DP-203 Design and implement data storage Practice Question
A data engineering team uses Azure Data Factory to load data from Azure SQL Database to Azure Data Lake Storage Gen2. They notice that the pipeline runs fail intermittently due to transient errors. They need to implement a retry policy with exponential backoff. What is the most efficient way to achieve this?
⚠ Common exam trap
The trap here is that candidates may overcomplicate the solution by choosing a custom loop or validation activity, overlooking that Azure Data Factory's native 'Retry' property with exponential backoff is the simplest and most efficient built-in mechanism for handling transient errors.
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
✓
Configure the 'Retry' property on the copy activity with a count and exponential backoff interval
Azure Data Factory natively supports configuring a 'Retry' property on activities, including Copy activities, with an exponential backoff interval. This built-in mechanism automatically retries the activity upon transient failures without requiring custom logic, making it the most efficient and maintainable approach for handling intermittent errors.
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 a 'Validation' activity before the copy to check source availability
Why it's wrong here
A Validation activity only checks whether a dataset exists or a condition is met; it cannot retry a copy that fails mid-execution on a transient SQL error. It is tempting because pre-flight checks catch missing sources, but the requirement is automatic retry with exponential backoff on the copy activity itself.
- ✗
Create a custom .NET activity to handle retries
Why it's wrong here
A custom .NET activity requires authoring, deploying and maintaining Azure Batch-linked code, and it still would not integrate with the copy activity's built-in retry semantics. It is tempting when built-in activities lack a capability, but here the copy activity's retry and retryIntervalInSeconds properties already deliver the required backoff.
- ✗
Add a 'Until' loop with a wait activity in the pipeline
Why it's wrong here
An Until loop with a Wait activity re-runs the whole pipeline on a fixed interval, adding latency and consuming activity runs rather than retrying the failed copy at the source. It is tempting because Until loops do implement polling patterns, but Data Factory's copy activity already exposes retry and retryIntervalInSeconds properties for transient faults.
- ✓
Configure the 'Retry' property on the copy activity with a count and exponential backoff interval
Why this is correct
The copy activity's retry property automatically reattempts failed runs, and its exponential backoff interval progressively lengthens the delay between attempts, absorbing transient faults without custom pipeline logic. This is the native, most efficient mechanism for the stated requirement.
Go deeper
Related to this question
About these practice questions
Courseiva writes every DP-203 question from scratch — 509 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.