Will I get access to recorded sessions if I miss a live class?

Yes. All live sessions are recorded in high definition and uploaded to your Edureka Learning Management System (LMS) account within 24 hours of the session. You can watch sessions at your own pace, rewind and replay specific segments, and access all lab datasets, reference materials and supplementary resources alongside the recordings.

Is Power BI relevant if my organization uses Tableau or other BI tools?

Yes. Power BI knowledge is highly transferable and complementary to other BI tools. Core skills in data modeling, DAX measures, visualization best practices and data storytelling learned in Power BI apply directly when working with any BI platform. Many organizations use multiple BI tools simultaneously — Power BI for Microsoft 365-integrated reporting and operational dashboards, alongside Tableau or Qlik for specialized analytics. Power BI's deep integration with Azure, Microsoft Fabric and the Microsoft 365 ecosystem also makes it the preferred choice for organizations already on the Microsoft stack, which represents the majority of enterprise IT environments globally.

How is Power BI different from Excel for data analysis?

While Excel remains powerful for individual-level data manipulation and ad hoc analysis, Power BI is purpose-built for enterprise-scale analytics, interactive dashboards and organizational data sharing. Power BI handles datasets in the hundreds of millions of rows using columnar compression, while Excel struggles beyond a few hundred thousand rows; Power BI's DAX language enables complex calculations across related tables that are difficult or impossible in Excel formulas. Power BI Service enables governed, role-based sharing and collaboration across an entire organization; Power BI includes built-in AI features such as Copilot, Key Influencers and Anomaly Detection unavailable in standard Excel; and Power BI connects natively to cloud data sources, real-time streaming data and the full Microsoft Fabric platform.

Is Advanced Power BI a strong career choice in 2026?

Absolutely. Organizations across industries are investing heavily in data-driven decision-making and AI-augmented analytics. Professionals skilled in advanced DAX, data modeling, RLS, and GenAI-powered insights are in high demand for roles such as BI Developer, Analytics Consultant, and Data Visualization Specialist.

How is Generative AI transforming Power BI?

GenAI enhances Power BI through Copilot-powered DAX generation, automated narrative insights, smart summaries, and AI-assisted data modeling. It accelerates report development, improves analytical depth, and enables business users to derive insights through natural language interactions.

What industries hire Advanced Power BI professionals with AI skills?

Retail and FMCG, Banking and Financial Services (BFSI), Healthcare, E-commerce, Manufacturing, Telecommunications, EdTech, Consulting, and Government sectors actively hire BI professionals who can deliver AI-powered executive dashboards and enterprise reporting solutions.

What is the difference between Advanced Power BI and traditional BI tools?

Traditional BI tools focus primarily on visualization and static reporting. Advanced Power BI with GenAI integrates data modeling, enterprise governance (RLS), AI-powered analytics, automation, and intelligent narratives, enabling scalable, insight-driven decision-making across organizations.

Best Power BI Certification Training Course for All Levels

Name: Advanced Power BI Certification Course with GenAI
Item: Advanced Power BI Certification Course with GenAI
Rating: 5
Author: Tanmoy Kar

Module 1: Introduction to Business Intelligence and Power BI Ecosystem

Topics:

BI Fundamentals
Modern Analytics Landscape
Power BI Ecosystem: Desktop, Service, Mobile, Report Server
Licensing Models, Governance and Architecture
Integration with Microsoft 365, Teams, and SharePoint
Power BI Data Flow
Architecture Deep Dive

Hands-on:

Install and configure Power BI Desktop
Navigate the Desktop interface components
Build your first report with sample data

Skills you will learn:

Power BI ecosystem navigation
Understanding BI data flows
Licensing and governance basics

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Module 2: Data Source Connectivity and Data Preparation Fundamentals

Topics:

