DP-900 Describe an analytics workload on Azure Practice Question
Your company has a data pipeline in Azure Data Factory that runs daily. Recently, the pipeline started failing with timeouts. You suspect a downstream database is slow. What should you do to monitor and alert on pipeline run duration?
⚠ Common exam trap
It's easy for candidates to confuse reactive troubleshooting tools (like Log Analytics queries) with proactive monitoring and alerting capabilities, or mistakenly think Azure Service Health or Advisor are designed for pipeline-specific performance alerts.
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 alerts in Azure Monitor based on pipeline run duration
Azure Monitor can be configured to create alerts based on metrics such as pipeline run duration. When the duration exceeds a threshold, an alert triggers, allowing proactive notification of slow downstream databases. This directly addresses the need to monitor and alert on pipeline performance issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure alerts in Azure Monitor based on pipeline run duration
Why this is correct
Azure Data Factory pipeline runs emit metrics such as PipelineRun Succeeded, PipelineRun Failed, and PipelineRun Duration to Azure Monitor. You can create a metric alert rule that continuously evaluates this duration and triggers an action—like an email, webhook, or ITSM ticket—when a threshold is exceeded. This is a real-time, proactive monitoring approach because Azure Monitor collects these metrics natively without requiring any additional log configuration.
- ✗
Query the pipeline runs in Log Analytics
Why it's wrong here
Log Analytics is primarily a query engine for analyzing logs and metrics after they have been collected, not a notification service. To use it for alerts, you must first configure ADF diagnostic settings to send pipeline runs to a Log Analytics workspace, then you would still need to define a log alert rule in Azure Monitor using a KQL query. That alert rule is what notifies you; simply querying pipeline runs in Log Analytics only gives you historical, reactive visibility into past durations.
- ✗
Check Azure Service Health for issues
Why it's wrong here
Azure Service Health is focused on the health of Azure's underlying platform and infrastructure, reporting incidents like regional outages, network issues, or planned maintenance that can affect many customers. It does not contain telemetry from your specific Azure Data Factory pipeline runs, nor can it alert you about a custom threshold such as pipeline run duration. If a regional problem is slowing all pipelines, Service Health may explain it, but it cannot generate duration-specific pipeline alerts.
- ✗
Use Azure Advisor to check performance recommendations
Why it's wrong here
Azure Advisor is a recommendation engine that periodically reviews your Azure resources and suggests best-practice improvements in areas such as cost, performance, security, and reliability. It operates on historical configuration and usage data, offering optimization suggestions like choosing a different integration runtime size or tuning concurrency, but it does not provide real-time monitoring. Advisor will not notify you when your pipeline run duration crosses a threshold, so it is not a substitute for an Azure Monitor alert.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
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