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?
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
The correct configuration is to set the retry property to 3 and retryIntervalInSeconds to 300 in the activity policy. This ensures up to three retries with a 5-minute (300-second) delay between each attempt. Option A is incorrect because Azure Data Factory supports a fixed retry interval, not exponential backoff via the Retry policy. Option C is incorrect because timeout is separate from retry configuration; retry is configured at the activity level. Option D is incorrect because the correct property name is 'retry' not 'maxRetries', and the delay is specified in seconds as 'retryIntervalInSeconds'.
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 backoff is not a property; retry is simply a number.
- ✓
Set retry to 3 and retryIntervalInSeconds to 300 in the activity policy
Why this is correct
This configures 3 retries with 300 seconds (5 minutes) interval.
- ✗
Set the activity timeout to 15 minutes and enable retry
Why it's wrong here
Timeout is not related to retries.
- ✗
Set maxRetries to 3 and delay to 5 minutes in the pipeline JSON
Why it's wrong here
No 'maxRetries' property; it's 'retry'.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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