Connecting to Flat Files: Excel, CSV, JSON, XML, PDF
Relational Database Connections: SQL Server, MySQL, PostgreSQL, Oracle
Cloud Data Sources: Azure SQL, Azure Synapse, AWS Redshift
Online Services: SharePoint, Google Analytics, Salesforce
Web APIs, REST Endpoints and OData Feeds
Data Source Parameters and Dynamic Connections
Import Mode vs DirectQuery vs Live Connection vs Composite
Configuring On-Premises and Cloud Data Gateways
Data Source Credentials Management and Security Best Practices

Hands-on:

Connect to multiple heterogeneous data sources
Build hybrid model with Import + DirectQuery
Extract data from REST APIs
Set up parameters for dynamic source connections

Skills you will learn:

Multi-source data connectivity
Hybrid connectivity modeling
Gateway configuration management
Secure credential handling
Connectivity mode optimization

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Module 3: Power Query Editor - Data Transformation Mastery

Topics:

Power Query interface and Applied Steps
Data Profiling for Quality Assurance
Data type detection and conversion
Column operations (split, merge, pivot, unpivot)
Filtering, sorting, and removing duplicates
Text transformations and pattern matching
Date and time operations
Conditional columns and custom calculations
Advanced Editor and M Code Fundamentals

Hands-on:

Clean messy sales data with inconsistent formats
Transform wide-format data to long-format
Create custom columns using conditional logic
Extract data from nested JSON structures
Build reusable data transformation templates
Handle null values and data quality issues

Skills you will learn:

M code development
Data profiling techniques
Query transformation pipelines
Error handling logic
Data cleansing workflows

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Module 4: Advanced Power Query - Data Shaping and Integration

Topics:

Merging queries (inner, left, right, full outer joins)
Appending queries and union operations
Query folding and performance optimization
Parameters for dynamic filtering
Custom functions creation
Query dependencies management
Error handling and data validation
Fuzzy matching and approximate joins

Hands-on:

Combine data from multiple regional files
Create a date dimension table using M functions
Build parameterized queries for dynamic reporting
Implement fuzzy merge for customer matching
Develop custom functions for reusable logic
Optimize slow-performing queries

Skills you will learn:

Advanced query merging
Reusable M functions
Query folding optimization
Dynamic parameter modeling
Fuzzy data reconciliation

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Module 5: Data Modeling Fundamentals

Topics:

Star schema vs. snowflake schema
Fact tables and dimension tables
Primary and foreign keys
Relationship types (one-to-many, many-to-many)
Cardinality and cross-filter direction
Active vs. inactive relationships
Role-playing dimensions
Model Optimization and Performance Tuning

Hands-on:

Design a sales data model with proper relationships
Create a date dimension with hierarchies
Build a customer dimension with attributes
Implement role-playing dimensions (order date, ship date)
Resolve many-to-many relationships
Document data model using diagrams

Skills you will learn:

Dimensional model design
Relationship configuration
Role-playing implementation
Model performance tuning

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Module 6: DAX Fundamentals

Topics:

DAX Syntax, Operators, and Expressions
Data Types and Type Conversion
Working with Tables and Columns
Calculated columns vs. measures
Basic aggregation functions (SUM, AVERAGE, COUNT, MIN, MAX)
Simple Implicit vs. explicit measures
RELATED and RELATEDTABLE functions
Logical and Conditional Functions
Basic date and time functions
Understanding Auto Date/Time

Hands-on:

Create calculated columns for data categorization
Build basic KPI measures (Total Sales, Profit Margin)
Calculate running totals and cumulative values
Create rank-based calculated columns
Implement percentage calculations

Skills you will learn:

DAX expression writing
Row vs filter context
KPI measure development
Cross-table calculations
Aggregation logic implementation

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Module 7: Advanced DAX

Topics:

