- A
Enable Event Hubs Capture to automatically write data to Data Lake Storage in Avro format.
Why wrong: Event Hubs Capture writes data in Avro format, not Parquet, so it does not meet the format requirement.
- B
Use Azure Data Factory to copy data from Event Hubs to Data Lake Storage every 5 minutes.
Why wrong: Azure Data Factory copies data in batches (e.g., every 5 minutes), introducing latency that may not satisfy real-time requirements.
- C
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Azure Stream Analytics provides real-time processing from Event Hubs and supports writing directly to Data Lake Storage in Parquet format with partitioning.
- D
Configure Stream Analytics output to partition by date and hour.
Configuring Stream Analytics output to partition by date and hour ensures the data is stored in the required folder structure without additional processing.
- E
Use Azure Databricks to process the stream and write to Data Lake Storage.
Azure Databricks can consume Event Hubs streams and write Parquet files with custom partitioning, supporting low-latency while enabling advanced transformations if needed.
Azure Stream Analytics for Parquet Output to Data Lake — Partitioned by Date and Hour
This DP-203 practice question tests your understanding of design and implement data storage. 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. A key principle to apply: azure Stream Analytics. 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 company ingests streaming data from multiple sources into Azure Event Hubs. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and hour. The solution must minimize cost and processing latency. Which THREE actions should be taken?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Azure Stream Analytics (C) is ideal for this scenario because it processes streaming data from Event Hubs in real time and can write directly to Azure Data Lake Storage Gen2 in Parquet format, which provides efficient compression and columnar storage for analytics. Additionally, configuring the output partitioning by date and hour (D) ensures the data is organized in the required structure without post-processing. Azure Databricks (E) can also process the stream from Event Hubs and write Parquet with partitioning, offering flexibility for complex transformations while still meeting latency requirements, though it may incur slightly higher cost than Stream Analytics for simple pipelines. Options A and B are incorrect: Event Hubs Capture writes Avro, not Parquet, and Data Factory introduces batch latency.
Key principle: Azure Stream Analytics
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Event Hubs Capture to automatically write data to Data Lake Storage in Avro format.
Why it's wrong here
Event Hubs Capture writes data in Avro format, not Parquet, so it does not meet the format requirement.
- ✗
Use Azure Data Factory to copy data from Event Hubs to Data Lake Storage every 5 minutes.
Why it's wrong here
Azure Data Factory copies data in batches (e.g., every 5 minutes), introducing latency that may not satisfy real-time requirements.
- ✓
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Why this is correct
Azure Stream Analytics provides real-time processing from Event Hubs and supports writing directly to Data Lake Storage in Parquet format with partitioning.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Azure Stream Analytics
- ✓
Configure Stream Analytics output to partition by date and hour.
Why this is correct
Configuring Stream Analytics output to partition by date and hour ensures the data is stored in the required folder structure without additional processing.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Azure Stream Analytics
- ✓
Use Azure Databricks to process the stream and write to Data Lake Storage.
Why this is correct
Azure Databricks can consume Event Hubs streams and write Parquet files with custom partitioning, supporting low-latency while enabling advanced transformations if needed.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Azure Stream Analytics
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse Event Hubs Capture's Avro output with the ability to write Parquet directly, or they mistakenly choose batch-oriented tools like Data Factory when a real-time streaming service (Stream Analytics) meets all requirements.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a SQL-like query language to process streaming data and supports native output to Azure Data Lake Storage Gen2 with Parquet serialization. Partitioning by date and hour in the output configuration (Option D) ensures that data is organized into folder structures like `YYYY/MM/DD/HH`, which optimizes downstream query performance in tools like Azure Synapse Analytics or Spark. Under the hood, Stream Analytics uses checkpointing and exactly-once semantics for reliable delivery, making it suitable for real-time analytics pipelines.
KKey Concepts to Remember
- Azure Stream Analytics
- Parquet format
- Event Hubs Capture
- Partitioning by date and hour
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
Azure Stream Analytics
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.
Review azure Stream Analytics, then practise related DP-203 questions on the same topic to reinforce the concept.
- →
Design and implement data storage — study guide chapter
Learn the concepts, then practise the questions
- →
Design and implement data storage practice questions
Targeted practice on this topic area only
- →
All DP-203 questions
851 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
Related DP-203 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Secure, monitor, and optimize data storage and data processing practice questions
Practise DP-203 questions linked to Secure, monitor, and optimize data storage and data processing.
Design and develop data processing practice questions
Practise DP-203 questions linked to Design and develop data processing.
Design and implement data security practice questions
Practise DP-203 questions linked to Design and implement data security.
Monitor and optimize data storage and processing practice questions
Practise DP-203 questions linked to Monitor and optimize data storage and processing.
Design and implement data storage practice questions
Practise DP-203 questions linked to Design and implement data storage.
Develop data processing practice questions
Practise DP-203 questions linked to Develop data processing.
DP-203 fundamentals practice questions
Practise DP-203 questions linked to DP-203 fundamentals.
DP-203 scenario practice questions
Practise DP-203 questions linked to DP-203 scenario.
DP-203 troubleshooting practice questions
Practise DP-203 questions linked to DP-203 troubleshooting.
Practice this exam
Start a free DP-203 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DP-203 question test?
Design and implement data storage — This question tests Design and implement data storage — Azure Stream Analytics.
What is the correct answer to this question?
The correct answer is: Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format. — Azure Stream Analytics (C) is ideal for this scenario because it processes streaming data from Event Hubs in real time and can write directly to Azure Data Lake Storage Gen2 in Parquet format, which provides efficient compression and columnar storage for analytics. Additionally, configuring the output partitioning by date and hour (D) ensures the data is organized in the required structure without post-processing. Azure Databricks (E) can also process the stream from Event Hubs and write Parquet with partitioning, offering flexibility for complex transformations while still meeting latency requirements, though it may incur slightly higher cost than Stream Analytics for simple pipelines. Options A and B are incorrect: Event Hubs Capture writes Avro, not Parquet, and Data Factory introduces batch latency.
What should I do if I get this DP-203 question wrong?
Review azure Stream Analytics, then practise related DP-203 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Azure Stream Analytics
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
More DP-203 practice questions
- You are designing a data storage solution for IoT sensor data. The data is written thousands of times per second and req…
- A data processing job in Azure Synapse Analytics writes results to a table in the dedicated SQL pool. After a failure, t…
- A multinational corporation uses Azure Data Lake Storage Gen2 to store petabytes of parquet files partitioned by date an…
- A company ingests streaming data from IoT devices into Azure Event Hubs. The data must be processed in near real-time to…
- Which THREE factors should be considered when choosing between Azure Stream Analytics and Azure Databricks for a real-ti…
- You are designing a data lake on Azure Data Lake Storage Gen2. The data will be used by both batch processing (Spark) an…
Last reviewed: Jun 11, 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.