- A
Enable Event Hubs Capture to automatically write data to Data Lake Storage in Avro format.
Why wrong: Capture writes Avro, not Parquet, and does not support custom partitioning.
- B
Use Azure Data Factory to copy data from Event Hubs to Data Lake Storage every 5 minutes.
Why wrong: Data Factory is not optimized for real-time streaming.
- C
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Stream Analytics can transform and output Parquet to Data Lake.
- D
Configure Stream Analytics output to partition by date and hour.
Partitioning by date and hour is required for the storage layout.
- E
Use Azure Databricks to process the stream and write to Data Lake Storage.
Why wrong: Databricks adds cost and latency compared to Stream Analytics.
DP-203 Design and implement data storage Practice Question
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. 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 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.
Option C is correct because Azure Stream Analytics can read directly from Event Hubs and write to Azure Data Lake Storage Gen2 in Parquet format, which is a columnar format optimized for analytics and compression. This approach minimizes cost and processing latency by enabling real-time, continuous processing without the need for intermediate storage or batch orchestration, meeting the requirements for low-latency ingestion.
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.
- ✗
Enable Event Hubs Capture to automatically write data to Data Lake Storage in Avro format.
Why it's wrong here
Capture writes Avro, not Parquet, and does not support custom partitioning.
- ✗
Use Azure Data Factory to copy data from Event Hubs to Data Lake Storage every 5 minutes.
Why it's wrong here
Data Factory is not optimized for real-time streaming.
- ✓
Use Azure Stream Analytics to read from Event Hubs and write to Data Lake Storage in Parquet format.
Why this is correct
Stream Analytics can transform and output Parquet to Data Lake.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Configure Stream Analytics output to partition by date and hour.
Why this is correct
Partitioning by date and hour is required for the storage layout.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Azure Databricks to process the stream and write to Data Lake Storage.
Why it's wrong here
Databricks adds cost and latency compared to Stream Analytics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Event Hubs Capture (which writes Avro) with the ability to write Parquet directly, or they overcomplicate the solution by choosing batch tools like Data Factory or heavy compute like Databricks when a simpler, cost-effective streaming service like 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
- 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 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.
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-203 question test?
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
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. — Option C is correct because Azure Stream Analytics can read directly from Event Hubs and write to Azure Data Lake Storage Gen2 in Parquet format, which is a columnar format optimized for analytics and compression. This approach minimizes cost and processing latency by enabling real-time, continuous processing without the need for intermediate storage or batch orchestration, meeting the requirements for low-latency ingestion.
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.
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?
Read the scenario before looking for a memorised answer.
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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.
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