Evaluation Context and Context Transition
CALCULATE function advanced usage
ALL, ALLEXCEPT, ALLSELECTED functions
Time intelligence functions (YTD, QTD, MTD, SAMEPERIODLASTYEAR)
Period-over-period calculations (YoY, MoM)
Moving averages and rolling calculations
FILTER and CALCULATETABLE functions
VALUES, DISTINCT, and SELECTEDVALUE

Hands-on:

Create YoY, MoM, QoQ comparison measures
Implement rolling 12-month averages
Calculate same period last year with fiscal calendar
Build dynamic period selection using slicers
Create custom fiscal year calculations

Skills you will learn:

Context transition control
Advanced CALCULATE patterns
Time intelligence modeling
Period comparison analysis
Virtual table filtering

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Module 8: Iterator Functions and Complex Calculations

Topics:

Iterator functions (SUMX, AVERAGEX, COUNTX, RANKX)
SUMMARIZE and ADDCOLUMNS for virtual tables
Statistical functions (MEDIAN, STDEV, PERCENTILE)
EARLIER function for nested row contexts
ABC/Pareto analysis using DAX
Complex filtering scenarios
Dynamic measures with SWITCH
Variables (VAR) for optimization

Hands-on:

Build ABC analysis for product categorization
Create customer RFM segmentation
Calculate weighted averages and complex KPIs
Implement Pareto analysis (80/20 rule)
Build dynamic top N analysis
Create cohort analysis measures

Skills you will learn:

Iterator-based calculations
Virtual table construction
ABC/Pareto implementation
Complex KPI modeling
DAX performance optimization

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Module 9: Data Visualization Fundamentals

Topics:

Visualization selection criteria
Bar and column charts (clustered, stacked, 100% stacked)
Line and area charts
Pie, donut, and treemap charts
Scatter and bubble charts
Tables and matrix visuals
Card and multi-row card visuals
Conditional formatting rules
Tooltips and drill-through configuration

Hands-on:

Build executive KPI dashboard with cards
Create sales trend analysis with line charts
Design product performance comparison with bar charts
Implement conditional formatting for variance analysis
Create matrix with drill-down hierarchies
Build interactive scatter plot for correlation analysis

Skills you will learn:

Visualization selection strategy
KPI dashboard design
Interactive drill-down setup
Conditional formatting logic
Analytical chart configuration

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Module 10: Advanced Visualizations and Custom Visuals

Topics:

Gauge, KPI, and waterfall charts
Funnel and ribbon charts
Decomposition tree for root cause analysis
Key influencers visual
Q and A visual and natural language queries
Custom visuals from AppSource
Python and R visuals integration
Exploring Spatial Insights with Geospatial Analytics

Hands-on:

Build sales funnel with conversion metrics
Create waterfall chart for P&L analysis
Implement decomposition tree for drill-through analysis
Build key influencers for customer churn analysis
Import and configure custom visuals (Gantt, Sankey)
Create geographic analysis with shape maps

Skills you will learn:

Visual storytelling
AI visual implementation
Custom visual integration
Geospatial analytics modeling
Statistical visualization integration

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Module 11: Report Design - UX/UI Best Practices

Topics:

Report design principles and best practices
Color theory and accessible color palettes
Custom themes and JSON theme files
Buttons, bookmarks, and navigation
Mastering Layout: Z-order, Grouping, and Binning
Page navigation and drill-through pages
Mobile layout optimization
Report performance optimization
Accessibility features (alt text, tab order)

Hands-on:

Design a corporate-branded report template
Create custom theme JSON file
Implement bookmark navigation for story-telling
Build drill-through pages for detailed analysis
Optimize mobile layout for tablets and phones
Apply accessibility standards (WCAG compliance)

Skills you will learn:

UX/UI report design
Custom theme development
Mobile layout optimization
Accessibility compliance implementation

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Module 12: Interactive Reports

Topics:

Report, page, and visual-level filters
Slicer types and configurations
Sync slicers across multiple pages
Drill-down, drill-through, and drill-up
Cross-highlighting and cross-filtering
Edit interactions configuration
Field parameters for dynamic measures
Relative date filtering

