment in near real-time. They will use Azure Event Hubs to capture tweets and need to aggregate sentiment scores over 5-minute windows. The aggregated results must be stored in Azure Blob Storage for later analysis. Which Azure service should they use to perform the stream processing?

Question 1mediummultiple choice

Full question →

A
Azure Stream Analytics
Azure Stream Analytics is specifically designed for real-time stream processing and can easily ingest from Event Hubs, apply windowed aggregations, and output to Blob Storage.
B
Azure Data Factory
Why wrong: Azure Data Factory is an orchestration and data integration service for batch processing, not real-time stream processing.
C
Azure Databricks
Why wrong: Azure Databricks can handle stream processing via Structured Streaming, but it requires more configuration and is overkill for a simple aggregation task best suited for Stream Analytics.
D
Azure Synapse Analytics
Why wrong: Azure Synapse Analytics is an analytics service for querying and processing data at scale, but it is not designed for real-time stream processing from Event Hubs directly.

Full breakdown with real-world context →

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 a real-time stream processing engine designed to ingest data from sources like Azure Event Hubs, apply temporal aggregations (e.g., 5-minute tumbling windows), and output results directly to Azure Blob Storage. It provides built-in support for windowed functions and exactly-once delivery semantics, making it ideal for near-real-time sentiment analysis without requiring custom code.

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
Why this is correct
Azure Stream Analytics is specifically designed for real-time stream processing and can easily ingest from Event Hubs, apply windowed aggregations, and output to Blob Storage.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an orchestration and data integration service for batch processing, not real-time stream processing.
✗
Azure Databricks
Why it's wrong here
Azure Databricks can handle stream processing via Structured Streaming, but it requires more configuration and is overkill for a simple aggregation task best suited for Stream Analytics.
✗
Azure Synapse Analytics
Why it's wrong here
Azure Synapse Analytics is an analytics service for querying and processing data at scale, but it is not designed for real-time stream processing from Event Hubs directly.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse Azure Data Factory or Azure Synapse Analytics as stream processing tools, but Data Factory is batch-only and Synapse is primarily a data warehouse, not a real-time stream processor.

Detailed technical explanation

How to think about this question

Azure Stream Analytics uses a SQL-like query language with temporal operators such as TumblingWindow, HoppingWindow, and SlidingWindow to define aggregation intervals. Under the hood, it leverages a distributed stream processing engine that guarantees exactly-once delivery to output sinks like Blob Storage, even during failures. In a real-world scenario, the marketing team could use a query like 'SELECT System.Timestamp AS WindowEnd, AVG(sentiment) AS AvgSentiment INTO blobOutput FROM tweetInput GROUP BY TumblingWindow(minute, 5)' to achieve the required aggregation.

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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — Azure Stream Analytics is the correct choice because it is a real-time stream processing engine designed to ingest data from sources like Azure Event Hubs, apply temporal aggregations (e.g., 5-minute tumbling windows), and output results directly to Azure Blob Storage. It provides built-in support for windowed functions and exactly-once delivery semantics, making it ideal for near-real-time sentiment analysis without requiring custom code.

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

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