- A
Use Blob Storage event triggers to invoke Azure Functions
Why wrong: Event triggers are for real-time processing, not batch incremental processing with late-arriving data.
- B
Use Azure Synapse Pipelines with a schedule and full load each time
Why wrong: Full loads are inefficient and not incremental; they do not handle late-arriving data efficiently.
- C
Use Azure Data Factory with watermark columns in the source
Why wrong: Watermark columns require a column indicating last modified time, which may not exist in file-based sources.
- D
Store processed file names in a Delta table and compare with source folder listing
Delta table provides a reliable way to track processed files and can be updated incrementally.
Question 237 of 851
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 data engineering team is designing a batch processing pipeline that reads from Azure Data Lake Storage Gen2, transforms data using Azure Databricks, and writes to Azure Synapse Analytics. The pipeline must process data incrementally and handle late-arriving data up to 2 hours. Which approach should they use to track processed files?
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
✓
Store processed file names in a Delta table and compare with source folder listing
Option D is correct because storing processed file names in a Delta table allows the pipeline to track which files have already been ingested, supporting incremental processing and handling late-arriving data up to 2 hours. By comparing the current source folder listing against the Delta table, the pipeline can identify only new or late-arriving files, avoiding reprocessing and ensuring exactly-once semantics. This approach integrates seamlessly with Azure Databricks and Delta Lake's ACID transactions, providing reliable state management for batch pipelines.
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 Blob Storage event triggers to invoke Azure Functions
Why it's wrong here
Event triggers are for real-time processing, not batch incremental processing with late-arriving data.
- ✗
Use Azure Synapse Pipelines with a schedule and full load each time
Why it's wrong here
Full loads are inefficient and not incremental; they do not handle late-arriving data efficiently.
- ✗
Use Azure Data Factory with watermark columns in the source
Why it's wrong here
Watermark columns require a column indicating last modified time, which may not exist in file-based sources.
- ✓
Store processed file names in a Delta table and compare with source folder listing
Why this is correct
Delta table provides a reliable way to track processed files and can be updated incrementally.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Azure Data Factory with watermark columns (Option C) because it is a common incremental load pattern, but they overlook that watermark columns apply to row-based sources with change tracking, not to file-based sources where the challenge is tracking which files have been processed.
Detailed technical explanation
How to think about this question
Delta Lake uses a transaction log to record metadata about files, enabling time travel and ACID-compliant state tracking. When storing processed file names in a Delta table, the pipeline can leverage Delta's `MERGE` or `INSERT` operations to atomically update the tracking table, and use `LIST` or `dbutils.fs.ls` to enumerate the source folder, then perform an anti-join to find unprocessed files. This pattern is robust for late-arriving data because the tracking table persists across runs and can be queried with a timestamp filter to re-process files that arrived within the 2-hour window.
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.
Visual reference
What to study next
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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: Store processed file names in a Delta table and compare with source folder listing — Option D is correct because storing processed file names in a Delta table allows the pipeline to track which files have already been ingested, supporting incremental processing and handling late-arriving data up to 2 hours. By comparing the current source folder listing against the Delta table, the pipeline can identify only new or late-arriving files, avoiding reprocessing and ensuring exactly-once semantics. This approach integrates seamlessly with Azure Databricks and Delta Lake's ACID transactions, providing reliable state management for batch pipelines.
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: Jul 4, 2026
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