DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You need to ensure that an Azure Data Factory pipeline retries a failed activity up to three times with a 5-minute delay between retries. How should you configure the activity?
⚠ Common exam trap
DP-203 often tests the exact ADF property names — candidates pick 'maxRetries' or 'Exponential' because those names appear in other Azure services, but ADF specifically uses 'retry' and 'retryIntervalInSeconds'.
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
✓
Set retry to 3 and retryIntervalInSeconds to 300 in the activity policy
Azure Data Factory activity policies expose two retry-related properties: 'retry' (an integer count) and 'retryIntervalInSeconds' (the fixed delay between attempts). Setting retry to 3 and retryIntervalInSeconds to 300 produces exactly three retries with a 5-minute (300-second) fixed interval, matching the requirement. This is configured on the activity's policy object in the pipeline JSON or via the Author UI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Retry policy on the pipeline activity as 'Exponential' with count 3
Why it's wrong here
Exponential retry uses a growing backoff interval, not a fixed 5-minute delay between attempts. It is tempting because exponential policies are the standard resilience pattern for transient throttling, and would be correct where the requirement specified increasing intervals rather than a constant one.
- ✓
Set retry to 3 and retryIntervalInSeconds to 300 in the activity policy
Why this is correct
The activity policy's retry property sets the maximum retry count, and retryIntervalInSeconds defines the pause between attempts. Setting retry to 3 and retryIntervalInSeconds to 300 yields three retries at five-minute intervals, exactly matching the stated requirement.
- ✗
Set the activity timeout to 15 minutes and enable retry
Why it's wrong here
The timeout setting bounds total activity execution duration; it does not schedule retries or define the interval between them. It is tempting because extending timeout is a common fix for long-running activities, and would be correct where the failure cause is an activity exceeding its default execution limit.
- ✗
Set maxRetries to 3 and delay to 5 minutes in the pipeline JSON
Why it's wrong here
Data Factory's activity retry interval is set through the Retry policy's intervalInSeconds property, not a 'delay' key, so this JSON would not apply the 5-minute spacing. It is tempting because hand-editing pipeline JSON is a legitimate configuration route, and would be correct if the property names matched the schema.
Go deeper
Related to this question
Learn chapter
Implement Azure Data Factory Pipelines
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.