Hands-on:

Build multi-page interactive sales dashboard
Implement cross-report drill-through
Create field parameters for user-selected measures
Configure complex slicer interactions
Build dynamic what-if scenarios
Implement hierarchical navigation structure

Skills you will learn:

Slicer synchronization setup
Dynamic field parameters
Cross-report navigation design
Interactive dashboard development

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Module 13: Power BI Service

Topics:

Power BI Service workspace architecture
Publishing from Desktop to Service
Workspace roles (Admin, Member, Contributor, Viewer)
Apps creation and distribution
Sharing reports and dashboards
Subscriptions and data alerts
Comments and collaboration features
Tracing Data Lineage and Impact Analysis

Hands-on:

Create and configure workspaces
Publish reports and create apps
Set up role-based access control
Configure email subscriptions
Create data-driven alerts
Implement content distribution strategy

Skills you will learn:

Power BI Service publishing
Workspace security management
App distribution strategy
Role-based access configuration
Deployment pipeline governance

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Module 14: Dashboards and Power BI Mobile

Topics:

Dashboard vs. report differences
Pinning visuals to dashboards
Dashboard tiles configuration
Real-time dashboards with streaming datasets
Dashboard themes
Mobile app features and capabilities
Phone layout design
Q&A on dashboards

Hands-on:

Build executive dashboard with pinned tiles
Create real-time streaming dashboard
Design mobile-optimized phone layout
Configure natural language Q&A
Implement mobile-specific features
Build location-aware mobile report

Skills you will learn:

Dashboard design configuration
Streaming data integration
Mobile layout optimization
Natural language Q&A setup
Executive dashboard storytelling

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Module 15: Data Refresh, Gateway, and Dataset Management

Topics:

On-Premises Data Gateway Configuration
Personal vs Enterprise Gateway
Dataset Refresh Types (Scheduled and On-Demand)
Gateway Installation and Configuration
Incremental Refresh Setup and Policies
Refresh Failure Troubleshooting
Dataset Storage Modes (Import, DirectQuery, Composite)
Query Caching and Performance Optimization
Dataset Endorsement and Certification

Hands-on:

Install and configure enterprise gateway
Set up scheduled refresh with multiple data sources
Implement incremental refresh for large datasets
Troubleshoot common refresh failures
Configure hybrid connectivity scenarios
Optimize dataset for query performance

Skills you will learn:

Enterprise refresh configuration
Gateway lifecycle management
Incremental refresh modeling
Refresh issue diagnostics
Dataset performance optimization

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Module 16: Securing Data and Tracking Metrics

Topics:

Security Layers in Power BI
Row-Level Security (RLS) Implementation
Static Roles vs Dynamic Security
USERNAME() and USERPRINCIPALNAME() Functions
Role-Based Data Access
Object-Level Security
Testing RLS with “View As”
Sensitivity Analysis and Compliance
Security Best Practices

Hands-on:

Implement RLS for regional sales managers
Create dynamic security using user mapping table
Build multi-level security hierarchy
Test RLS with different user personas
Configure object-level security for sensitive data
Document security model and role assignments

Skills you will learn:

Row-level security design
Dynamic role configuration
Object-level security setup
Access validation testing
Data governance implementation

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Module 17: Advanced Analytics – What-If Parameters and Forecasting

Topics:

What-If Parameters Creation
Scenario Analysis and Modeling
Built-in Forecasting and Trend Lines
Clustering and Outlier Detection
Statistical Analysis Functions
Calculated Tables for Scenario Modeling
Building Data-Driven Scorecards
Streamlining Deployment Pipelines
Power BI Embedded Analytics

Hands-on:

Create sales forecasting with confidence intervals
Implement customer clustering analysis
Build inventory optimization model
Create Monte Carlo simulation
Develop cohort retention analysis

Skills you will learn:

