Microsoft MB-260 Practice Test - Questions Answers, Page 7
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You are a Customer Data Platform Specialist. Your organization is using Power Query when connecting to Data Sources in audience insights. You need to load eCommerce Contacts to audience insights.
Which statement about loading data to audience insights using Power Query is correct?
You must create a separate Power Query data source for each entity you wish to ingest.
Power Query automatically recognizes header rows in files when you use the Text/CSV connector.
After you save a Power Query data source, you have to manually trigger the initial refresh process.
You can add additional entities to the data source using Get Data functionality in the Power Query.
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a Customer Data Platform Specialist. Your company's information technology department (IT) has a CSV file stored on one of their Shared Documents folders within their SharePoint sites which they have ingested into audience insights. The file contains a row header with some special characters, columns of different types (quantities, prices, etc.), and some rows with a high proportion of nulls and missing primary keys. You have been asked to clean and transform the data in audience insights to be ready for unification.
What should you do?
Solution: Clean the data by removing any rows where the primary key is missing, delete any leading or trailing zeros on the primary key, and name the query. Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a Customer Data Platform Specialist. Your company's information technology department (IT) has a CSV file stored on one of their Shared Documents folder within their SharePoint sites which they have ingested into audience insights. The file contains a row header with some special characters, columns of different types (quantities, prices, etc.), and some rows with a high proportion of nulls and missing primary keys. You have been asked to clean and transform the data in audience insights to be ready for unification.
What should you do?
Solution: Clean the data by transforming the first row to be used as headers and removing special characters and spaces from header row, defining column types to be appropriate field types, remove rows with missing primary keys, and name the query. Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a Customer Data Platform Specialist. Your company's information technology department (IT) has a CSV file stored on one of their Shared Documents folder within their SharePoint sites which they have ingested into audience insights. The file contains a row header with some special characters, columns of different types (quantities, prices, etc.), and some rows with a high proportion of nulls and missing primary keys. You have been asked to clean and transform the data in audience insights to be ready for unification.
What should you do?
Solution: Clean the data by transforming the first row to be used as headers and remove any special characters in header, defining column types to be appropriate field types, remove any rows with missing primary key, and name the query. Create a full name and full address columns by merging the appropriate columns if they exist. Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a Customer Data Platform Specialist. Your company's information technology department already ingested a CSV file with column names in the first row into audience insights. You are asked to clean and transform the data to get it ready for unification.
What can you do to satisfy the requirements?
Solution: Clean the data by transforming the first row to be used as headers, defining column types to be appropriate field types, and naming the query. Create a full name column if it does not exist by merging the columns for the first name and last name. Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are a Customer Data Platform Specialist. Your company's information technology department already ingested a CSV file with column names in the first row into audience insights. You are asked to clean and transform the data to get it ready for unification.
What can you do to satisfy the requirements?
Solution: Clean the data by changing columns with numbers to integer number format, which includes fields such as price, number of purchases, and postal code. You should convert primary key to integer number field if it contains only numbers.
Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are a Customer Data Platform Specialist. Your company's information technology department already ingested a CSV file with column names in the first row into audience insights. You are asked to clean and transform the data to get it ready for unification.
What can you do to satisfy the requirements?
Solution: Clean the data by removing any rows with nulls and deleting any leading zeros on the primary key. Click "Next" and your data is now ready for unification.
Does this meet the goal?
Yes
No
You are a Customer Data Platform Specialist. Your information technology (IT) team created the customer profile records by unifying the Account, Contact, and Web Account tables. After unification was complete, the team noticed that three relationships were created automatically (CustomerToContact, CustomerToAccount and CustomerToWebAccount). The team needs to know how they can configure and update these three relationships.
What feedback should you provide?
Relationships created via the unification process can be edited by those with contributor-level access.
You can edit each of the three relationships by clicking into the Relationship tab and selecting edit.
You cannot edit any of the three relationships, as they are non-editable system relationships.
Relationships created via the unification process can only be edited by those with administrator-level access.
You are a Customer Data Platform Specialist. Audience insights users state that they are not able to quickly find details on specific customers while searching customer profiles. You explain the index and search capabilities within audience insights.
Which two statements are correct about configuring and using search and filter index capabilities? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You can add up to 25 custom fields to index and configure filtering for each one of the fields as an administrator.
The only fields that are available for indexing are the ones that exist in the customer profile that you created during the data unification process.
You can only add a filter for a specific field from the search and filter index page in audience insights.
Users with a contributor role can add fields on the search and filter index page in audience insights and configure how many filtered records to show.
You are a Customer Data Platform Specialist. Your company's information technology team wants to use the out-of-the-box customer lifetime value (CLV) machine learning (ML) capabilities that come with audience insights, but the team has some concerns about the suitability of their data. You need to confirm if their research about data requirements is correct.
Which two statements are correct about the data characteristic requirements for configuring the CLV ML model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
There should be at least two to three transactions per customer ID, preferably across multiple dates.
There should be at least 100,000 unique customers to perform the CLV model.
There should be preferably two to three years of transactional data to predict CLV for one year.
The CLV model will not run if there is any missing data in the fields.
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