The answer is Azure Stream Analytics. This service is the correct choice because it is purpose-built for real-time stream processing and natively integrates with Azure Event Hubs as an input and dedicated SQL pool as an output, enabling you to handle late-arriving data streaming without reprocessing the entire stream. It achieves this through a configurable late arrival window, which allows you to specify a tolerance of up to 30 minutes for events that arrive after their event time, using event time processing to reorder or adjust results within that window. On the DP-203 exam, this scenario tests your understanding of temporal windows in streaming pipelines—a common trap is to choose Azure Data Factory or Databricks for this task, but those services lack the native, low-latency late-arrival handling that Stream Analytics provides. A useful memory tip: think of the late arrival window as a “grace period” for tardy taxis—Stream Analytics lets them in up to 30 minutes late without making you replay the entire trip history.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. 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.
You are designing a data pipeline in Azure Synapse Analytics that ingests streaming taxi trip data from Azure Event Hubs. The data must be processed in near real-time and stored in a dedicated SQL pool. The pipeline should handle late-arriving data (up to 30 minutes late) without reprocessing the entire stream. Which Azure service should you use to process the streaming data?
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 designed for real-time stream processing with native support for Event Hubs as an input and dedicated SQL pool as an output. It can handle late-arriving data via its built-in 'late arrival' window (configurable up to 30 minutes) using event time processing, without requiring reprocessing of the entire stream.
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 Databricks Structured Streaming
Why it's wrong here
While capable, Azure Databricks is more complex to set up and manage for simple stream processing compared to Stream Analytics.
✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics provides built-in support for late-arriving events and can output directly to Synapse SQL pool.
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 service for batch data movement and transformation, not real-time stream processing.
✗
Azure Functions with Event Hubs trigger
Why it's wrong here
Azure Functions can process events but requires custom code for late-arriving data and windowing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory's 'real-time' monitoring or Azure Functions' 'event-driven' nature with true stream processing, overlooking the need for built-in windowing and late-arrival handling that only Azure Stream Analytics provides.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a temporal windowing model (e.g., tumbling, hopping, sliding) and event time alignment to handle out-of-order and late events. The 'late arrival tolerance' is configured in the query using the WITH clause (e.g., WITH (LATEARRIVALTOLERANCE = 30 MINUTES)), which allows events up to 30 minutes late to be included in the correct window without reprocessing. Under the hood, Stream Analytics uses a checkpoint-based state management system that persists window state in Azure Storage, enabling exactly-once semantics even with late data.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Develop data processing — This question tests Develop data processing — 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 designed for real-time stream processing with native support for Event Hubs as an input and dedicated SQL pool as an output. It can handle late-arriving data via its built-in 'late arrival' window (configurable up to 30 minutes) using event time processing, without requiring reprocessing of the entire stream.
What should I do if I get this DP-203 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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Question Discussion
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