DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You need to monitor the performance of an Azure Data Factory pipeline that copies data from an on-premises SQL Server to Azure Blob Storage. The pipeline runs on a self-hosted integration runtime. Which metric is most important to monitor to ensure the self-hosted IR is not a bottleneck?
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
✓
Queue depth for the self-hosted IR
Queue depth for the self-hosted IR is the most important metric to monitor because it indicates how many activities are queued waiting for the IR to process. A high queue depth suggests the IR is overloaded and becoming a bottleneck. In contrast, pipeline duration (A) reflects total time but does not isolate IR performance. Active connections (C) measure concurrent loads but not queuing. Data read/written (D) track throughput, but a bottleneck may appear even with high throughput if the IR cannot keep up.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pipeline duration metric
Why it's wrong here
Pipeline duration reflects overall runtime, not the IR's health specifically.
- ✓
Queue depth for the self-hosted IR
Why this is correct
High queue depth indicates the IR is unable to process activities quickly enough.
- ✗
Number of active connections to the IR
Why it's wrong here
Connection count does not directly measure processing capacity.
- ✗
Data read and data written metrics for the pipeline
Why it's wrong here
These metrics show throughput but do not indicate if the IR is the bottleneck.
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
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.
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.
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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.