You have a Power BI query named Sales that imports the columns shown in the following table.
Users only use the date part of the Sales_Date field. Only rows with a Status of Finished are used in analysis.
You need to reduce the load times of the query without affecting the analysis.
Which two actions achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
To reduce the load times of the query without affecting the analysis: First, removing the rows where Sales[Status] is Canceled will reduce the dataset size, thereby improving query performance and load times. Secondly, removing the Sales[Canceled_Date] column reduces the number of columns to be loaded. This is beneficial especially since, after removing rows with Status 'Canceled', the Canceled_Date column will only have null values, making it unnecessary for analysis and only contributing to load times.
A, only records with state finished are used D, personally I would transform the column to a date format and not split it since only the date part is used Not E, All the cancelled rows are already deleted with A and when a order is not cancelled it will contain a null value
Splitting the column without deleting one of them isn't going to do anything for performance. And you're right that if step A is done then the cancelled_date column will only contain null values. But reducing the number of columns is going to improve the performance - even if that column is all null.
AE is correct: 'Each correct answer presents a complete solution.' E presents a complete solution on its own without A.
Although the Microsoft reference says: Separate date and time, if bound together. If any of your tables have columns that combine date and time, make sure that you separate them into distinct columns before importing them into Power BI. This approach will increase compression abilities. But, the question says, each choice will provide a complete solution. and AE are those options only.
No, option E is about delete the Canceled Date fileld not the rows
It says: You need to reduce the LOAD times of the query without affecting the analysis. Only answers A and E can reduce the load times. D may reduce only the time needed to process the data. Someone said that E is not the answer because: "All the cancelled rows are already deleted with A and when a order is not cancelled it will contain a null value". You must read again the decription cause it says " Each answer presents a COMPLETE solutiuon" not part of a solution.
"Separate date and time, if bound together. If any of your tables have columns that combine date and time, make sure that you separate them into distinct columns before importing them into Power BI. This approach will increase compression abilities." Source: https://learn.microsoft.com/en-us/training/modules/get-data/8-performance-issues
You focus too much on load. Answer E doesn't resolve the date part of the question. Splitting columns is correct, not E.
The 'date part of the question' is not a requirement, it's not like the time is negatively impacting the users.
I was on the Track of AD, but I think you are right. Splitting up [Sale_Date] alone doesn't help us with faster load times (the question is not about performance). Also, analyzing the two options separately ("Each correct answer presents a complete solution"), deleting [Canceled_Date] for sure is a right answer.
I agree here as well. Choosing D (splitting up [Sales_Date]) may even increase the LOAD time of the query, even though it might decrease its PROCESSING time. E (removing [Canceled_Date]) on the other hand removes an entire column to be loaded, thereby decreasing the LOAD time.
I think AE is correct
I agree.
This was on my exam, 2/2024.
Was at the exam on February 12th 2024
D does not reduce load times. Probably it increases it. If we remove rows with Status = Cancelled then Canceled Date would always be null so we better remove the column and then option E becomes better than D.
Option A is obvious. Option D reduces the cardinality of the column which can have a huge impact in the loading time of the model.
21-feb- 2024, Scored 920 My answer BE
i was mixing up load times and processing times. option D could potentially increase loading time, it would decrease processing time. processing time is for calculations, load time...is for loading
Answer A is obvious. I choose answer E because we want to analyze the rows with Status = Finished, that implies that every record in the analysis would have the Canceled_Date = null. That column would add nothing to the report, so we could safely drop it and that would improve a little the load time.
Answer: AD Explanation: A: Removing uninteresting rows will increase query performance. D: Splitting the Sales_Date column will make comparisons on the Sales date faster. The Power BI Desktop data model only supports date/time, but they can be formatted as dates or times independently. Date/Time – Represents both a date and time value. Underneath the covers, the Date/Time value is stored as a Decimal Number Type. Since there's a T in the dates column before split, it's saved as a source text value. Splitting converts it to a numeric value. This reduces the size.
D does not reduce anything
A, D and E are correct, but since we need to choose only 2, then A & D
E will improve load time, nor D
D is not correct not considering whether it affects the performance but remember question said that they wished to analyse the data with the column Sales_Date but they didn't say they not including time for analysis.
I think it's DE. We need optimize only loading time, so A will reduces amount of rows we are loading, but Power Query must go through all dataset to delete specified values, so it takes more time than delete entire column
A and D. As E removing column doesn't decrease load time as query load time is dependent on the number of rows in the dataset. SInce rows will remain the same even after E, and the time taken to reach the bottom of the query is still the same. removing rows is actually where you save query load time. So A and D