DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring an Azure Data Factory pipeline that copies data from an on-premises SQL Server to Azure Blob Storage. You notice frequent failures due to transient network errors. Which TWO actions should you take to improve reliability?
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
✓
Enable fault tolerance in the copy activity to skip incompatible rows.
Options C and D are correct. Enabling fault tolerance allows the copy activity to skip incompatible rows and continue, while configuring a retry policy automatically retries the activity on failure due to transient errors. Option A is incorrect because deploying a self-hosted IR in Azure does not address transient network errors; it is used for connectivity to on-prem data stores. Option B is incorrect because staged copy is for copying large datasets efficiently, not for handling transient errors. Option E is incorrect because increasing parallelism improves throughput but does not improve reliability against transient failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a self-hosted integration runtime on a VM in Azure.
Why it's wrong here
Self-hosted IR is for on-premises connectivity, not retry.
- ✗
Use staged copy with Azure Data Lake as intermediate storage.
Why it's wrong here
Staging helps with large data, not transient errors.
- ✓
Enable fault tolerance in the copy activity to skip incompatible rows.
Why this is correct
Fault tolerance allows pipeline to continue despite errors.
- ✓
Configure a retry policy on the copy activity.
Why this is correct
Retries handle transient failures.
- ✗
Increase the degree of copy parallelism.
Why it's wrong here
Parallelism increases throughput, not reliability.
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
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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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.