- A
Use Azure IoT Hub for POS streaming and Azure Blob Storage for offline store uploads, then process with Stream Analytics and Data Factory
Why wrong: Two separate ingestion services increase management overhead and cost; IoT Hub is designed for device-to-cloud, not necessarily POS systems.
- C
Use Azure Data Lake Storage for all data, then use Azure Databricks structured streaming for real-time and batch
Why wrong: ADLS is storage, not an ingestion service; Databricks structured streaming can read from ADLS but not directly ingest streaming data without a queue.
- D
Use Azure Stream Analytics directly on POS data and store offline uploads in Blob Storage, then batch process with U-SQL
Why wrong: Stream Analytics cannot directly ingest from POS systems without a messaging layer; offline store uploads still need ingestion.
Quick Answer
The answer is Azure Event Hubs with Kafka protocol, combined with Stream Analytics and Event Hubs Capture, because this pattern provides a single, unified ingestion endpoint for both streaming POS data and batch offline uploads, eliminating the need for separate pipelines. By using the Kafka protocol, Event Hubs can handle high-throughput streaming and replayable batch data in one namespace, while Event Hubs Capture automatically lands the raw data into Azure Data Lake Storage for cost-effective batch processing, and Stream Analytics delivers real-time dashboards. On the DP-203 exam, this scenario tests your understanding of unified ingestion patterns and the trade-off between simplicity and scalability—a common trap is to split streaming and batch into separate services like IoT Hub and Blob Storage, which increases cost and complexity. Remember the memory tip: “One Hub to rule them all, Capture for the batch call.”
DP-203 Hybrid ingestion for streaming and batch Practice Question
This DP-203 practice question tests your understanding of design and 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 for a retail company. The solution must ingest streaming sales data from point-of-sale (POS) systems and batch uploads from stores that are offline. The total data volume is 5 TB daily. The solution must allow real-time dashboards and periodic batch processing. Which combination of services and ingestion patterns is most cost-effective and scalable?
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 Event Hubs with Kafka protocol for all incoming data. Use Stream Analytics for real-time dashboards and Event Hubs Capture to land data in ADLS for batch processing
Option B is correct because Azure Event Hubs with Kafka protocol provides a unified ingestion endpoint for both streaming POS data and batch offline uploads, eliminating the need for separate services. Stream Analytics enables real-time dashboards, while Event Hubs Capture automatically lands data into Azure Data Lake Storage for cost-effective batch processing, making this the most scalable and cost-effective solution for 5 TB daily.
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 IoT Hub for POS streaming and Azure Blob Storage for offline store uploads, then process with Stream Analytics and Data Factory
Why it's wrong here
Two separate ingestion services increase management overhead and cost; IoT Hub is designed for device-to-cloud, not necessarily POS systems.
- ✗
Use Azure Data Lake Storage for all data, then use Azure Databricks structured streaming for real-time and batch
Why it's wrong here
ADLS is storage, not an ingestion service; Databricks structured streaming can read from ADLS but not directly ingest streaming data without a queue.
- ✗
Use Azure Stream Analytics directly on POS data and store offline uploads in Blob Storage, then batch process with U-SQL
Why it's wrong here
Stream Analytics cannot directly ingest from POS systems without a messaging layer; offline store uploads still need ingestion.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-203 exam frequently reuses these exact scenarios with slightly different constraints.
✓Use Azure Event Hubs with Kafka protocol for all incoming data. Use Stream Analytics for real-time dashboards and Event Hubs Capture to land data in ADLS for batch processingCorrect answer▾
✗Use Azure IoT Hub for POS streaming and Azure Blob Storage for offline store uploads, then process with Stream Analytics and Data FactoryWrong answer — click to see why▾
Why this is wrong here
Two separate ingestion services increase management overhead and cost; IoT Hub is designed for device-to-cloud, not necessarily POS systems.
✗Use Azure Data Lake Storage for all data, then use Azure Databricks structured streaming for real-time and batchWrong answer — click to see why▾
Why this is wrong here
ADLS is storage, not an ingestion service; Databricks structured streaming can read from ADLS but not directly ingest streaming data without a queue.
✗Use Azure Stream Analytics directly on POS data and store offline uploads in Blob Storage, then batch process with U-SQLWrong answer — click to see why▾
Why this is wrong here
Stream Analytics cannot directly ingest from POS systems without a messaging layer; offline store uploads still need ingestion.
Analysis generated from the official DP-203blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume IoT Hub is required for streaming data, but Event Hubs with Kafka protocol is more cost-effective and scalable for high-volume POS data, and they overlook Event Hubs Capture as a built-in mechanism for batch landing.
Detailed technical explanation
How to think about this question
Event Hubs Capture works by automatically writing ingested events to Azure Data Lake Storage or Blob Storage in Avro format at a configurable time or size interval, enabling seamless batch processing without additional code. The Kafka protocol support in Event Hubs allows existing Kafka producers (e.g., from POS systems) to connect without protocol translation, ensuring low-latency ingestion. For 5 TB daily, Event Hubs throughput units (TUs) or processing units (PUs) can be auto-scaled, and Capture writes data in parallel to ADLS, optimizing cost by separating hot (streaming) and cold (batch) paths.
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
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FAQ
Questions learners often ask
What does this DP-203 question test?
Design and develop data processing — This question tests Design and develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Azure Event Hubs with Kafka protocol for all incoming data. Use Stream Analytics for real-time dashboards and Event Hubs Capture to land data in ADLS for batch processing — Option B is correct because Azure Event Hubs with Kafka protocol provides a unified ingestion endpoint for both streaming POS data and batch offline uploads, eliminating the need for separate services. Stream Analytics enables real-time dashboards, while Event Hubs Capture automatically lands data into Azure Data Lake Storage for cost-effective batch processing, making this the most scalable and cost-effective solution for 5 TB daily.
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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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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