Microsoft Fabric - DP-600 Exam Preparation

Master the Microsoft Fabric Data Analytics landscape with comprehensive exam preparation! This course equips you with the skills and knowledge to excel in the DP-600 Data Analyst Associate certification.

Plan, Implement, and Manage Data Analytics Solutions:

• Data Analytics Environment Planning: Learn to plan a data analytics environment, defining requirements for components, features, performance, and capacity, including stock-keeping units (SKUs).
• Fabric Admin Portal Configuration: Recommend settings in the Fabric admin portal to optimize your data analytics setup.
• Data Gateway Management: Choose the right data gateway type for seamless data connectivity.
• Workspace and Access Control: Implement workspace and item-level access controls within Fabric for secure data management.
• Data Sharing: Implement data sharing for workspaces, warehouses, and lakehouses to facilitate collaboration.
• Sensitivity Labels: Manage sensitivity labels in semantic models and lakehouses to ensure data security and compliance.
• Capacity Management: Configure Fabric-enabled workspace settings and manage Fabric capacity effectively.
• Analytics Development Lifecycle: Manage the analytics development lifecycle from planning to deployment.
• Version Control: Implement version control for a workspace to track changes and collaborate efficiently.
• Power BI Project Management: Create and manage a Power BI Desktop project (.pbip) for streamlined development.
• Deployment Solutions: Plan and implement deployment solutions for your data analytics projects.
• Impact Analysis: Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models.
• Semantic Model Deployment: Deploy and manage semantic models using the XMLA endpoint.
• Reusable Assets: Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models.

Prepare and Serve Data:

• Lakehouse/Warehouse Object Creation: Create objects in a lakehouse or warehouse to structure your data.
• Data Ingestion: Ingest data using data pipelines, dataflows, or notebooks.
• Shortcut Management: Create and manage shortcuts for easy data access.
• File Partitioning: Implement file partitioning for analytics workloads in a lakehouse to improve performance.
• Data Transformation: Create views, functions, and stored procedures for data manipulation.
• Data Enrichment: Enrich data by adding new columns or tables.
• Data Copying: Copy data from a Fabric data source to a lakehouse or warehouse using various methods.
• Data Pipeline Scheduling: Schedule data pipelines, dataflows, and notebooks for automated data processing.
• Data Transformation Techniques: Transform data using cleansing, schema design (star schema, bridge tables), denormalization, aggregation, and data merging.
• Data Quality: Identify and resolve duplicate data, missing data, or null values.
• Data Type Conversion: Convert data types using SQL or PySpark.
• Performance Optimization: Filter data and optimize performance in data loading, dataflows, notebooks, and SQL queries.
• Delta Table Optimization: Identify and resolve issues with Delta table file sizes.

Implement Semantic Models:

• Semantic Model Design: Design and build semantic models, choosing appropriate storage modes like Direct Lake.
• DAX Studio & Tabular Editor: Implement use cases for DAX Studio and Tabular Editor 2.
• Star Schema Implementation: Implement a star schema for semantic models.
• Relationships: Implement relationships, including bridge tables and many-to-many relationships.
• DAX Calculations: Write calculations using DAX variables, iterators, table filtering, windowing, and information functions.
• Calculation Groups & Dynamic Strings: Implement calculation groups, dynamic strings, and field parameters.
• Large Format Datasets: Design and build large format datasets.
• Composite Models: Design and build composite models with aggregations.
• Row-Level Security (RLS) & Object-Level Security (OLS): Implement dynamic row-level security and object-level security.
• Security Validation: Validate row-level security and object-level security.
• Enterprise-Scale Optimization: Optimize enterprise-scale semantic models.
• Performance Improvement: Improve performance in queries and report visuals.
• DAX Studio Optimization: Improve DAX performance using DAX Studio.
• **Tabular Editor Optimization