Microsoft DP-600題庫 - DP-600通過考試

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DP-600題庫, DP-600通過考試, DP-600測試引擎, DP-600認證題庫, DP-600測試題庫

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Microsoft DP-600 Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Load and prepare data 20-25% - Ingest data from source systems
  • 1. Use Data Factory copy activity for batch ingestion
  • 2. Ingest data using PySpark or Spark SQL
  • 3. Configure Data Gateway for hybrid scenarios
  • 4. Use Data Factory data flow for transformation
  • 5. Implement streaming data ingestion with Eventstream
- Create and configure items
  • 1. Create and configure Lakehouse, Warehouse, or data pipeline
  • 2. Create and configurehortcuts
  • 3. Configure data processing with notebooks
Topic 2: Design and manage the data model 20-25% - Implement and configure a data model
  • 1. Configure SQL analytics endpoint
  • 2. Implement row-level security (RLS)
  • 3. Create and manage semantic models
  • 4. Use DirectLake mode for large datasets
- Design a data model
  • 1. Implement dimension and fact tables
  • 2. Design a star or snowflake schema
  • 3. Choose appropriate data model type (lakehouse vs warehouse)
  • 4. Define relationships and hierarchies
Topic 3: Deploy and maintain a data solution 10-15% - Deploy data assets
  • 1. Automate deployments using APIs and scripts
  • 2. Use deployment pipelines for development to production
  • 3. Implement CI/CD for Fabric items
- Maintain a data solution
  • 1. Optimize query performance
  • 2. Manage workspace and capacity settings
  • 3. Implement data refresh strategies
Topic 4: Secure and monitor data solutions 15-20% - Secure data solutions
  • 1. Configure sensitive data classifications
  • 2. Configure workspace and item permissions
  • 3. Implement column-level and row-level security
  • 4. Use Microsoft Purview for data governance
- Monitor data solutions
  • 1. Implement alerting and notifications
  • 2. Review and analyze capacity metrics
  • 3. Use OneLake monitoring capabilities
  • 4. Monitor pipeline and dataflow execution
Topic 5: Clean, transform, and enrich data 25-30% - Clean data
  • 1. Validate data quality using Data Quality Profiling
  • 2. Handle missing values and duplicates
  • 3. Apply data cleansing techniques
- Enrich data
  • 1. Implement incremental data loading
  • 2. Implement slowly changing dimensions (SCD)
  • 3. Merge and join data sources
- Transform data
  • 1. Perform schema evolution and mapping
  • 2. Use Dataflow Gen2 for transformations
  • 3. Use Spark libraries for data transformation
  • 4. Implement data standardization and normalization

>> Microsoft DP-600題庫 <<

DP-600通過考試 - DP-600測試引擎

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最新的 Microsoft Certified DP-600 免費考試真題 (Q148-Q153):

問題 #148
You have a Fabric workspace that contains a warehouse named Warehouse!. Warehousel contains the following data.

You need to create a T-SQL statement that will denormalize the tables and include the ContractType and StartDate attributes in the results. The solution must meet the following requirements:
* Include attributes from matching rows in the Contract table.
* Ensure that all the rows from the Employee table are preserved.
* Return the total number of employees per contract type for all the contract types that have more than two employees.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:

Comprehensive Detailed Explanation
We are tasked with writing a T-SQL query to denormalize the tables Employee and Contract in a Fabric warehouse.
Requirements Breakdown
Include attributes from matching rows in the Contract table
This means we must join Employee and Contract on EmployeeID.
Ensure that all rows from the Employee table are preserved
This requires a LEFT OUTER JOIN from Employee to Contract. If an employee has no contract, we still want the employee record included.
Return the total number of employees per contract type for all contract types that have more than two employees This means we need to:
Group by ContractType
Count distinct EmployeeID
Filter groups where that count > 2.
To filter aggregated results, we use HAVING, not WHERE.
Completed Query
WITH result AS (
SELECT
e.EmployeeID,
e.EmployeeName,
e.EmployeePosition,
c.ContractType,
c.StartDate
FROM Employee AS e
LEFT OUTER JOIN Contract AS c
ON c.EmployeeID = e.EmployeeID
)
SELECT
ContractType,
COUNT(DISTINCT EmployeeID) AS TotalEmployees
FROM result
GROUP BY ContractType
HAVING COUNT(DISTINCT EmployeeID) > 2;
Why This is Correct
LEFT OUTER JOIN ensures all employees are included, regardless of contracts.
HAVING allows filtering aggregated groups by employee counts.
The query returns ContractType, StartDate (as requested in denormalization), and aggregated employee counts.
References
T-SQL JOINs in Microsoft Fabric
Aggregate functions and HAVING clause
Denormalization best practices in Fabric warehouses


問題 #149
You create a semantic model by using Microsoft Power Bl Desktop. The model contains one security role named SalesRegionManager and the following tables:
* Sales
* SalesRegion
* Sales Ad dress
You need to modify the model to ensure that users assigned the SalesRegionManager role cannot see a column named Address in Sales Address.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

答案：

解題說明：

Explanation:

To ensure that users assigned the SalesRegionManager role cannot see the Address column in the SalesAddress table, follow these steps in sequence:
Open the model in Tabular Editor.
Select the Address column in SalesAddress.
Set Object Level Security to None for SalesRegionManager.


問題 #150
You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse! contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?

  • A. a managed table
  • B. a shortcut
  • C. a dataflow
  • D. a view

答案：D


問題 #151
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2.
Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse1 contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?

  • A. a managed table
  • B. a dataflow
  • C. a shortcut
  • D. a view

答案：C


問題 #152
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using 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.

答案：

解題說明：

Explanation:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
DataFrame write partitionBy
Apache Spark optimization with partitioning


問題 #153
......

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