Salesforce Service Cloud Consultant study plan

Map the full case lifecycle from creation to closure, naming the standard objects and fields involved at each stage

Diagram the service data model (Account, Contact, Case, Entitlement, Service Contract, Asset, Work Order) and the lookup relationships between them

Configure at least three case assignment paths (queue, escalation rule, assignment rule) in a developer org and verify each fires correctly

Explain how case status, priority, and record type interact with support processes and milestones

Build a multi-step support process with record types, path guidance, and validation rules for a sample support org

Configure case escalation rules and entitlement milestones, then test the breach behaviour against a defined SLA

Compare entitlement-versus-milestone-versus-escalation-rule behaviour and document when each is the right tool

Draft solution design notes for a client that needs two distinct support processes with different SLAs

Configure a service console with a Lightning page layout tailored to a case-working agent, including related lists, highlights panel, and utility bar

Set up at least two interaction channels (web-to-case or email-to-case plus one messaging or chat channel) and route inbound items to the correct queue

Configure macros, quick text, and keyboard shortcuts, and confirm each reduces clicks in a scripted agent scenario

Explain how Omni-Channel routing models (queue-based, skills-based, and capacity-based) differ and pick one for a given staffing scenario

Design a knowledge article data model including data categories, article types, and validation status workflow

Configure article visibility, channel deployment, and promotion rules for at least two audiences (internal vs. customer-facing)

Create a suggested-article and search configuration using keywords and data category filters, then test retrieval quality

Write a knowledge governance plan covering ownership, review cadence, and article lifecycle

List the Agentforce service building blocks (topics, actions, instructions, channels, grounding) and describe how each contributes to an agent

Identify at least four concrete service use cases where an AI agent adds value and four where a human agent should stay in the loop

Explain Einstein/Agentforce features relevant to service (case classification, reply recommendations, article generation, summaries) and the data prerequisites for each

Draft a channel strategy that combines self-service deflection, messaging, and assisted service

Build reports covering case volume, first contact resolution, average handle time, and SLA attainment, and explain the reporting granularity limits

Design a service dashboard for a support manager and a separate one for an agent, and justify the metric choices

Explain how historical trend and snapshot reporting differ, and when case snapshots are required

Describe how AI-generated summaries and classifications should be monitored with analytics after go-live

Write a phased rollout plan covering discovery, design, build, UAT, and hypercare for a mid-size service deployment

Identify the top five adoption risks in a service console rollout and pair each with a mitigation

Articulate how Service Cloud capabilities map to common industry scenarios (financial services, healthcare, manufacturing, public sector)

Document a data migration and user-provisioning checklist for a service implementation

Re-run a timed 60-question mixed practice set and score each domain separately

Re-teach the three weakest domains from notes without looking at documentation, then verify against official docs

Revisit every missed item and write the underlying rule or configuration fact that resolves it

Finalise a one-page quick-reference sheet covering the case data model, console setup steps, and knowledge visibility rules