You need to ensure that a monitoring solution can query logs from Azure Data Factory and Azure Synapse Analytics in a single place. What should you configure?

Azure Monitor Workbooks for each service

A Log Analytics workspace and send diagnostic logs from both services to it

A separate storage account for each service's logs

An Azure Event Hub to stream logs to a third-party tool

What is the purpose of a 'test' pipeline in Azure Data Factory?

To monitor the health of the integration runtime

To automatically scale the pipeline resources

To store the data processed by the pipeline

To validate the functionality of data pipelines before production deployment

A data warehouse in Azure Synapse Analytics has a fact table that is updated nightly with new records. Over time, queries on the table have become slower. The data engineer suspects stale statistics. What should they do to ensure query performance is maintained?

Run UPDATE STATISTICS on the fact table after each nightly load.

Disable auto-stats and rely on manual statistics only when queries are slow.

Create a new statistics object for every column in the fact table.

Rebuild all indexes on the fact table every night.

A data engineer uses Azure Data Factory to copy data from an on-premises SQL Server to Azure Blob Storage. The copy activity takes longer than expected. They need to measure the performance of the data movement to identify the bottleneck. What should they do?

Use Azure Monitor to view the 'Integration Runtime' CPU metric and assume the on-premises machine is the bottleneck.

Run a simple SELECT COUNT(*) query on the source SQL Server to measure its performance.

Enable diagnostic logs for the Data Factory and review the 'Copy activity' logs to see the throughput and duration for each copy operation.

Check the Blob Storage 'Ingress' metric to determine if the storage account is the bottleneck.

A data engineer is copying data from an on-premises SQL Server to Azure Blob Storage using Azure Data Factory with a self-hosted integration runtime. The copy activity is slow, and the engineer wants to measure whether the bottleneck is the source, the network, or the sink. They have already reviewed the ADF metrics and see that 'Data read' is high but 'Data written' is low. What should they do next?

Increase the degree of copy parallelism in the activity to improve throughput.

Switch the copy to use a staging copy via Azure Blob Storage to improve performance.

Use Azure ExpressRoute to improve network connectivity between on-premises and Azure.

Check the self-hosted integration runtime's resource utilization (CPU, memory, network) during the copy to see if it is saturated.

Microsoft Certified:Azure Data Engineer Associate

Domain 3Objective 2

Monitor Data Storage and Data Processing DP-203 Practice Questions (Page 9)

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 41–45

41foundation · easy
You need to ensure that a monitoring solution can query logs from Azure Data Factory and Azure Synapse Analytics in a single place. What should you configure?
Select an answer first
42foundation · easy
What is the purpose of a 'test' pipeline in Azure Data Factory?
Select an answer first
43application · medium
A data warehouse in Azure Synapse Analytics has a fact table that is updated nightly with new records. Over time, queries on the table have become slower. The data engineer suspects stale statistics. What should they do to ensure query performance is maintained?
Select an answer first
44application · medium
A data engineer uses Azure Data Factory to copy data from an on-premises SQL Server to Azure Blob Storage. The copy activity takes longer than expected. They need to measure the performance of the data movement to identify the bottleneck. What should they do?
Select an answer first
45expert · hard
A data engineer is copying data from an on-premises SQL Server to Azure Blob Storage using Azure Data Factory with a self-hosted integration runtime. The copy activity is slow, and the engineer wants to measure whether the bottleneck is the source, the network, or the sink. They have already reviewed the ADF metrics and see that 'Data read' is high but 'Data written' is low. What should they do next?
Select an answer first

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