DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring an Azure Data Factory pipeline that runs daily. You notice that some runs are failing due to transient network errors. You want to automatically retry the failed activities with a 5-minute delay, up to 3 times. How should you configure this?
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
✓
On each activity, set 'Retry' to 3 and 'Retry interval' to 00:05:00.
Azure Data Factory activities have a 'Retry' property that can be set to 3, and 'Retry interval' to 00:05:00. Option A is too low (1 retry). Option B is wrong because retry is per activity, not at pipeline level. Option D is wrong because the default retry is 0.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the pipeline's 'Concurrency' to 3 and 'Retry' to 1.
Why it's wrong here
Incorrect: Concurrency controls max simultaneous runs, not retry. 1 retry is insufficient.
- ✗
Leave the default settings as they are because Azure Data Factory automatically retries failed activities 3 times.
Why it's wrong here
Incorrect: Default retry is 0, not 3.
- ✓
On each activity, set 'Retry' to 3 and 'Retry interval' to 00:05:00.
Why this is correct
Correct: Activities have individual retry settings. Setting retry to 3 with 5-minute interval achieves the requirement.
- ✗
Configure a 'Retry' policy on the pipeline itself, setting maximum retries to 3 and retry interval to 5 minutes.
Why it's wrong here
Incorrect: Pipelines do not have a retry property; retry is configured on each activity.
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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Same concept, more angles
1 more way this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You have an Azure Data Factory pipeline that copies data from an FTP server to Azure Blob Storage. The pipeline runs successfully most of the time, but occasionally fails with a 'FTP server connection refused' error during peak hours. You need to minimize these failures with minimal cost. What should you do?
easy- ✓ A.Add a retry policy to the copy activity with a backoff interval.
- B.Set up Azure ExpressRoute to improve network reliability.
- C.Migrate the FTP server to SFTP.
- D.Increase the parallel copy count in the copy activity.
Why A: Adding a retry policy with a backoff interval handles transient connection issues. Option D is wrong because increasing parallel copies does not solve connection refused errors. Option B is wrong because Azure ExpressRoute is costly and unnecessary for FTP. Option C is wrong because migrating to SFTP may not resolve connection refused if the server is overloaded.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.