Pega CPDC study plan

Define Next-Best-Action, decisioning, and the difference between predictive and adaptive decisioning in your own words

Map how Customer Decision Hub components (data layer, strategies, arbitration, adaptive models, simulation) connect end to end

Sketch the Decisioning lifecycle from customer interaction through action presentation

List the core data objects (customer, interaction history, propositions, action) and what each stores

Describe how customer data is ingested and mapped into decisioning data sets

Explain the role of propositions and actions and how eligibility is determined

Identify strategy components (strategies, decision tables, decision trees, when rules) and when to use each

Trace a single decision through a strategy to see where each component fires

Build a simple strategy that filters a candidate action set using eligibility conditions

Explain how Next-Best-Action Designer organizes engagement policies, action groups, and treatments

Distinguish applicability, suitability, and prioritization rules and where each is configured

Configure a basic action group with at least two actions and a treatment

Design engagement policies that apply business constraints (contact limits, suppression) to decisions

Order and weight actions in an action group and explain how priority affects selection

Explain how NBA Designer output feeds arbitration

Review a configuration and identify common misconfigurations in eligibility and suitability

Explain arbitration as the process that selects among eligible, suitable actions

Describe how adaptive models learn from outcomes and how propensities are produced

List the inputs and outputs of an adaptive model and how feedback is captured

Distinguish adaptive model behavior from predictive model behavior

Describe how simulation testing evaluates strategy changes before deployment

Explain the purpose of historical data in simulation and the limits of that approach

Interpret simulation results to decide whether a change is safe to promote

Identify risks of overfitting or biased feedback loops in adaptive learning

Answer practice questions across all four domains and log every miss with a reason

Re-read notes on the two weakest domains and rewrite the key concepts from memory

Explain out loud the full decision path from data to arbitration without notes

Resolve any remaining terminology gaps by writing definitions for unclear terms

Complete a full timed mock exam and score it by domain

Review every incorrect answer and confirm the correct reasoning

Revisit the weakness log from week 7 and verify each item is closed

Summarize the exam's core decisioning narrative in a short outline for final recall