DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You manage an Azure Data Factory pipeline that copies data from an on-premises SQL Server to Azure Data Lake Storage Gen2. The pipeline runs daily and completes successfully. You need to be alerted when the pipeline duration exceeds 60 minutes. You want to minimize administrative effort. What should you do?
⚠ Common exam trap
The trap here is assuming that Azure Monitor metric alerts can directly monitor pipeline duration, when duration is only available in activity logs requiring a log search alert.
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
✓
Configure a diagnostic setting to send activity logs to a Log Analytics workspace, then create a log search alert rule using a query that filters for pipeline runs with duration greater than 60 minutes.
To alert on pipeline duration, you need access to run-level logs that include the Duration property. Diagnostic settings export Azure Data Factory activity logs to Log Analytics, where you can write a query to find runs exceeding 60 minutes and attach an alert rule. This is the least-effort, native Azure solution. Metric-based alerts on pipeline metrics like ADFPipelineRun or Failed Runs do not capture duration, and embedding custom activities adds unnecessary complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a Web activity in the pipeline that calls a Logic App to send an email if the pipeline runs longer than 60 minutes.
Why it's wrong here
A Web activity inside the pipeline would need to evaluate elapsed time during execution, which is complex and error-prone. The pipeline itself cannot easily know its total duration while still running. Moreover, this requires building and maintaining a Logic App workflow, increasing administrative effort. The requirement is to minimize effort, and this solution adds unnecessary custom components and maintenance overhead.
- ✓
Configure a diagnostic setting to send activity logs to a Log Analytics workspace, then create a log search alert rule using a query that filters for pipeline runs with duration greater than 60 minutes.
Why this is correct
Diagnostic settings route Azure Data Factory activity logs, including pipeline run records with a Duration property, to Log Analytics. A log search alert rule can run a scheduled query such as ADFPipelineRun | where Duration > 60m and trigger an action group when results are found. This is the standard, low-effort method to alert on pipeline duration without custom code or external monitoring.
- ✗
Create an Azure Monitor alert rule on the ADFPipelineRun metric with a threshold of 60 minutes.
Why it's wrong here
The ADFPipelineRun metric reports the number of pipeline runs, not their durations. Azure Monitor metric alerts evaluate numeric metrics over time, and there is no duration metric for pipeline runs. To alert on duration, you must use log-based alerts that query the ADFPipelineRun activity logs, where duration is a property of each run record. Therefore, this approach cannot detect long-running pipelines.
- ✗
Enable Azure Monitor alerts on the Failed Runs metric of the pipeline and set the threshold to 1.
Why it's wrong here
The Failed Runs metric counts failed pipeline runs, not long-running ones. A pipeline that exceeds 60 minutes but completes successfully will not increment this metric. Setting a threshold on Failed Runs would only alert on failures, which is not the stated requirement. This approach does not address duration and would generate false positives if a failure occurs for unrelated reasons.
Go deeper
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Implement Data Encryption and Access Control
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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