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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement and manage semantic models | 25–30 | - Deploy and maintain semantic models
|
| Topic 2: Prepare data for analytics | 45–50 | - Implement data storage structures
|
| Topic 3: Maintain a data analytics solution | 25–30 | - Implement security and governance
|
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NEW QUESTION # 207
You have a semantic model named Model1 that contains data that relates to customers and their bank account balances.
Model1 has the following tables and columns.
A customer can have one or more accounts. Each account can be associated to multiple customers.
You need to ensure that users can query Model1 to identify the total transaction amounts by customer.
What should you add to Model1?
Answer: D
Explanation:
We need to define a many-to-many relation between DimAccount and DimCustomer. This can be done using a bridge table.
Note: Relate many-to-many dimensions
The classic many-to-many scenario relates two entities, for example bank customers and bank accounts. Consider that customers can have multiple accounts, and accounts can have multiple customers. When an account has multiple customers, they're commonly called joint account holders.
Modeling these entities is straight forward. One dimension table stores accounts, and another dimension table stores customers. As is characteristic of dimension tables, there's a unique identifier (ID) column in each table. To model the relationship between the two tables, a third table is required. This table is commonly referred to as a bridging table. In this example, it's purpose is to store one row for each customer-account association.
Reference:
https://learn.microsoft.com/en-us/power-bi/guidance/relationships-many-to-many
NEW QUESTION # 208
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.
What can you identify about the pickupLongitude column?
Answer: C
Explanation:
The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. References = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.
NEW QUESTION # 209
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
In Workspace1, you create a data pipeline named Pipeline1.
You have CSV files stored in an Azure Storage account.
You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1.
The activity must support Power Query M formula language expressions.
Which type of activity should you add?
Answer: A
Explanation:
To copy data from CSV files to Lakehouse1 in Workspace1, you should add a copy activity to Pipeline1.
https://learn.microsoft.com/en-us/fabric/data-factory/connector-lakehouse-copy-activity
NEW QUESTION # 210
Hotspot Question
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows.
You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF dbo.FactSales;
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
https://learn.microsoft.com/en-us/sql/t-sql/statements/create-table-as-clone-of-transact- sql?view=fabric&preserve-view=true Creates a new table as a zero-copy clone of another table in Warehouse in Microsoft Fabric. Only the metadata of the table is copied. The underlying data of the table, stored as parquet files, is not copied.
https://learn.microsoft.com/en-us/fabric/data-warehouse/clone-table
Separate and independent
Upon creation, a table clone is an independent and separate copy of the data from its source.
Any changes made through DML or DDL on the source of the clone table are not reflected in the clone table.Similarly, any changes made through DDL or DML on the table clone are not reflected on the source of the clone table.
NEW QUESTION # 211
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a warehouse named DW1. DW1 contains two tables named Employees and Sales. All users have read access to DW1.
You need to implement access controls to meet the following requirements:
For the Sales table, ensure that the users can see only the sales data from their respective region.
For the Employees table, restrict access to all Personally Identifiable Information (PII).
Maintain access to unrestricted data for all the users.
What should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Requirement Analysis
Sales table # Users should only see sales data for their own region .
This requires filtering rows based on a condition (e.g., Region = User's Region).
The correct feature: Row-level security (RLS) .
Employees table # Restrict access to Personally Identifiable Information (PII) while allowing access to non- sensitive columns.
This requires restricting access to specific columns (such as SSN, phone, address, etc.), but leaving others visible.
The correct feature: Column-level security .
Maintain access to unrestricted data for all users # Both RLS and column-level security allow users to continue accessing non-restricted data.
Why Other Options Are Not Correct
Item permissions : Control access at the object level (e.g., table or report), not at the row or column level. Too coarse for this requirement.
Workspace permissions : Apply at the workspace level, not suitable for table-level data filtering.
Completed Answer
Employees # Column-level security
Sales # Row-level security (RLS)
References
Row-level security in Microsoft Fabric
Column-level security in Microsoft Fabric
NEW QUESTION # 212
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