Exam DP-600 topic 1 question 26 discussion

D. Query folding is NOT occurring. Query folding refers to the ability of Power Query to push data transformation logic back to the data source, which can perform the transformations more efficiently. When query folding does not occur, all the data is pulled into Power BI and transformations are applied locally, which can be resource-intensive and lead to running out of resources, especially with large datasets like your 30 CSV files. E. The delta type of the column used to partition the data has changed: While this could cause issues with incremental refresh accuracy, it would not typically result in "running out of resources" during the refresh.

i don't really know. But A and D regarding query folding seem for me not to be relevant since query folding does not make sense anyway on CSV/Flatfile connections. In my understanding, query folding sends a query back to the source. And what kind of query would that be to a CSV source?

The answer is E. Because CSV does not support Query Folding.

Answer is E : https://learn.microsoft.com/en-us/power-query/query-folding-examples#no-query-folding-example

data type issue becoz PBI supports for lakehouse source

The correct answer is "E." The answer is written incorrectl, they meant to say "data type" instead of "delta type," which is the reason for running out of resources.

Answer D. If query folding is not supported, all transformations are performed in Power BI memory. This can cause excessive resource usage, especially for large data sources such as 30 CSV files. This is the most likely cause of the error. Answer E is not correct. If the partitioning column type has changed, it will cause an update error, but not a resource overload. The error will be more related to data incompatibility.

The delta type of the column used to partition the data has changed.

D. Query folding is NOT occurring Query folding happens when Power BI sends this list (your transformations) to the database, so it does the heavy lifting for you. If folding doesn’t happen, Power BI has to handle all the processing on its own, which is slower and less efficient.

Query folding is NOT occurring.

D. While query folding typically does not apply to CSV files, OneLake does provide some structure that can prevent loading all data into PowerBI Memory.

Correct Option is D) If query folding is not occurring, it means that all 30 CSV files are likely being loaded into memory for processing, which can lead to resource exhaustion, especially if the files are large or numerous. A. Query folding is occurring: If query folding were happening, it would typically improve performance and reduce resource usage, not cause a failure. B. Only refresh complete days is selected: This option usually helps in managing the data being refreshed by only focusing on completed data, potentially alleviating resource issues rather than causing them. C. XMLA Endpoint is set to Read Only: If the XMLA Endpoint were set to read-only, it wouldn't directly cause a refresh failure due to resource issues. It would restrict write operations but not necessarily impact resource allocation during a refresh. E. The delta type of the column used to partition the data has changed: Changes in partitioning columns could cause refresh issues, but they wouldn’t inherently lead to resource exhaustion unless they also disrupt query folding.

Was in exam. Scored 95% Chose D. Honestly, I was guessing. No clue. However, query folding does occur for CSV files stored IN OneLake. OneLake does the work. This different from semantic models created with files stored on a normal machine and what had been common knowledge for a Power BI user.

Answer D

For efficient data processing, Power BI aims to push as much of the filtering and calculations as possible to the source system (OneLake in this case). This is called query folding. When query folding fails, Power BI needs to pull all the raw data into the semantic model and perform operations there, increasing memory and processing strain. E This could lead to refresh errors but is less likely to cause the specific behavior of running out of resources.

IMHO, The answer is D. Link: https://learn.microsoft.com/en-us/power-bi/connect-data/incremental-refresh-troubleshoot#cause-the-data-source-doesnt-support-query-folding Cause: Data source queries aren't being folded While problems with query folding can usually be determined in Power BI Desktop before publishing to the service, it's possible that model refresh queries aren't being folded, leading to excessive refresh times and query mashup engine resource utilization. This situation happens because a query is created for every partition in the model. If the queries aren't being folded, and data isn't being filtered at the data source, the engine then attempts to filter the data.

D. Query folding is NOT occurring. As described in Incremental refresh and real-time data for models - Requirements, incremental refresh is designed for data sources that support query folding. Make sure data source queries are being folded in Power BI Desktop before publishing to the service, where query folding issues can be significantly compounded. So having said this, csv files is not a data source that support query folding. https://learn.microsoft.com/en-us/power-bi/connect-data/incremental-refresh-troubleshoot#cause-the-data-source-doesnt-support-query-folding