Scenario modeling techniques
Forecasting model development
Clustering analysis implementation
Predictive simulation modeling
Advanced analytics dashboards

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Module 18: AI-Powered Analytics

Topics:

Key Influencers Visual for Pattern Analysis
Smart Narratives for Automated Storytelling
Decomposition Tree for Root Cause Analysis
Quick Insights and Pattern Discovery
Creating and Managing Dataflows
Azure Cognitive Services Integration
Text Analytics and Sentiment Analysis
Anomaly Detection
Natural Language Q&A Optimization
Python Visuals and Predictive Modeling

Hands-on:

Implement sentiment analysis on customer feedback
Create automated insights dashboard
Apply anomaly detection to sales data
Build image classification for product categorization
Optimize Q&A with synonyms and featured questions

Skills you will learn:

AI visual analytics
Sentiment analysis modeling
Anomaly detection techniques
Cognitive services integration
Python predictive analytics

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Module 19: Copilot for Power BI and Generative AI Integration

Topics:

Microsoft Copilot in Power BI Overview
Copilot: Preparing Data for AI
AI-Assisted Report Page Creation
Copilot for DAX Formula Generation
Natural Language Narrative Summaries
AI-Powered Data Transformation Suggestions
Fabric Copilot Integration
Power BI Model Context Protocol (MCP) Servers

Hands-on:

Create report pages using Copilot suggestions
Generate complex DAX measures with AI assistance
Build AI-powered executive summaries
Implement smart narrative for dynamic insights
Create conversational analytics with Q&A
Develop AI-augmented dashboards

Skills you will learn:

Copilot report generation
AI-assisted DAX development
AI-driven data preparation
Conversational analytics design
Generative BI integration

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Module 20: End-to-End Enterprise Analytics Solution

Topics:

Requirements Gathering and Analysis
Data Architecture Design
Data Modeling and Transformation
Advanced DAX Implementation
Report and Dashboard Design
Security and Governance Implementation
Deployment and Maintenance Strategy
Documentation and Knowledge Transfer

Hands-on:

Build end-to-end sales and operations analytics solution
Connect to SQL, Excel, and API data sources
Design star schema with 10+ tables
Create complex DAX measures
Build 5 interactive reports with advanced visuals
Implement RLS and security model
Deploy to Power BI Service with scheduled refresh

Skills you will learn:

Enterprise analytics architecture
Advanced DAX modeling
Governance framework implementation
Production BI deployment
Solution design presentation

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Module 21: Getting Started with Microsoft Fabric (Self-Paced)

Topics:

Why Fabric After Power BI
Fabric Architectural Fundamentals
OneLake Unified Storage Concept
Fabric Workloads Overview
Semantic Models Hosted on Fabric Data
Fabric Capacities and Workload Isolation
Power BI’s Role in Fabric Analytics Lifecycle

Hands-on:

Enable Fabric in Power BI tenant
Explore Fabric workspace and workloads
Create a Lakehouse and review structure
Inspect OneLake storage layout
Connect Power BI to Fabric-backed data

Skills you will learn:

Fabric architecture fundamentals
Unified analytics framework
OneLake storage management
Fabric–Power BI integration
Capacity governance basics

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Module 22: Fabric Data Engineering with Power Query, Apache Spark and Delta (Self-Paced)

Topics:

Cloud-Based Data Engineering in Fabric
Fabric Power Query Editor (Cloud Execution)
Visual Query Editor using Dataflows Gen2
Delta Tables and Transactional Storage
Apache Spark Notebooks in Fabric
When to Use Power Query vs Spark
Data Engineering Patterns for Power BI Consumption

Hands-on:

Build Dataflows Gen2 using Visual Query Editor
Apply transformations using Fabric Power Query
Create Delta tables in Lakehouse
Use Spark notebooks for large-scale transformations
Validate Power BI consumption from engineered data

Skills you will learn:

