MCQs on Monitoring and Debugging | Azure Data Factory MCQs Question
In this set of Azure Data Factory MCQs Question, we will explore essential topics related to Monitoring and Debugging in Azure Data Factory (ADF). This includes understanding how to monitor pipeline runs, diagnose errors with activity logs and metrics, configure alerts for failures and SLAs, and apply best practices for debugging. Additionally, we will cover how Azure Monitor and Log Analytics integrate with ADF for efficient troubleshooting and performance optimization.
Chapter 9: Monitoring and Debugging – MCQs
Topic 1: Pipeline Run Monitoring in ADF Studio
- What is the primary purpose of monitoring pipeline runs in ADF Studio?
a) To deploy new pipelines
b) To track the performance and success of pipeline executions
c) To configure linked services
d) To manage data flow transformations - Which feature in ADF Studio allows you to view the status of each pipeline run?
a) Activity Monitor
b) Pipeline Monitoring Dashboard
c) Azure Monitor integration
d) Real-time Log Streaming - What type of information can you gather from the pipeline run monitoring dashboard in ADF Studio?
a) Resource usage and billing data
b) Real-time performance and error messages
c) Data flow transformations
d) User access logs - Which of the following is NOT available in the pipeline monitoring interface in ADF Studio?
a) Activity execution status
b) Pipeline run history
c) Detailed cost analysis
d) Time taken for each activity execution - How can you filter pipeline runs based on specific criteria in ADF Studio?
a) By activity type
b) By run status
c) By pipeline execution time
d) All of the above - Which action can be performed directly from the pipeline run monitoring interface in ADF Studio?
a) Re-trigger failed pipeline runs
b) Create new linked services
c) Configure data flow transformations
d) Update access permissions
Topic 2: Diagnosing Errors with Activity Logs and Metrics
- What is the primary use of activity logs in Azure Data Factory?
a) To schedule data pipeline executions
b) To diagnose issues and track activity history
c) To monitor data storage utilization
d) To perform data transformations - How can you view detailed metrics for ADF pipeline activities?
a) Through Azure Monitor
b) By using the ADF portal dashboard
c) By querying Azure Log Analytics
d) All of the above - Which metric is typically used to monitor pipeline performance?
a) Data flow latency
b) CPU utilization of data flows
c) Pipeline run duration and success rate
d) Number of failed triggers - What type of error information can you find in ADF activity logs?
a) Data transformation failures
b) System alerts and warning messages
c) Resource consumption details
d) All of the above - How can you access activity logs for your Azure Data Factory instance?
a) Through the ADF portal
b) By accessing Azure Event Hub
c) By querying Azure Monitor logs
d) By enabling diagnostics in the Azure portal - What can you do if an activity fails in a pipeline, according to the activity log?
a) Update the activity’s parameters
b) Manually restart the pipeline
c) View detailed error messages for troubleshooting
d) All of the above
Topic 3: Configuring Alerts for Failures and SLAs
- What is the purpose of setting up alerts in Azure Data Factory?
a) To notify users when pipeline execution exceeds an SLA
b) To trigger automated scaling of compute resources
c) To schedule periodic pipeline executions
d) To automatically fix pipeline errors - Which of the following can you configure in Azure Data Factory to monitor pipeline failures?
a) Email notifications
b) Webhook alerts
c) SMS notifications
d) All of the above - How can you configure alerts for SLA breaches in ADF?
a) Using Azure Monitor Alerts
b) By setting pipeline run time thresholds
c) By integrating with Logic Apps
d) All of the above - What is a Service Level Agreement (SLA) in the context of ADF?
a) A contract specifying the acceptable duration for pipeline completion
b) A method for calculating pipeline run costs
c) A rule for data transformation
d) A configuration for integrating external systems - Which notification method can be used for real-time alerts in Azure Data Factory?
a) Webhooks
b) Log streaming
c) Data lake triggers
d) None of the above - What can you do if a pipeline does not meet the configured SLA?
a) Receive an alert via Azure Monitor
b) Automatically trigger a failure action in the pipeline
c) Extend the pipeline execution time
d) Both a and b
Topic 4: Best Practices for Debugging Pipelines and Data Flows
- What is the best way to identify the root cause of an issue in a pipeline?
