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Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:

Section	Objectives
Data Understanding	- Data sources and data types in Pega - Exploratory data analysis concepts
Pega Decisioning and AI Integration	- Embedding predictive analytics in Pega applications - Adaptive models and decision strategies
Data Preparation	- Feature engineering for predictive models - Data cleansing and transformation
Data Sets and Integration	- Data flows and dataset configuration - External data integration with Pega platform
Predictive Analytics	- Predictive model types in Pega (classification, regression) - Model training and evaluation

Pegasystems Pega Certified Data Scientist (PCDS) 87V1 Sample Questions:

1. The implementation of Next-Best-Action must involve

A) building a product catalog
B) inclusion of third party predictive models
C) defining a prioritization formula using contact policies
D) defining business issue and group hierarchy

2. A company wants to create a data set that includes mock-up customer add Which method is available to populate these fields?

A) Bayesian
B) Propensity
C) Monte Carlo
D) Import

3. A legal firm wants to use text analytics for easier and faster access to information to help with compliance related issues. The legal firm needs a taxonomy of legal concepts.
What is a taxonomy?

A) A list of valid categories
B) A list of business rules
C) A sentiment analysis model
D) The output of an expert survey

4. The decision components used on the strategy canvas can be individually configured Which function is available when configuring the Group By component?

A) Average
B) Divide
C) True if Some
D) Multiply

5. The Prediction Studio portal supports the creation of two distinct types of predictive Scoring models are used to__________________.

A) differentiate between good, better and best
B) score different propositions
C) predict continuous behavior
D) predict binary behavior

Solutions:

Question # 1 Answer: D

Question # 2 Answer: C

Question # 3 Answer: A

Question # 4 Answer: A

Question # 5 Answer: D