Secure, monitor, and optimize data storage and data processing →mediumMultiple ChoiceObjective-mapped
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your company has an Azure Data Factory pipeline that ingests data from multiple sources into Azure Data Lake Storage Gen2. The pipeline uses a self-hosted integration runtime (IR) running on an on-premises Windows server. Recently, the pipeline started failing with 'Connection timed out' errors during peak hours. You suspect network congestion. You need to resolve this issue with minimal cost and without modifying the pipeline activities. What should you do?
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
✓
Scale out the self-hosted IR by adding more nodes to the cluster.
Scaling out the self-hosted IR by adding more nodes distributes the load and reduces timeout issues. Option A is wrong because Azure ExpressRoute provides dedicated bandwidth but is costly and overkill for this scenario. Option B is wrong because increasing the polling interval does not fix network timeouts; it only changes how often the activity checks for data. Option D is wrong because migrating to Azure-SSIS IR is expensive and unnecessary for this pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement Azure ExpressRoute to provide dedicated bandwidth.
Why it's wrong here
Overkill and costly for this issue.
- ✗
Increase the 'Polling Interval' setting in the copy activity.
Why it's wrong here
Polling interval affects status check, not connectivity.
- ✓
Scale out the self-hosted IR by adding more nodes to the cluster.
Why this is correct
Distributes load and improves throughput.
- ✗
Migrate the self-hosted IR to Azure-SSIS IR.
Why it's wrong here
Expensive and not a direct solution for load.
Visual reference
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.