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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Analytics Solutions Using Microsoft Fabric |
| Exam Number: | DP-600 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 1 year (renewable via Microsoft certification renewal) |
| Available Languages: | German, French, English, Korean, Simplified Chinese, Spanish, Japanese, Portuguese (Brazil) |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 (out of 1000) |
| Exam Price: | $165 USD (varies by country) |
| Related Certifications: | DP-900 PL-300 DP-203 |
| Exam Format: | Drag and drop, Multiple choice, Scenario-based questions, Case studies |
| Recommended Training: | Microsoft Fabric Documentation DP-600 Learning Path (Microsoft Learn) |
| Exam Registration: | Pearson VUE Microsoft Exams Microsoft Certification Dashboard |
| Sample Questions: | Microsoft DP-600 Sample Questions |
| Exam Way: | Online proctored exam or in-person at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites required, but familiarity with data analytics, Power BI, and Azure data services is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/credentials/certifications/fabric-analytics-engineer/ |
>> DP-600 Fragen Beantworten <<
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| Thema | Einzelheiten |
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| Thema 1 |
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| Thema 2 |
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| Thema 3 |
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185. Frage
You have a Microsoft Power BI semantic model that contains a measure named TotalSalesAmount.
TotalSalesAmount returns a sales revenue amount that is translated into a selected currency.
You need to ensure that the value returned by TotalSalesAmount is formatted to use the correct currency symbol.
What should you include in the solution?
Antwort: C
Begründung:
Scenario:
Semantic model contains a measure: TotalSalesAmount .
This measure returns sales revenue translated into a selected currency .
Requirement: Format the result using the correct currency symbol dynamically.
Analysis:
Dynamic format string in DAX is specifically designed for applying formats (like currency symbols) dynamically based on slicers or calculation context.
WINDOW function # used for windowing calculations, not formatting.
Linguistic schema # used for Q & A natural language, not for formatting.
Field parameter # used to swap fields/measures dynamically, not for formatting.
A). A dynamic format string
186. Frage
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have the following code.
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:
* The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes
* Removing the partition will reduce the execution time of the query. - No
* Adding inferSchema='true' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.
187. Frage
You have a Fabric warehouse that contains the following data.
The data has the following characteristics:
* Each customer is assigned a unique CustomerlD value.
* Each customer is associated to a single SalesRegion value.
* Each customer is associated to a single CustomerAddress value.
* The Customer table contains 5 million rows.
* All foreign key values are non-null.
You need to create a view to denormalize the data into a customer dimension that contains one row per distinct CustomerlD value. The solution must minimize query processing time and resources.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
Antwort:
Begründung:
Explanation:
The goal is to denormalize the data into a customer dimension with one row per distinct CustomerID, minimizing query processing time and resources. Since each customer is associated with a single SalesRegion and a single CustomerAddress, and all foreign key values are non-null, a left outer join is appropriate to ensure all customers from the Customer table are included, even if there are any unexpected missing addresses (though the problem states each customer has a single address). However, given the non-null foreign key constraint, an inner join could also work, but left outer join is safer and aligns with denormalization to ensure completeness.
The join condition should link the Customer table (C) with the CustomerAddress table (CA) using CustomerID, as this ensures each customer ' s address data is included.
The join with SalesRegion (SR) on SalesRegionID is already included in the query, and since each customer has a single SalesRegion, it fits the denormalization requirement.
The where CA.AddressType = ' Main Office ' filter ensures only the main office address is included, which is a valid constraint for a customer dimension.
Thus, the completed T-SQL statement would effectively denormalize the data while minimizing resource use by leveraging the existing relationships and constraints.
188. Frage
You have a Fabric warehouse named Warehouse1 that contains a table named dbo.Product. dbo.Product contains the following columns.
You need to use a T-SQL query to add a column named PriceRange to dbo.Product. The column must categorize each product based on UnitPrice. The solution must meet the following requirements:
* If UnitPrice is 0, PriceRange is " Not for resale " .
* If UnitPrice is less than 50, PriceRange is " Under $50 " .
* If UnitPrice is between 50 and 250, PriceRange is " Under $250 " .
* In all other instances, PriceRange is " $250+ " .
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Comprehensive Detailed Explanation
We need to create a computed column PriceRange based on the UnitPrice column in the dbo.Product table.
Step 1: Requirements
If UnitPrice = 0 # " Not for resale "
If UnitPrice < 50 # " Under $50 "
If UnitPrice > = 50 AND UnitPrice < 250 # " Under $250 "
Otherwise # " $250+ "
This matches a CASE expression in T-SQL.
Step 2: CASE Expression Structure
The syntax is:
CASE
WHEN condition THEN result
WHEN condition THEN result
ELSE result
END
Step 3: Apply to Problem
SELECT
Item,
UnitPrice,
PriceRange = CASE
WHEN UnitPrice = 0 THEN ' Not for resale '
WHEN UnitPrice < 50 THEN ' Under $50 '
WHEN UnitPrice > = 50 AND UnitPrice < 250 THEN ' Under $250 '
ELSE ' $250+ '
END
FROM [Warehouse1].[dbo] .[Product];
Step 4: Why This is Correct
CASE starts the conditional evaluation.
ELSE handles the default branch ( $250+ ).
END closes the expression.
Meets all requirements exactly.
References
CASE expression in T-SQL
Computed columns in T-SQL
189. Frage
Hotspot Question
You have a Microsoft Power BI report and a semantic model that uses Direct Lake mode.
From Power BI Desktop, you open Performance analyzer as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
190. Frage
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