a) Check activity logs and error messages
b) Re-deploy the entire pipeline
c) Restart the ADF instance
d) Disable monitoring and alerts - Which of the following is a recommended practice for debugging data flows in ADF?
a) Use the Debug mode to test data flows
b) Modify the linked service credentials
c) Disable pipeline triggers
d) Update the integration runtime - When debugging an activity failure, what should you check first?
a) The data flow settings
b) The pipeline run history
c) The permissions of the Azure resources involved
d) The external system’s network configuration - How can you test a data flow in Azure Data Factory without running the entire pipeline?
a) By using the Data Flow Debugging feature
b) By running individual activities in isolation
c) By using sample data
d) By deploying the data flow to a virtual machine - What can you do if a pipeline is taking too long to run?
a) Analyze the pipeline performance in the Monitoring tab
b) Increase the timeout period in pipeline settings
c) Review and optimize the data flow
d) All of the above - What is the benefit of using data flow debugging in Azure Data Factory?
a) It allows testing transformations with sample data
b) It automatically fixes errors in the pipeline
c) It generates reports on pipeline performance
d) It schedules the pipeline execution
Topic 5: Integration with Azure Monitor and Log Analytics
- How does Azure Monitor help in monitoring Azure Data Factory?
a) By collecting logs and metrics from ADF resources
b) By enabling real-time alerts for pipeline failures
c) By providing visual performance dashboards
d) All of the above - What is the role of Azure Log Analytics in troubleshooting ADF pipelines?
a) It enables advanced querying and analysis of logs
b) It provides integration with external monitoring tools
c) It stores pipeline run history
d) It schedules automatic fixes for pipeline errors - How can you integrate Azure Data Factory with Azure Monitor for custom alerting?
a) By configuring diagnostic settings to send logs to Log Analytics
b) By using Azure Functions
c) By enabling pipeline triggers in the Azure portal
d) By connecting to third-party monitoring tools - What is a key benefit of using Azure Log Analytics with ADF?
a) Simplified pipeline management
b) Customizable and advanced log queries
c) Enhanced data transformation capabilities
d) Automated scaling of resources - Which of the following is a metric that can be monitored through Azure Monitor for ADF?
a) Pipeline execution duration
b) Resource consumption
c) Error count for activities
d) All of the above - How can you view the logs of an ADF pipeline in Azure Monitor?
a) By querying Log Analytics workspace
b) By reviewing the pipeline run details in the ADF portal
c) By accessing the Azure Activity Log
d) All of the above
Answer Key
| Qno | Answer |
|---|---|
| 1 | b) To track the performance and success of pipeline executions |
| 2 | b) Pipeline Monitoring Dashboard |
| 3 | b) Real-time performance and error messages |
| 4 | c) Detailed cost analysis |
| 5 | d) All of the above |
| 6 | a) Re-trigger failed pipeline runs |
| 7 | b) To diagnose issues and track activity history |
| 8 | d) All of the above |
| 9 | c) Pipeline run duration and success rate |
| 10 | d) All of the above |
| 11 | c) By querying Azure Monitor logs |
| 12 | c) View detailed error messages for troubleshooting |
| 13 | a) To notify users when pipeline execution exceeds an SLA |
| 14 | d) All of the above |
| 15 | d) All of the above |
| 16 | a) A contract specifying the acceptable duration for pipeline completion |
| 17 | a) Webhooks |
| 18 | d) Both a and b |
| 19 | a) Check activity logs and error messages |
| 20 | a) Use the Debug mode to test data flows |
| 21 | b) The pipeline run history |
| 22 | a) By using the Data Flow Debugging feature |
| 23 | d) All of the above |
| 24 | a) It allows testing transformations with sample data |
| 25 | d) All of the above |
| 26 | a) It enables advanced querying and analysis of logs |
| 27 | a) By configuring diagnostic settings to send logs to Log Analytics |
| 28 | b) Customizable and advanced log queries |
| 29 | d) All of the above |
| 30 | d) All of the above |