You have a CSV file that contains user complaints. The file contains a column named Logged. Logged contains the date and time each complaint occurred. The data in Logged is in the following format: 2018-12-31 at 08:59.
You need to be able to analyze the complaints by the logged date and use a built-in date hierarchy.
What should you do?
To analyze complaints by the logged date and use a built-in date hierarchy, you need to create a column that accurately reflects the date portion of the Logged column. Creating a column by example that starts with '2018-12-31' and setting the data type of the new column to Date ensures that the dates are recognized correctly. This will allow you to leverage the built-in date hierarchy functionalities effectively.
Option C should be the correct answer not option B
This explanation given by ChatGPT helped me to understand why C is the correct answer: Creating a new column by example is an effective approach to achieve this. In this case, you would create a new column and set the first example value as "2018-12-31." This action helps the Power Query Editor infer the desired transformation for the entire column. Additionally, you would set the data type of the new column to Date. By doing so, you ensure that the values in the column are recognized as dates, enabling the use of a built-in date hierarchy for analysis.
By the way how do you ask it from Chatgpt, do you paste complete question over there. But what about the image questions.
bro image question can aslo be answered
ChatGPT is wrong A TON. Beware!
ChatGPT gets Microsoft / Azure related questions wrong at like a 50% rate, FYI.
To analyze the complaints by the logged date and use a built-in date hierarchy, we need to extract the date portion of the "Logged" column and convert it to a date format. Therefore, the best option among the given choices is: A. Apply a transformation to extract the first 11 characters of the logged column. Since the date value is stored in the first 11 characters of the "Logged" column, extracting those characters using the "Extract" transformation in Power Query will give us the date value in the format "yyyy-MM-dd". We can then set the data type of the new column to "Date" to convert it into a date format. This will allow us to analyze the complaints by the logged date and use the built-in date hierarchy in Power BI. Option B is not necessary and will not achieve the desired result. Option C will create a single date value and not allow us to analyze the complaints by the logged date. Option D suggested extracting the last 11 characters, but they contain both date and time values and may not result in the correct date format.
i did try extracting first 11 characters but then cannot change the data type of 2018-12-31 to date. still dont know why
The date part is the first 10 characters, if you extract 11 characters then there will be a trailing space behind the date part thus won't be able to convert it to date. thus A is NOT the correct answer.
this question is more than once in the test examples but i still dont understand which is the correct answer :)
I'm starting to think that the person who posts those questions just answers them at random. B? really? that is absurd
Yes C is correct answer, B is making the date field a whole number which doesnt help in analysing the date field data
C is the correct answer.
A extracts 11 characters, the date format is 10 characters, including the last spacing characters will keep the data in string format --> not possible to create a date hierarchy; B extracting the year only will not enable a date hierarchy either and will set the year format to whole number D extracts the LAST 11 characters, there will be no date in there Only valid answer is C: you create by example and PBI will recognize the date format and automatically apply hierarchy
C is correct
A is less steps and lowers model size
Date type is missing for built-in hierarchy feature use
Answer definitely is C
I'd select option C
I am not sure also why there was a correct answer on getting first 11 characters as there are only 10, 11 will have a space and not be recognized if trying to convert to date format, however, it will never be "B", as the question is to analyse over time, not just by year. Answer "B" is just year.
Same this question came in previous topic (question #10, Topic1), and the answer was (Apply a transformation to extract the first 11 characters of the Logged column). So which one is correct?
B is the correct answer , because C is making the table as Date Table, which will remove the Date hierarchy feature .Question demands Date Hierarchy
we didn't get the answer :c
From a quick search, here's what I discovered. To analyze the complaints in the CSV file by the logged date and use a built-in date hierarchy, you should apply a transformation to extract the first 11 characters of the logged column. This action will isolate the date part of the 'Logged' column (e.g., '2018-12-31'), which is suitable for date hierarchy analysis. This approach is effective as it maintains the date in a recognizable format while discarding the time part, which may not be necessary for your date hierarchy analysis. This method ensures that you can analyze the data by year, month, and day without unnecessary complications. For further detailed steps and examples, you can refer to Microsoft's documentation on Power BI and data transformation techniques, such as Power BI Desktop - Add Column From Example and Power Query - Split Date and Time into Separate Columns. the answer is A
From a quick search, here's what I discovered. To analyze the complaints in the CSV file by the logged date and use a built-in date hierarchy, you should apply a transformation to extract the first 11 characters of the logged column. This action will isolate the date part of the 'Logged' column (e.g., '2018-12-31'), which is suitable for date hierarchy analysis. This approach is effective as it maintains the date in a recognizable format while discarding the time part, which may not be necessary for your date hierarchy analysis. This method ensures that you can analyze the data by year, month, and day without unnecessary complications. For further detailed steps and examples, you can refer to Microsoft's documentation on Power BI and data transformation techniques, such as Power BI Desktop - Add Column From Example and Power Query - Split Date and Time into Separate Columns.