Exam DP-600 topic 1 question 25 discussion

Agree with lengzhai's reference of the 2 links: A - Custom aggregations enables PBI to not perform a Full Scan of the underlying datasets. B - The AutoAggregations feature automatically creates aggregations on large datasets and based on query optimization determines the total number of rows that requires processing based on the generated query plan. Incorrect to this question context: C - Although caching helps improve performance on large datasets, it doesn't support DirectQuery (Important note in https://learn.microsoft.com/en-us/power-bi/connect-data/power-bi-query-caching) ; Also, it is a feature available in PBI Service that is automatic and needs no intervention from the user.

D: onelake integration not for Direct Query C: only at loading for first page So AV

A. user-defined aggregations B. automatic aggregation

User-defined aggregations (A): User-defined aggregations allow you to create pre-aggregated tables that summarize data at a higher level, reducing the volume of data that needs to be queried for specific visuals. This can significantly improve performance for common queries by avoiding the need to scan the entire dataset. Query caching (C): Query caching stores the results of previous queries for reuse. When users interact with visuals that require similar data, the cached results can be returned more quickly than querying the source again, thereby reducing execution time for those visuals.

A & B is the answer

B&D are correct. Direct Lakes are great for performance in the OneLake integration https://learn.microsoft.com/en-us/fabric/get-started/direct-lake-overview

A&B While query caching can be beneficial in certain scenarios, user-defined aggregations and automatic aggregations are typically more effective for improving query performance in Power BI reports with large datasets and complex queries. These methods reduce the volume of data processed in real-time queries, directly addressing the performance bottlenecks associated with querying large datasets.

CHATGPT saya AC

IMHO, A & B looks good

Agree with A B https://learn.microsoft.com/en-us/power-bi/transform-model/aggregations-advanced https://learn.microsoft.com/en-us/power-bi/enterprise/aggregations-auto

Agreed AB. Although query caching (C) will reduce query execution time too, you risk outdated cached results when working with real-time or dynamic data.

A. User-defined aggregations (UDAs) allow you to pre-aggregate specific calculations directly in the semantic model. This reduces the amount of data that needs to be retrieved from the source each time a visual requires the calculation, significantly improving query execution time. C. Power BI Desktop enables query caching for DirectQuery models. This stores frequently used queries on the client machine, eliminating the need to re-send them to the source data for subsequent interactions.

https://learn.microsoft.com/en-us/power-bi/enterprise/aggregations-auto https://learn.microsoft.com/en-us/power-bi/enterprise/onelake-integration-overview