A data platform team uses Azure Data Factory to orchestrate a complex pipeline with multiple activities, including data flows and stored procedures. The pipeline runs successfully but takes longer than the service-level agreement (SLA) allows. The team needs to identify which activity contributes the most to the overall duration. What should they do?

Enable diagnostic logs and query the ADFPipelineRun table for the 'RunDuration' field.

Set up alerts on the pipeline run duration to notify the team when it exceeds the SLA.

Use the ADF Monitor app to view the pipeline run details and examine the duration of each activity in the run's activity list.

Run the pipeline in debug mode and step through each activity manually.

A team runs a mix of interactive and batch queries on an Azure Synapse Analytics dedicated SQL pool. They notice that interactive queries are sometimes slow because batch queries consume too many resources. They need to ensure interactive queries get priority without manually killing batch queries. What should they do?

Set the entire SQL pool to a higher service level objective (SLO) to provide more resources for all queries.

Use workload classification to assign interactive queries to a workload group with a higher importance and a lower concurrency limit.

Use query labels to identify batch queries and then manually kill them when interactive queries are slow.

Create a separate SQL pool for interactive queries and replicate the data.

What is the primary purpose of setting up alerts on Azure Data Factory pipeline runs?

To increase the performance of pipelines

To automatically fix pipeline failures

To proactively notify administrators when pipelines fail or behave unexpectedly

Microsoft Certified:Azure Data Engineer Associate

Domain 3Objective 2

Monitor Data Storage and Data Processing DP-203 Practice Questions (Page 6)

Part of the Secure, monitor, and optimize data storage and data processing domain, which accounts for 30–35% of the DP-203 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~12–23 in this domain), expect 4–8 from this objective — we provide 46 practice questions to prepare you well beyond it. (estimate)

46questions here

10free pages

10concepts

30–35%of the exam

Questions 26–30

26application · medium
A data platform team uses Azure Data Factory to orchestrate a complex pipeline with multiple activities, including data flows and stored procedures. The pipeline runs successfully but takes longer than the service-level agreement (SLA) allows. The team needs to identify which activity contributes the most to the overall duration. What should they do?
Select an answer first
27foundation · easy
Which dynamic management view (DMV) in Azure SQL Database is commonly used to identify long-running queries?
Select an answer first
28expert · hard
A team runs a mix of interactive and batch queries on an Azure Synapse Analytics dedicated SQL pool. They notice that interactive queries are sometimes slow because batch queries consume too many resources. They need to ensure interactive queries get priority without manually killing batch queries. What should they do?
Select an answer first
29foundation · easy
Which Azure Data Factory activity metric can be used to measure the performance of a data movement operation?
Select an answer first
30foundation · easy
What is the primary purpose of setting up alerts on Azure Data Factory pipeline runs?
Select an answer first

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Monitor Data Storage and Data Processing DP-203 Practice Questions (Page 6)

Questions 26–30

Which dynamic management view (DMV) in Azure SQL Database is commonly used to identify long-running queries?

Which Azure Data Factory activity metric can be used to measure the performance of a data movement operation?

What is the primary purpose of setting up alerts on Azure Data Factory pipeline runs?

Keep studying — Secure, monitor, and optimize data storage and data processing