The correct architecture is Azure Stream Analytics with output to Data Lake Storage Gen2, using partitioning by date and device ID. This solution is optimal because Azure Stream Analytics natively processes streaming data from Event Hubs with low latency and directly writes to Data Lake Storage Gen2 in Parquet format, supporting automatic partitioning through a specified partition key like date and device ID. On the DP-203 exam, this scenario tests your understanding of real-time streaming analytics to data lake pipelines, often appearing as a distractor where candidates might mistakenly choose Azure Databricks with Structured Streaming or Azure Data Factory for orchestration—both add unnecessary complexity and latency for near-real-time needs. A common trap is overlooking Stream Analytics’ built-in partitioning for Parquet output, which eliminates extra code. Memory tip: “Stream to Lake, partition by date and device—Stream Analytics makes it nice.”
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Your team needs to provide near-real-time analytics on IoT sensor data streaming into Azure Event Hubs. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and device ID. Which architecture should you implement?
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 with output to Data Lake Storage Gen2, using partitioning by date and device ID.
Azure Stream Analytics provides native, low-latency processing of streaming data from Event Hubs with direct output to Azure Data Lake Storage Gen2. It supports automatic partitioning by specifying a partition key (e.g., date and device ID) in the output configuration, enabling efficient, near-real-time writes in Parquet format without additional orchestration.
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.
✓
Use Azure Stream Analytics with output to Data Lake Storage Gen2, using partitioning by date and device ID.
Why this is correct
Stream Analytics provides native partitioning and Parquet output.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Azure Data Factory with a tumbling window trigger to copy data from Event Hubs to Data Lake Storage.
Why it's wrong here
ADF is not designed for real-time streaming from Event Hubs.
✗
Use Azure Functions to read from Event Hubs and write to Data Lake Storage.
Why it's wrong here
Functions have scaling limitations and require manual partitioning logic.
✗
Use Azure Databricks with Structured Streaming to read from Event Hubs and write to Data Lake Storage.
Why it's wrong here
Databricks can do this, but Stream Analytics is a simpler serverless option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that any service capable of reading from Event Hubs is suitable for near-real-time analytics, ignoring the critical requirements for native partitioning, low latency, and managed checkpointing that Stream Analytics uniquely provides.
Detailed technical explanation
How to think about this question
Azure Stream Analytics uses a time-based watermark and checkpointing mechanism to ensure exactly-once delivery when writing to Data Lake Storage Gen2. The output partitioning is implemented via the 'Partition by' clause in the query, which maps to the hierarchical folder structure in ADLS Gen2 (e.g., /date=2023-10-01/deviceID=123/). This architecture avoids the need for external orchestration and can handle up to 1 GB/s throughput per streaming unit.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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 with output to Data Lake Storage Gen2, using partitioning by date and device ID. — Azure Stream Analytics provides native, low-latency processing of streaming data from Event Hubs with direct output to Azure Data Lake Storage Gen2. It supports automatic partitioning by specifying a partition key (e.g., date and device ID) in the output configuration, enabling efficient, near-real-time writes in Parquet format without additional orchestration.
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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