Monitor Data Storage and Data Processing DP-203 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data engineering team uses Azure Data Factory pipelines to load data into Azure Synapse Analytics. They notice that a nightly pipeline occasionally fails, but the failure is not always the same stage. They need to identify the most common failure point over the last 30 days and set up a proactive alert when that stage fails more than twice in a single run. What should they do?

   • ACreate a Log Analytics workspace query that groups pipeline runs by activity name and status, then create an alert rule based on that query.
   • BConfigure a diagnostic setting to send pipeline runs to a storage account, then use Azure Storage Analytics to query the logs.
   • CUse Azure Data Factory's built-in monitoring view to export the last 30 days of runs to a CSV file and manually count the failures.
   • DEnable Azure Monitor for VMs on the integration runtime and review the performance counters for each activity.
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2. 7application · medium

   A company runs a mission-critical Azure Stream Analytics job that processes IoT telemetry from thousands of devices. The job's 'Watermark delay' metric has been increasing steadily over the past hour, and the 'Input events' count is normal. The team suspects a downstream bottleneck. What should they do to confirm the bottleneck?

   • AReview the query's event ordering policy and adjust the tolerance window to reduce watermark delay.
   • BRestart the Stream Analytics job to clear the watermark delay and reset the backlog.
   • CIncrease the number of streaming units allocated to the job to improve processing throughput.
   • DCheck the 'Output Events' count and the 'Backlogged Input Events' metric in the job's monitoring charts.
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4. 8application · medium

   A data engineering team has developed a new Azure Data Factory pipeline that transforms and loads data into a reporting database. They want to validate the pipeline's functionality and performance before promoting it to production. They plan to run the pipeline with a small sample dataset. What should they do to ensure the test is meaningful?

   • ACreate a separate test pipeline that copies the same data and compare the outputs.
   • BRun the pipeline as a triggered run and monitor the pipeline run status in the Monitor app.
   • CUse Azure Monitor alerts to notify the team if the pipeline fails during the test run.
   • DRun the pipeline in debug mode and check the output of each activity against expected results.
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5. 9foundation · easy

   Why is it important to regularly update statistics on tables in a data warehouse?

   • ATo encrypt the data at rest
   • BTo ensure the query optimizer has accurate data distribution information for efficient query plans
   • CTo compress the data to reduce storage
   • DTo enforce data integrity constraints
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6. 10foundation · easy

   What is a common use of Azure Monitor metrics for Azure Data Factory pipelines?

   • ATo track the number of pipeline runs and their success/failure rates
   • BTo configure the pipeline's source and sink
   • CTo store the actual data processed by the pipeline
   • DTo define the schema of the data
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