Free Microsoft DP-600日本語 Practice Test & Real Exam Questions

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Explanation:

Step 1 - Read the Parquet file into a DataFrame
df = spark.read.parquet( " abfss://[email protected]/files/sales.parquet " ) This correctly loads the Parquet data into Spark.
Step 2 - Write into the Lakehouse as a managed table
If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:
Write the data in delta format (because Fabric Lakehouse tables are Delta tables).
Save the table under the tables folder, not files.
So the correct code is:
df.write.mode( " overwrite " ).format( " delta " ).saveAsTable( " tables/sales " ) Final Answer:
Format: delta
SaveAsTable Path: tables/sales
References:
Lakehouse tables in Microsoft Fabric
Save DataFrame as Delta Table in Spark
# Answer Selection:
First dropdown # delta
Second dropdown # tables/sales

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

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Explanation:

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:
Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.
Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.
Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.

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