Secure, monitor, and optimize data storage and data processing →mediumMultiple SelectObjective-mapped
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring the performance of an Azure Data Factory pipeline that uses a Copy activity to load data into Azure Synapse Analytics. Which THREE metrics should you monitor to identify potential performance bottlenecks?
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
✓
Throughput (data read/written per second).
Options A, D, and E are correct. Throughput (data read/written per second), Copy activity duration, and Data read and written metrics are direct indicators of performance bottlenecks in the Copy activity. Option B is incorrect because Integration runtime CPU utilization is not a standard metric exposed by Azure Data Factory for monitoring Copy activity performance; it reflects runtime resource usage but not directly the data transfer performance. Option C is incorrect because Pipeline run duration includes overhead from orchestration, such as pipeline activity scheduling and coordination, and is not a precise measure of the Copy activity's data transfer performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Throughput (data read/written per second).
Why this is correct
Throughput indicates the speed of data transfer.
- ✗
Integration runtime CPU utilization.
Why it's wrong here
CPU utilization is not exposed as a metric for Azure Data Factory.
- ✗
Pipeline run duration.
Why it's wrong here
Pipeline run duration includes orchestration time, not just copy performance.
- ✓
Copy activity duration.
Why this is correct
This directly measures the time taken for the copy operation.
- ✓
Data read and written metrics.
Why this is correct
These show the volume of data being processed.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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.