DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your Azure Data Factory pipeline uses a Copy activity to load data from an on-premises SQL Server to Azure Blob Storage. You notice that the pipeline is running slower than expected. You need to identify the bottleneck. Which Data Factory monitoring metric should you analyze first?
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
✓
Data Integration Unit (DIU) consumption
Data Integration Unit (DIU) consumption indicates whether the Copy activity is resource-bound and helps identify performance bottlenecks. Option A is wrong because source queue length reflects integration runtime queue depth, not a direct metric for Copy activity throughput. Option B is wrong because pipeline duration is a result of performance issues, not a metric to pinpoint the bottleneck first. Option C is wrong because activity run count is unrelated to performance analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Source queue length
Why it's wrong here
This metric is for self-hosted integration runtime queue, not Copy throughput.
- ✗
Pipeline duration
Why it's wrong here
Duration is an outcome, not a diagnostic metric for bottleneck identification.
- ✗
Activity run count
Why it's wrong here
Count does not indicate performance issues.
- ✓
Data Integration Unit (DIU) consumption
Why this is correct
High DIU consumption indicates the Copy activity is resource-constrained.
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.