BenchMark Quizzes by MDFT Pro

View Notebook Jobs in Timeline Chart

You work for MDFT Pro, a well-known training agency that processes student learning analytics data. Mark, a Data Engineering Lead at MDFT Pro, manages a Fabric notebook named StudentEngagementProcessor that calculates daily student engagement metrics from course activity logs. The notebook has been executing successfully for the past week, running automated analyses on quiz completion rates, video watch times, and forum participation. During the most recent run, StudentEngagementProcessor executed nine separate Spark jobs to process data from different course categories. Mark needs to visualize these jobs in a timeline chart to understand the execution pattern, identify any duration anomalies, and spot potential performance issues across the nine jobs. This timeline view will help him optimize the notebook’s execution by identifying which jobs take longer than expected and whether there are any bottlenecks in the data processing workflow.

What should Mark use to view the jobs in a timeline chart?

Choose the correct answer from the options below.

Explanations for each answer:

Real-Time hub is incorrect. Real-Time hub is used for monitoring streaming data sources like eventstreams and setting alerts on Fabric workspace item events. It doesn't provide job timeline visualization for completed notebook executions.
Monitoring hub is incorrect. While the Monitoring hub displays activities for Fabric items including notebook runs, it shows a list of activities rather than a timeline chart. To view a timeline visualization, you need to access the run series feature from the application run details.
The job history from the application run is incorrect. The job history shows individual completed jobs with their status and metadata, but doesn't provide a timeline chart visualization. For timeline views showing duration trends across multiple runs, you need the run series feature.
Spark History Server is incorrect. Spark History Server provides detailed information about completed and running Spark applications, including job graphs and DAG visualizations. However, it shows job graph views rather than timeline charts across multiple runs.
The run series from the details of the application run is correct. The run series feature provides a timeline vertical bar chart that visually represents the duration trend for each Spark application instance. It groups job runs from the same notebook into a run series and displays duration trends along with data input/output trendlines. You can access it from the Monitoring hub's historical view by opening your notebook, selecting Historical runs, and then selecting Monitor run series.

Learn more about monitoring Apache Spark run series:

Monitor Apache Spark Run Series

Practice Exam

View Notebook Jobs in Timeline Chart

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