- A
Use Azure Databricks Auto Loader with Delta Live Tables to ingest streaming data, and use Change Data Capture from Cosmos DB to update the reference data inline.
Why wrong: Change Data Capture adds unnecessary complexity for infrequent updates.
- B
Use Azure Data Factory to copy data from Event Hubs to Azure Data Lake Storage Gen2 in batches, then use Azure Databricks to process and enrich with Cosmos DB.
Why wrong: Batch processing increases latency and complexity.
- C
Use Azure Stream Analytics to ingest from Event Hubs, join with Cosmos DB reference data, and output to Azure Data Lake Storage Gen2 in Parquet format.
Why wrong: Adds extra service and cost without benefit.
- D
Use Azure Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.
Simplifies processing and handles schema evolution.
Quick Answer
The answer is to use Azure Databricks Structured Streaming to read from Event Hubs, perform a streaming static join with reference data from Cosmos DB, and write to Delta Lake with schema evolution enabled. This approach is correct because a streaming static join efficiently enriches the live Event Hubs stream with infrequently updated reference data without the overhead of Change Data Capture or additional services, while Delta Lake’s schema evolution handles changes in the patient data structure automatically. On the DP-203 exam, this scenario tests your ability to balance fault tolerance, cost, and complexity in a streaming pipeline—common traps include over-engineering with Azure Stream Analytics (which adds cost and integration overhead) or opting for batch processing (which increases latency). Remember the memory tip: “Static join for static ref, schema evolution for change.” This keeps your pipeline lean and resilient.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. 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.
You are a data engineer for a healthcare company that processes patient data. You have an Azure Databricks workspace with a cluster configured for data processing. You need to implement a solution that processes streaming data from Azure Event Hubs, enriches it with reference data stored in Azure Cosmos DB, and writes the output to Delta Lake in Azure Data Lake Storage Gen2. The solution must ensure that the data processing is fault-tolerant and can handle schema evolution. The reference data is updated infrequently. You need to choose an approach that minimizes complexity and cost. What should you do?
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 Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.
Option B is correct because Auto Loader can incrementally process streaming data from Event Hubs (via Event Hubs connector), and it can automatically handle schema evolution. Broadening the reference data join to use a streaming static join in Structured Streaming is efficient for infrequently updated reference data. Option A is wrong because using Change Data Capture adds complexity. Option C is wrong because using Azure Stream Analytics would require additional service cost and integration. Option D is wrong because using batch processing for streaming data would increase latency.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 Databricks Auto Loader with Delta Live Tables to ingest streaming data, and use Change Data Capture from Cosmos DB to update the reference data inline.
Why it's wrong here
Change Data Capture adds unnecessary complexity for infrequent updates.
- ✗
Use Azure Data Factory to copy data from Event Hubs to Azure Data Lake Storage Gen2 in batches, then use Azure Databricks to process and enrich with Cosmos DB.
Why it's wrong here
Batch processing increases latency and complexity.
- ✗
Use Azure Stream Analytics to ingest from Event Hubs, join with Cosmos DB reference data, and output to Azure Data Lake Storage Gen2 in Parquet format.
Why it's wrong here
Adds extra service and cost without benefit.
- ✓
Use Azure Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.
Why this is correct
Simplifies processing and handles schema evolution.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
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Develop data processing — study guide chapter
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Develop data processing practice questions
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FAQ
Questions learners often ask
What does this DP-203 question test?
Develop data processing — This question tests Develop data processing — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use Azure Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table. — Option B is correct because Auto Loader can incrementally process streaming data from Event Hubs (via Event Hubs connector), and it can automatically handle schema evolution. Broadening the reference data join to use a streaming static join in Structured Streaming is efficient for infrequently updated reference data. Option A is wrong because using Change Data Capture adds complexity. Option C is wrong because using Azure Stream Analytics would require additional service cost and integration. Option D is wrong because using batch processing for streaming data would increase latency.
What should I do if I get this DP-203 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 21, 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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