The correct choice is a tumbling window trigger with a window size of 1 day and a late arrival window of 2 hours, because this configuration directly addresses the need for idempotent batch processing while accommodating late-arriving data. A tumbling window trigger in Azure Data Factory processes data in fixed, non-overlapping intervals, and the late arrival window allows the pipeline to automatically rerun the same window when files arrive after the scheduled time—up to the specified 2-hour delay—ensuring no data is missed and the transformation remains idempotent. On the DP-203 exam, this scenario tests your understanding of how to design fault-tolerant batch pipelines that handle data latency without duplication; a common trap is choosing a scheduled trigger, which lacks the automatic rerun capability for late files. Remember the memory tip: “Tumble and wait—late data won’t be late if you set the window to 2.”
DP-203 Design and develop data processing 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.
A data engineer is designing a batch processing pipeline that reads data from Azure Blob Storage, transforms it using Azure Databricks, and writes the output to Azure Synapse Analytics. The source files are in CSV format and arrive daily at 02:00 UTC. The transformation must be idempotent and the pipeline should handle late-arriving data (up to 2 hours). What is the best approach to trigger the pipeline?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours
Option C is correct because a tumbling window trigger in Azure Data Factory allows you to define a fixed-size window (1 day) and a late arrival window (2 hours), which ensures idempotent processing by automatically rerunning the window for late-arriving data within the specified delay. This matches the requirement for daily batch processing at 02:00 UTC while handling data arriving up to 2 hours late.
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.
✗
Storage event trigger using Azure Event Grid
Why it's wrong here
Similar to event trigger, may cause duplicate processing.
✗
Schedule trigger set to 02:00 UTC daily
Why it's wrong here
Does not handle late-arriving data.
✓
Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours
Why this is correct
Ensures idempotency and handles late data by allowing up to 2 hours delay.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Event trigger on blob creation in the container
Why it's wrong here
May fire multiple times for the same file and requires deduplication logic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between schedule triggers (fixed time) and tumbling window triggers (window-based with late arrival handling), where candidates mistakenly choose a simple schedule trigger because they overlook the late-arriving data requirement.
Trap categories for this question
Similar concept trap
Similar to event trigger, may cause duplicate processing.
Detailed technical explanation
How to think about this question
Tumbling window triggers in Azure Data Factory use a fixed-size window (e.g., 1 day) and automatically compute the window start and end times based on the trigger frequency. The late arrival window allows the trigger to rerun the window for data that arrives within the specified delay, ensuring all data is processed exactly once within that window. This is implemented via the 'maxConcurrency' and 'retryPolicy' settings, which prevent overlapping runs and handle transient failures.
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 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: Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours — Option C is correct because a tumbling window trigger in Azure Data Factory allows you to define a fixed-size window (1 day) and a late arrival window (2 hours), which ensures idempotent processing by automatically rerunning the window for late-arriving data within the specified delay. This matches the requirement for daily batch processing at 02:00 UTC while handling data arriving up to 2 hours late.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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