K2 University https://validator.w3.org/feed/docs/rss2.html Blog Home Salesforce Platform Development Lifecycle and Deployment Architect Platform Developer II Platform Developer Salesforce Platform Administrator II Platform Administrator Marketing Cloud Engagement Administrator Configuration and Setup (Objectives 1, 2) – Platform Administrator Explain how to use best practices for writing effective prompts. Identify the considerations for retrievers in Data Cloud such as individual and ensemble. Given a scenario, identify the considerations for search type such as keyword, vector, and hybrid. Given a scenario, identify when it’s appropriate to use Agent API. Given a set of business requirements, recommend an appropriate use for AI agents in the customer service process. Explain agentic service capabilities, use cases, and configuration processes. Knowledge Check Using RAG for Conference Information Search Refining conference search by country with a flow action Knowledge Check Use the Agent to answer common questions Leverage the project management lifecycle to deliver a successful solution on time and within scope. Given a set of requirements, determine how to support different business and sales process scenarios for Campaigns, Leads, and Opportunities. Understand what generative AI tools are available in Agentforce for Sales. Understand what predictive AI tools are available in Sales Cloud. Identify ethical challenges of AI and apply Salesforce’s Trusted AI Principles to sales scenarios. Aura Debugging Exercises LWC Debugging Exercises Best Practices Exercises Aggregate Queries Exercises Field Modeling Exercises Object Modeling Exercises Database Normalization Exercises Set up Developer Org Turn on Data Cloud Turn on Einstein and Agentforce Set up Permission Sets Learn about the Service Agent Data Ingestion and Harmonization Identity Resolution Calculated Insight Template Triggered Prompt Flows Apply Data Cloud permissions, permission sets, and org-wide settings. Describe and configure the available data stream types and data bundles. Identify use cases for data spaces and create data spaces based on requirements. Manage and administer Data Cloud using reports, dashboards, flows, packaging, and data kits. Diagnose and explore data using Data Explorer, Profile Explorer, and APIs. Identify the different transformation capabilities within Data Cloud. Describe processes and considerations for data ingestion from different sources into Data Cloud. Define, map, and model data using best practices and aligning to requirements for identity resolution. Use available tools to inspect and validate ingested and modeled data.