Question 495 of 982

Describe an analytics workload on Azure →hardMultiple ChoiceObjective-mapped

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 financial services company processes real-time stock trade data from multiple exchanges. Trades are ingested into Azure Event Hubs. The company needs to compute a 5-minute sliding window average of trade prices per stock symbol and ensure that each trade is processed exactly once within the window. The aggregated results must be stored in Azure SQL Database for historical reporting and also sent to a Power BI dashboard for near real-time visualization. Which Azure service should be used for the real-time processing?

Question 1hardmultiple choice

Full question →

A
Azure Stream Analytics
Correct. Stream Analytics is designed for complex event processing with windowed aggregations and supports exactly-once delivery. It can output to multiple sinks, including SQL Database and Power BI, in near real-time.
B
Azure Databricks with Structured Streaming
Why wrong: Azure Databricks can process streaming data, but ensuring exactly-once semantics with sliding windows adds complexity (e.g., checkpointing, managing state). Stream Analytics provides a simpler, managed solution for this exact requirement.
C
Azure Data Factory
Why wrong: Azure Data Factory is an ETL orchestration service for batch and incremental data movement. It is not designed for real-time stream processing with sub-minute sliding windows.
D
Azure Event Hubs
Why wrong: Event Hubs is an ingestion service that captures streaming data but does not perform computations or aggregations. A separate processing engine is needed to compute the average.

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 purpose-built for real-time stream processing with native support for time-based windowing (e.g., 5-minute sliding window) and exactly-once semantics when used with Azure Event Hubs as input and Azure SQL Database as output. It can directly compute the sliding window average of trade prices per stock symbol and output results to both Azure SQL Database for historical storage and Power BI for near real-time visualization, all without requiring additional code or infrastructure management.

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
Correct. Stream Analytics is designed for complex event processing with windowed aggregations and supports exactly-once delivery. It can output to multiple sinks, including SQL Database and Power BI, in near real-time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Databricks with Structured Streaming
Why it's wrong here
Azure Databricks can process streaming data, but ensuring exactly-once semantics with sliding windows adds complexity (e.g., checkpointing, managing state). Stream Analytics provides a simpler, managed solution for this exact requirement.
✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is an ETL orchestration service for batch and incremental data movement. It is not designed for real-time stream processing with sub-minute sliding windows.
✗
Azure Event Hubs
Why it's wrong here
Event Hubs is an ingestion service that captures streaming data but does not perform computations or aggregations. A separate processing engine is needed to compute the average.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse Azure Event Hubs (a data ingestion service) with a processing engine, or assume that Azure Databricks is the only option for streaming analytics, overlooking the simpler, fully managed, and cost-effective Azure Stream Analytics for straightforward windowed aggregations.

Detailed technical explanation

How to think about this question

Azure Stream Analytics uses a declarative SQL-like query language that supports temporal operators such as HOPPINGWINDOW or SLIDINGWINDOW to define the 5-minute window. Under the hood, it leverages checkpointing and exactly-once delivery guarantees by coordinating with Event Hubs' offset tracking and Azure SQL Database's idempotent write mechanisms, ensuring no trade is counted twice even during failures or restarts. In a real-world scenario, this is critical for financial compliance, where duplicate or missed trades could lead to incorrect reporting or regulatory penalties.

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 purpose-built for real-time stream processing with native support for time-based windowing (e.g., 5-minute sliding window) and exactly-once semantics when used with Azure Event Hubs as input and Azure SQL Database as output. It can directly compute the sliding window average of trade prices per stock symbol and output results to both Azure SQL Database for historical storage and Power BI for near real-time visualization, all without requiring additional code or infrastructure management.

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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