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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Design and manage the data model20-25%- Design a data model
  • 1. Define relationships and hierarchies
  • 2. Design a star or snowflake schema
  • 3. Choose appropriate data model type (lakehouse vs warehouse)
  • 4. Implement dimension and fact tables
- Implement and configure a data model
  • 1. Use DirectLake mode for large datasets
  • 2. Create and manage semantic models
  • 3. Implement row-level security (RLS)
  • 4. Configure SQL analytics endpoint
Secure and monitor data solutions15-20%- Secure data solutions
  • 1. Use Microsoft Purview for data governance
  • 2. Configure workspace and item permissions
  • 3. Configure sensitive data classifications
  • 4. Implement column-level and row-level security
- Monitor data solutions
  • 1. Use OneLake monitoring capabilities
  • 2. Monitor pipeline and dataflow execution
  • 3. Review and analyze capacity metrics
  • 4. Implement alerting and notifications
Deploy and maintain a data solution10-15%- Deploy data assets
  • 1. Implement CI/CD for Fabric items
  • 2. Automate deployments using APIs and scripts
  • 3. Use deployment pipelines for development to production
- Maintain a data solution
  • 1. Manage workspace and capacity settings
  • 2. Optimize query performance
  • 3. Implement data refresh strategies
Load and prepare data20-25%- Ingest data from source systems
  • 1. Ingest data using PySpark or Spark SQL
  • 2. Use Data Factory data flow for transformation
  • 3. Use Data Factory copy activity for batch ingestion
  • 4. Configure Data Gateway for hybrid scenarios
  • 5. Implement streaming data ingestion with Eventstream
- Create and configure items
  • 1. Create and configurehortcuts
  • 2. Create and configure Lakehouse, Warehouse, or data pipeline
  • 3. Configure data processing with notebooks
Clean, transform, and enrich data25-30%- Clean data
  • 1. Validate data quality using Data Quality Profiling
  • 2. Handle missing values and duplicates
  • 3. Apply data cleansing techniques
- 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
- Enrich data
  • 1. Merge and join data sources
  • 2. Implement slowly changing dimensions (SCD)
  • 3. Implement incremental data loading

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q173-Q178):

NEW QUESTION # 173
n a financial services context, a client manages approximately 8 TB of structured data, encompassing multiple schemas such as 'Customer', 'Transactions', and 'Accounts', which are organized into tables. They utilize SQL scripts for data transformation and implement object and column-level security measures to protect sensitive information. Which data store within Microsoft Fabric would be the most appropriate for this use case?

Answer: D

Explanation:
Option A is the correct choice as a Data Warehouse can accommodate an unlimited amount of structured data, organizing it by databases, schemas, and tables. It supports SQL for data transformation and provides comprehensive security measures, including object-level (e.g., tables, views, functions, stored procedures) and column-level security, row-level security, and dynamic data masking techniques.


NEW QUESTION # 174
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.
You have a Fabric tenant that contains a semantic model.
You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.
You query a measure that includes the username () function, and the query returns a blank result.
You need to ensure that the measure returns the user principal name (UPN) of a user.
Solution: You update the measure to use the USEROBJECT () function.
Does this meet the goal?

Answer: A


NEW QUESTION # 175
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?

Answer: C


NEW QUESTION # 176
Hotspot Question
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.

Answer:

Explanation:


NEW QUESTION # 177
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?

Answer: A

Explanation:
https://learn.microsoft.com/en-us/fabric/data-warehouse/monitor-using-dmv


NEW QUESTION # 178
......

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