Quick Answer
The answer is Structured Streaming, the correct Spark API for aggregating streaming data with 1-minute tumbling windows from Azure Event Hubs. This API is purpose-built for event-time-based aggregations, automatically managing state and watermarking to handle late-arriving data, which is essential when grouping streaming records into fixed 1-minute intervals. On the DP-203 exam, this scenario tests your understanding of real-time processing patterns, often appearing as a choice between Structured Streaming and the older DStreams API—a common trap where candidates mistakenly select DStreams, which lacks native event-time windowing and exactly-once semantics. Remember that Structured Streaming integrates natively with Event Hubs as a source and Synapse Analytics as a sink via `foreachBatch` or `writeStream`, making it the only viable option for reliable, stateful windowed aggregation. Memory tip: think "Structured for stateful windows"—if you need tumbling windows and exactly-once delivery, Structured Streaming is your only structured path.
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 processing solution in Azure Databricks to transform streaming data from Azure Event Hubs. The data must be aggregated in 1-minute tumbling windows and written to Azure Synapse Analytics. Which Spark API should you use?
RDD API
Why wrong: Low-level, not suitable for streaming.
Structured Streaming
Supports windowed aggregations and streaming sinks.
Spark Streaming (DStreams)
Why wrong: Legacy API; Structured Streaming preferred.
DataFrame API with batch processing
Why wrong: Not for streaming.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Structured Streaming
Structured Streaming is the correct choice because it provides native support for event-time-based aggregations, such as 1-minute tumbling windows, and integrates seamlessly with Azure Event Hubs as a streaming source and Azure Synapse Analytics as a streaming sink using the `foreachBatch` or `writeStream` API. It offers exactly-once semantics and automatic state management for windowed operations, which are essential for reliable streaming ETL.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
RDD API
Why it's wrong here
Low-level, not suitable for streaming.
Structured Streaming
Why this is correct
Supports windowed aggregations and streaming sinks.
Related concept
Read the scenario before looking for a memorised answer.
Spark Streaming (DStreams)
Why it's wrong here
Legacy API; Structured Streaming preferred.
DataFrame API with batch processing
Why it's wrong here
Not for streaming.
Common exam traps
The trap here is that candidates confuse the older Spark Streaming (DStreams) API with Structured Streaming, assuming both are equally capable for event-time windows, but DStreams lack native event-time support and are deprecated in favor of Structured Streaming.
Detailed technical explanation
Under the hood, Structured Streaming treats streaming data as an unbounded table and uses incremental query execution to compute tumbling window aggregates via watermarking and state store (backed by RocksDB or HDFS). When writing to Azure Synapse Analytics, the `foreachBatch` pattern is commonly used to batch micro-batches into efficient COPY INTO or JDBC writes, leveraging Synapse's PolyBase for high-throughput ingestion. A subtle behavior is that watermark delay must be configured to handle late-arriving events, otherwise the aggregation may drop data or produce incorrect results.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Develop data processing — study guide chapter
Learn the concepts, then practise the questions
Develop data processing practice questions
Targeted practice on this topic area only
All DP-203 questions
846 questions across all exam domains
Microsoft Azure Data Engineer Associate DP-203 study guide
Full concept coverage aligned to exam objectives
DP-203 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise DP-203 questions linked to Secure, monitor, and optimize data storage and data processing.
Practise DP-203 questions linked to Design and develop data processing.
Practise DP-203 questions linked to Design and implement data security.
Practise DP-203 questions linked to Monitor and optimize data storage and processing.
Practise DP-203 questions linked to Design and implement data storage.
Practise DP-203 questions linked to Develop data processing.
Practise DP-203 questions linked to DP-203 fundamentals.
Practise DP-203 questions linked to DP-203 scenario.
Practise DP-203 questions linked to DP-203 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Structured Streaming — Structured Streaming is the correct choice because it provides native support for event-time-based aggregations, such as 1-minute tumbling windows, and integrates seamlessly with Azure Event Hubs as a streaming source and Azure Synapse Analytics as a streaming sink using the `foreachBatch` or `writeStream` API. It offers exactly-once semantics and automatic state management for windowed operations, which are essential for reliable streaming ETL.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 24, 2026
This DP-203 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-203 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.