Certified Pega Data Scientist study guide
Certified Pega Data Scientist · Work through your exam, one topic at a time.
This page is an independent study resource for candidates preparing for what is commonly searched as the Certified Pega Data Scientist credential. No exam named "Certified Pega Data Scientist" appears in the Pega Academy training catalog retrieved for this page, so logistics such as item count, duration, passing score, fee, retake rules, and validity are left unstated rather than guessed. The catalog does show where data-science-adjacent skills are taught: the Pega Process AI Essentials mission, listed at beginner level with 4 Modules, 3 Challenges, and 3 hrs 30 mins, tagged to Pega Platform, Process AI, Decision Management, and Case Management. A related Decision Management and Next Best Action mission, Preparation for 1:1 Customer Engagement Implementation, sits at intermediate level with 3 Modules, 9 Challenges, and 6 hrs 20 mins. The Certified Pega Data Scientist study guide has 4 sections and about 1,236 words.
01Pega Platform: Data Foundations for Data Science
The Pega Platform domain tests whether you can prepare and govern data for analytics inside Pega. Expect questions on case data modeling, data pages, data transforms, data sets, data flows, and how these artifacts support predictive modeling and reporting.
373 words, about 2 min02Process AI: Mining and Improving Workflows
Process AI tests how to use event data and AI-assisted analysis to improve workflows. The focus is process mining, task mining, process discovery, conformance checking, and automation opportunity assessment.
284 words, about 1 min03Decision Management: Models, Strategies, and Arbitration
Decision Management tests how predictive and rule-based logic combine to choose actions. Expect decision strategies, decision tables, decision trees, scorecards, adaptive models, Next-Best-Action, arbitration, and interaction history.
276 words, about 1 min04Case Management: Lifecycle Data for Prediction
Case Management tests how work is modeled and how case data becomes analytical features and labels. Expect case types, stages, steps, processes, child cases, data propagation, service levels, and case lifecycle.
303 words, about 2 min