The correct choice is to configure a retry policy with exponential backoff on the Azure Data Factory activities. This pattern directly improves pipeline reliability for transient errors—such as temporary network blips or service throttling—by automatically retrying the failed activity after progressively longer delays, which prevents overwhelming the source or sink systems. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of built-in fault-handling mechanisms in Azure Data Factory, often appearing as a scenario where you must distinguish between retry policies and architectural changes like adding a dead-letter queue. A common trap is choosing a manual retry or a fixed-interval retry, but exponential backoff is specifically designed for intermittent failures because it respects system recovery time. Remember the mnemonic “R-E-T-R-Y: Retry Exponentially, Then Relax, Yield” to recall that delays increase after each attempt.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company's data engineering team uses Azure Data Factory to orchestrate a pipeline that ingests data from Azure Blob Storage, transforms it using Azure Databricks, and loads it into Azure Synapse Dedicated SQL Pool. The pipeline fails intermittently due to transient errors. Which pattern should they implement to improve reliability?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure retry policy with exponential backoff on activities
Option D is correct because configuring a retry policy with exponential backoff on the Azure Data Factory activities directly addresses transient errors (e.g., network blips, throttling) by automatically retrying the failed activity after increasing delays. This pattern is specifically designed for intermittent failures and is a built-in feature of Azure Data Factory, improving pipeline reliability without architectural changes.
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.
✗
Replace Azure Databricks with Azure Functions
Why it's wrong here
Azure Functions are not designed for large-scale transformations and do not automatically retry.
✗
Increase the pipeline timeout to 24 hours
Why it's wrong here
Increasing timeout does not handle the transient errors; the activity still fails.
✗
Split the pipeline into multiple smaller pipelines
Why it's wrong here
Splitting pipelines reduces blast radius but does not retry on failures.
✓
Configure retry policy with exponential backoff on activities
Why this is correct
Retry policies with backoff automatically retry failed activities due to transient errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse increasing timeout (Option B) with retry logic, or think splitting pipelines (Option C) improves reliability against transient errors, when in fact only a retry policy with backoff directly mitigates intermittent failures in Azure Data Factory.
Detailed technical explanation
How to think about this question
Azure Data Factory's retry policy with exponential backoff uses a configurable interval (e.g., 60 seconds) that doubles with each retry attempt, up to a maximum interval (default 1 hour). This aligns with best practices for handling throttling from Azure Synapse Dedicated SQL Pool (e.g., 503 errors) or Blob Storage (e.g., 429 errors), where immediate retries would likely fail again. The retry count can be set from 0 to 1000, and the policy applies per activity, not the entire pipeline, allowing granular control.
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-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure retry policy with exponential backoff on activities — Option D is correct because configuring a retry policy with exponential backoff on the Azure Data Factory activities directly addresses transient errors (e.g., network blips, throttling) by automatically retrying the failed activity after increasing delays. This pattern is specifically designed for intermittent failures and is a built-in feature of Azure Data Factory, improving pipeline reliability without architectural changes.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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