DP-900 Describe core data concepts Practice Question
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?
⚠ Common exam trap
Many 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.
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 retry policy with exponential backoff on activities
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.
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 is a serverless, event-driven compute service intended for lightweight, short-running integrations, not for large-scale data transformation workloads that Databricks is designed to handle. Swapping the compute engine does nothing to add retry capability to the Data Factory activity that is failing on transient errors; you would still need to implement retries and backoff manually. Therefore, this change neither addresses the symptom nor the root cause of the intermittent failures.
- ✗
Increase the pipeline timeout to 24 hours
Why it's wrong here
Increasing the pipeline timeout simply tells Azure Data Factory how long to wait before declaring a failure; it does not instruct the activity to attempt the operation again after an error. A transient network blip or throttling response can still cause the activity to fail immediately on its single attempt, making the longer timeout irrelevant. You must add an explicit retry policy to recover from such intermittent errors, not just more time for the same attempt.
- ✗
Split the pipeline into multiple smaller pipelines
Why it's wrong here
Splitting a monolithic pipeline into several smaller pipelines is a sound modularity and blast-radius-reduction practice, but each new pipeline still has the same activity-level failure behavior. A transient error occurring inside one smaller activity will cause that specific pipeline run to fail just as before, since no retry has been configured. This only improves isolation and debugging; it does not add resilience to transient faults.
- ✓
Configure retry policy with exponential backoff on activities
Why this is correct
Configuring a retry policy on the failing activity causes Azure Data Factory to automatically re-attempt the operation when it detects an error, and adding exponential backoff spaces out those attempts so the transient condition (such as throttling or a temporary service outage) has time to clear. With a retry count and interval set on the activity, you avoid hard failures caused by intermittent issues without manual intervention. This is the standard, directly-scoped solution for transient errors in ADF pipelines.
Visual reference
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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
Dedicated SQL pool
A Dedicated SQL pool is a cloud-based analytics service in Azure Synapse Analytics that provides a managed, scalable environment for running large-scale data warehousing queries using Transact-SQL.
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