- A
Delta Lake
Provides ACID transactions and supports exactly-once semantics.
- B
Azure Databricks Structured Streaming
Supports watermarking and event-time processing for late data.
- C
PolyBase
Why wrong: Used for batch loading, not streaming.
- D
Azure Stream Analytics
Why wrong: Does not provide native exactly-once semantics with Delta Lake.
- E
Azure Data Factory
Why wrong: Not designed for streaming processing.
Quick Answer
The answer is Azure Databricks Structured Streaming and Delta Lake. Delta Lake’s ACID transactions and schema enforcement provide the foundation for exactly-once semantics by ensuring that each record is written only once, even during failures or retries. Structured Streaming complements this by handling late-arriving data through watermarking and event-time processing, which allows the system to define a threshold for lateness and correctly aggregate or join delayed records without duplication. On the DP-203 exam, this pairing tests your understanding of how to combine a streaming engine with a transactional storage layer to achieve end-to-end reliability in Azure Databricks. A common trap is choosing Azure Stream Analytics, which lacks native Delta Lake integration for exactly-once guarantees, or Azure Data Factory, which is batch-only. Memory tip: think “Delta delivers durability, Streaming sorts the stragglers”—Delta locks the write, Streaming waits for the late.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of 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 that uses Azure Databricks. The solution needs to process streaming sales data from Event Hubs and batch data from Azure Data Lake Storage Gen2. You need to ensure that the solution can handle late-arriving data and maintain exactly-once semantics. Which TWO technologies should you use?
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
Delta Lake
Correct answers: A and D. Delta Lake provides ACID transactions and schema enforcement, enabling exactly-once semantics. Azure Databricks Structured Streaming can handle late-arriving data using watermarking and event-time processing. Option B is wrong because Azure Stream Analytics does not integrate natively with Delta Lake for exactly-once semantics. Option C is wrong because Azure Data Factory is not suitable for streaming processing. Option E is wrong because PolyBase is for batch loading, not streaming.
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.
- ✓
Delta Lake
Why this is correct
Provides ACID transactions and supports exactly-once semantics.
Related concept
Static NAT maps one inside address to one outside address.
- ✓
Azure Databricks Structured Streaming
Why this is correct
Supports watermarking and event-time processing for late data.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
PolyBase
Why it's wrong here
Used for batch loading, not streaming.
- ✗
Azure Stream Analytics
Why it's wrong here
Does not provide native exactly-once semantics with Delta Lake.
- ✗
Azure Data Factory
Why it's wrong here
Not designed for streaming processing.
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 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.
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: Delta Lake — Correct answers: A and D. Delta Lake provides ACID transactions and schema enforcement, enabling exactly-once semantics. Azure Databricks Structured Streaming can handle late-arriving data using watermarking and event-time processing. Option B is wrong because Azure Stream Analytics does not integrate natively with Delta Lake for exactly-once semantics. Option C is wrong because Azure Data Factory is not suitable for streaming processing. Option E is wrong because PolyBase is for batch loading, not streaming.
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
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