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| Section | Objectives |
|---|---|
| Topic 1: Implement analytics solutions | - Data warehousing and modeling
|
| Topic 2: Manage and secure Microsoft Fabric | - Governance and administration
|
| Topic 3: Get data into Microsoft Fabric | - Data ingestion and integration
|
| Topic 4: Implement data engineering solutions | - Lakehouse architecture
|
| Topic 5: Real-time analytics | - Streaming and event processing
|
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155. Frage
You have a Fabric notebook that has the Python code and output shown in the following exhibit.
Which type of analytics are you performing?
Antwort: B
Begründung:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.
156. Frage
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains three schemas named schemaA, schemaB. and schemaC You need to ensure that a user named User1 can truncate tables in schemaA only.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
GRANT ALTER ON SCHEMA::schemaA TO User1;
The ALTER permission allows a user to modify the schema of an object, and granting ALTER on a schema will allow the user to perform operations like TRUNCATE TABLE on any object within that schema. It is the correct permission to grant to User1 for truncating tables in schemaA.
References =
GRANT Schema Permissions
Permissions That Can Be Granted on a Schema
157. Frage
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.
You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?
Antwort: A
Begründung:
The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B.
The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. Reference = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.
158. Frage
You have the following KQL query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
159. Frage
You have source data in a CSV file that has the following fields:
* SalesTra nsactionl D
* SaleDate
* CustomerCode
* CustomerName
* CustomerAddress
* ProductCode
* ProductName
* Quantity
* UnitPrice
You plan to implement a star schema for the tables in WH1. Thedimension tables in WH1 will implement Type 2 slowly changing dimension (SCD) logic.
You need to design the tables that will be used for sales transaction analysis and load the source data.
Which type of target table should you specify for the CustomerName, CustomerCode, and SaleDate fields?
To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
We are designing a star schema for sales transactions. In a star schema:
Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).
Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).
Factless fact tables capture events without numeric measures.
Junk dimensions store miscellaneous low-cardinality attributes.
The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) # so Customer and Date attributes belong to Dimension tables.
Field-by-field classification
CustomerCode
A business key (natural key) for the customer dimension.
Belongs in the Customer Dimension table.
The answer: Dimension.
CustomerName
A descriptive attribute of the customer.
Belongs in the Customer Dimension table.
The answer: Dimension.
SaleDate
A time attribute used for analysis.
Belongs in the Date Dimension table.
The answer: Dimension.
Final answer:
CustomerCode # Dimension
CustomerName # Dimension
SaleDate # Dimension
References:
Star schema design for Power BI / Fabric
Slowly Changing Dimensions in data warehousing
# This follows Kimball's star schema best practices.
160. Frage
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