Cloud transformation workflows
Spark data processing
Delta Lake implementation
Data pipeline design
BI-ready data engineering

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Module 23: Real-Time Intelligence and Streaming Analytics (Self-Paced)

Topics:

Real-Time Intelligence Workload Overview
Eventstream Concepts and Data Ingestion
Streaming Data into OneLake
KQL Databases and Query Concepts
Power BI Integration with Real-Time Datasets
Use Cases for Operational Dashboards

Hands-on:

Create an Eventstream in Microsoft Fabric
Ingest streaming data into a KQL database
Query streaming data using KQL
Build a real-time Power BI dashboard
Monitor latency and data freshness

Skills you will learn:

Real-time architecture design
Event-driven ingestion
KQL query development
Streaming dashboard creation
Latency monitoring techniques

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Module 24: Performance, AI and Operations in Fabric Analytics (Self-Paced)

Topics:

Direct Lake Execution Model (Fabric-Specific)
Cross-Workspace Semantic Model Reuse
Fabric Capacity Management and Throttling
Monitoring Fabric Workload Performance
Copilot in Fabric for Analytics Assistance
Cost Optimization and Operational Best Practices

Hands-on:

Build a Direct Lake–backed semantic model
Reuse semantic models across workspaces
Monitor Fabric capacity utilization
Deploy an optimized Fabric-to-Power BI solution

Skills you will learn:

Direct Lake optimization
Capacity management strategy
Semantic model reuse
Performance monitoring
AI-assisted analytics

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Advanced Power BI Certification Course with GenAI

Why should you take Advanced Power BI Certification Course with GenAI?

Curriculum

Module 1: Introduction to Business Intelligence and Power BI Ecosystem

Module 2: Data Source Connectivity and Data Preparation Fundamentals

Module 3: Power Query Editor - Data Transformation Mastery

Module 4: Advanced Power Query - Data Shaping and Integration

Module 5: Data Modeling Fundamentals

Module 6: DAX Fundamentals

Module 7: Advanced DAX

Module 8: Iterator Functions and Complex Calculations

Module 9: Data Visualization Fundamentals

Module 10: Advanced Visualizations and Custom Visuals

Module 11: Report Design - UX/UI Best Practices

Module 12: Interactive Reports

Module 13: Power BI Service

Module 14: Dashboards and Power BI Mobile

Module 15: Data Refresh, Gateway, and Dataset Management

Module 16: Securing Data and Tracking Metrics

Module 17: Advanced Analytics – What-If Parameters and Forecasting

Module 18: AI-Powered Analytics

Module 19: Copilot for Power BI and Generative AI Integration

Module 20: End-to-End Enterprise Analytics Solution

Module 21: Getting Started with Microsoft Fabric (Self-Paced)

Module 22: Fabric Data Engineering with Power Query, Apache Spark and Delta (Self-Paced)

Module 23: Real-Time Intelligence and Streaming Analytics (Self-Paced)

Module 24: Performance, AI and Operations in Fabric Analytics (Self-Paced)

Advanced Power BI with GenAI Course Description

What is Advanced Power BI Certification Training with GenAI by Edureka?

Who should enroll in Advanced Power BI Training with GenAI?

What are the prerequisites for Edureka's Advanced Power BI Training?

What topics are covered in the Advanced Power BI Certification Training with GenAI course?

Does the Advanced Power BI course include Microsoft Fabric content?

What are the system requirements for this Advanced Power BICertification Training with GenAI?

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Advanced Power BI FAQs

Will I get access to recorded sessions if I miss a live class?

Is Power BI relevant if my organization uses Tableau or other BI tools?

How is Power BI different from Excel for data analysis?

Is Advanced Power BI a strong career choice in 2026?

How is Generative AI transforming Power BI?

What industries hire Advanced Power BI professionals with AI skills?

What is the difference between Advanced Power BI and traditional BI tools?

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