A data engineer runs a nightly Spark job in Azure Synapse Analytics that processes 500 GB of data. The job currently uses a Spark pool with 10 nodes (each with 8 cores and 64 GB memory). The job completes in 45 minutes, but the team wants to reduce cost. The job is CPU-bound and does not require large memory. Which approach should the engineer take?

Increase the number of nodes to 20 to finish faster.

Switch to a memory-optimized node size.

Reduce the number of nodes to 6 and increase cores per node.

Keep the same nodes but enable autoscale to scale down after the job.

A team runs a Power BI report that executes the same DAX query against an Azure Synapse Analytics dedicated SQL pool every 5 minutes. The query takes 20 seconds to run. The data changes only once per day. The team wants to reduce the load on the SQL pool. Which feature should they enable?

A workload classification to assign the query to a lower priority.

A materialized view on the underlying tables.

Result-set caching on the dedicated SQL pool.

A non-clustered index on the filtered columns.

In Azure Synapse Analytics, what is the primary purpose of a table index?

To manage user permissions on the table.

To encrypt the table data at rest.

To automatically compact small files in the table storage.

To speed up data retrieval by reducing the amount of data scanned for queries.

What is the primary purpose of file compaction in a data lake that stores many small files?

To compress the data to reduce storage costs.

To combine small files into larger files to reduce processing overhead.

To partition the data by a specific column for better query performance.

To encrypt the data files to meet security requirements.

In Azure Data Factory, what is the purpose of a retry policy on an activity?

To automatically retry the activity a specified number of times if it fails.

To change the activity type based on the input data.

To log the activity execution details for auditing.

To increase the compute resources for the activity.

Microsoft Certified:Azure Data Engineer Associate

Domain 3Objective 3

Optimize and Troubleshoot Data Storage and Data Processing DP-203 Practice Questions (Page 3)

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 38 practice questions to prepare you well beyond it. (estimate)

38questions here

8free pages

8concepts

30–35%of the exam

Questions 11–15

11application · medium
A data engineer runs a nightly Spark job in Azure Synapse Analytics that processes 500 GB of data. The job currently uses a Spark pool with 10 nodes (each with 8 cores and 64 GB memory). The job completes in 45 minutes, but the team wants to reduce cost. The job is CPU-bound and does not require large memory. Which approach should the engineer take?
Select an answer first
12application · medium
A team runs a Power BI report that executes the same DAX query against an Azure Synapse Analytics dedicated SQL pool every 5 minutes. The query takes 20 seconds to run. The data changes only once per day. The team wants to reduce the load on the SQL pool. Which feature should they enable?
Select an answer first
13foundation · easy
In Azure Synapse Analytics, what is the primary purpose of a table index?
Select an answer first
14foundation · easy
What is the primary purpose of file compaction in a data lake that stores many small files?
Select an answer first
15foundation · easy
In Azure Data Factory, what is the purpose of a retry policy on an activity?
Select an answer first

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