Exam PL-300 topic 1 question 10 discussion

Answer C is best approach Split the Logged column by using "at" as the delimiter.

C, You should split the Logged column by using "at" as the delimiter. This will allow you to separate the date and time into separate columns, which will enable you to analyze the complaints by date and use a built-in date hierarchy. Alternatively, you could also use a transformation to extract the date and time from the Logged column and set the data type of the new columns to Date and Time, respectively. Option A is incorrect because it only extracts the last 11 characters of the Logged column, which would not include the date. Option B is incorrect because the data in the Logged column is in a non-standard date format and cannot be directly converted to the Date data type. Option D is incorrect because it only extracts the first 11 characters of the Logged column, which would not include the time.

The Correct Answer is D for two main reasons 1) The question is only interested in creating a DATE hierarchy, so time is irrelevant here, extracting the first 11 characters covers the entire Date. 2) the 'at' delimiter of option C will include the spaces as suffix and prefix, in the new split columns

Split the logged column with the delimiter space

D. Apply a transformation to extract the first 11 characters of the Logged column. Extracting the first 11 characters will give you 2018-12-31, which is the date part. You can then set the data type of this new column to Date. This is the most straightforward and efficient approach.

Explanation: The Logged column format is "2018-12-31 at 08:59". To analyze by date and use a built-in date hierarchy, you need to extract just the date part. The first 11 characters ("2018-12-31") represent the date. Once extracted, you can convert this new column into a Date data type for proper analysis and use of the date hierarchy. This ensures that the data is in a suitable format for date-based analysis.

Answer is correct. Tried and tested.

I have seen the same question in the Microsoft pl-300 preparation course on coursera and here is the answer to that question: You have a CSV file that contains user complaints. The table contains a column called Logged, which has data and the time of each complaint. The data is logged in the following format: 2022-12-21 at 08:35. You want to analyze the complaints by the logged date and utilize Power BI’s built-in date hierarchy. What should you do? - Split the logged column using ‘at’ as a delimiter. - You can use the same column as the date hierarchy. - Apply transformation to extract the column's first characters that only contain the date. - Change the data type of the logged column to Date. ✔

I have seen the same question in the Microsoft pl-300 preparation course on coursera and here is the answer to that question: You have a CSV file that contains user complaints. The table contains a column called Logged, which has data and the time of each complaint. The data is logged in the following format: 2022-12-21 at 08:35. You want to analyze the complaints by the logged date and utilize Power BI’s built-in date hierarchy. What should you do? 1- Split the logged column using ‘at’ as a delimiter. 2- You can use the same column as the date hierarchy. 3- Apply transformation to extract the column's first characters that only contain the date. 4- Change the data type of the logged column to Date. ✔

My bad, A is talking about the last 11 characters, so my choice is D.

And yeah you should change the data type after D so the correct answer should be A

I've tried in PBI desktop, and D works well.

Tried it in power query. splitting the column by delimiter at indeed will result in a space character after the date as in 2018-12-31[space] however the space does not prevent power query from automatically converting the data type to date. If we chose to extract the first 11 characters, power query will still treat them as text. It does not become a date data type.

Tested. Yes, delimiter can customized by delimiting "at" but the column format is still Text. answer A automatically change the column format to Date hierarchy.

Option D

CCCCCCCCCCCCCCCCC! No Brainer!

answer is D, apply a TRANSFORMATION; using a demo. create a csv with complaint id, complaint date(use the date format described in the example) Create new report -> transform data : opens Power Query editor New Sources -> csv Here comes the tricky part: when you import a csv, you can apply TRANSFORMATION on the fly; there is a button at the bottom left part of the window : Extract table using examples click that button, here comes the TRANSFORMATION You are presented with a form allowing you to pick up the fields and the data you want by example. Name your headers according to the csv headers and in the first data row, type the kind of data you want. For the logged column, you will type the date only. After import you will see that Query editor imported and converted your field in Date format. Finally, go to report view and expand your imported csv in the Data pane. You will see that Power BI created a date hierarchy