A data engineering team uses Azure Databricks to process streaming data that is accumulated in a Delta Lake table. The batch job reads new data every hour and performs an aggregation. The team notices that the job takes longer than expected, and they suspect that the batch size is too large. They want to optimize the batch size to improve performance. What should the team do?

Use the 'maxFilesPerTrigger' option when reading from the Delta Lake table to limit the number of files processed per batch.

Set the 'spark.sql.shuffle.partitions' to a higher value to increase the parallelism of the aggregation.

Increase the number of partitions in the DataFrame by using 'repartition' before the aggregation.

Use 'coalesce' to reduce the number of partitions and decrease the overhead of task scheduling.

Microsoft Certified:Azure Data Engineer Associate

Domain 2Objective 2

Develop a Batch Processing Solution DP-203 Practice Questions (Page 8)

Part of the Develop data processing domain, which accounts for 40–45% of the DP-203 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~16–29 in this domain), expect 4–7 from this objective — we provide 42 practice questions to prepare you well beyond it. (estimate)

42questions here

9free pages

16concepts

40–45%of the exam

Questions 36–40

36foundation · easy
Which Azure service provides a unified analytics platform that combines data warehousing, big data analytics, and data integration, allowing you to run both SQL and Spark workloads in a single workspace?
Select an answer first
37foundation · easy
When using Azure Synapse Link for Azure Cosmos DB, which Synapse component is used to query the replicated data?
Select an answer first
38foundation · easy
In Azure Databricks, which component is a set of compute resources that runs your Spark jobs and can be configured with a specific runtime version and node type?
Select an answer first
39foundation · easy
Which tool is commonly used in Azure Databricks to develop and run Python or Scala code interactively for batch processing?
Select an answer first
40application · medium
A data engineering team uses Azure Databricks to process streaming data that is accumulated in a Delta Lake table. The batch job reads new data every hour and performs an aggregation. The team notices that the job takes longer than expected, and they suspect that the batch size is too large. They want to optimize the batch size to improve performance. What should the team do?
Select an answer first

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Develop a Batch Processing Solution DP-203 Practice Questions (Page 8)

Questions 36–40

Which Azure service provides a unified analytics platform that combines data warehousing, big data analytics, and data integration, allowing you to run both SQL and Spark workloads in a single workspace?

When using Azure Synapse Link for Azure Cosmos DB, which Synapse component is used to query the replicated data?

In Azure Databricks, which component is a set of compute resources that runs your Spark jobs and can be configured with a specific runtime version and node type?

Which tool is commonly used in Azure Databricks to develop and run Python or Scala code interactively for batch processing?

Keep studying — Develop data processing