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Pegasystems PEGAPCDS87V1 Exam Overview:
| Certification Vendor: | Pegasystems |
| Exam Name: | Pega Certified Data Scientist (PCDS) 87V1 |
| Exam Number: | PEGAPCDS87V1 |
| Certificate Validity Period: | No formal expiration; Pega certifications remain valid with technology updates |
| Passing Score: | 70% |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Related Certifications: | Pega Certified Decisioning Consultant (PCDC) Pega Certified Decisioning Consultant 87V1 Pega Certified Marketing Consultant |
| Real Exam Qty: | 50 |
| Exam Format: | Multiple Choice, Scenario-Based Questions |
| Exam Price: | Varies by region (typically US$250–$300) |
| Sample Questions: | Pegasystems PEGAPCDS87V1 Sample Questions |
| Exam Way: | Delivered via Pearson VUE test centers or online proctored delivery. |
| Pre Condition: | Recommended background in Data Science and AI concepts; familiarity with Pega Customer Decision Hub and predictive/adaptive analytics. |
| Official Syllabus URL: | https://academy.pega.com/exam/pega-certified-data-scientist-2 |
Pegasystems PEGAPCDS87V1 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Next-Best-Action Concepts | - Core decisioning principles
|
| Topic 2: Decision Strategies | - Developing and evaluating decision strategies
|
| Topic 3: Pega Process AI | - Process AI fundamentals
|
| Topic 4: Text Analytics / NLP | - Leveraging text analytics
|
| Topic 5: Adaptive Analytics | - Adaptive model usage
|
| Topic 6: Predictive Analytics | - Creating predictive models
|
Pegasystems Pega Certified Data Scientist (PCDS) 87V1 Sample Questions:
1. A company wants to simulate decisions that requires large amounts of data. However, the organisation's live data is inaccessible. Your advice is to use a Monte Carlo data set.
The Monte Carlo method________________
A) combines external data sets into a larger data set
B) makes the organisation's live data accessible
C) generates data that the company can use as input for adaptive decisioning
D) enables the company to generate random data for most of its application needs
2. A Scoring Model allows you to differentiate between
A) Good, Better, Best
B) Good, Bad, Unknown
C) Good, Bad
D) Accept, Reject, Maybe Later
3. When building a predictive model, what is a valid predictor data type?
A) Symbolic
B) Character
C) Boolean
D) String
4. You are the Decisioning Consultant on an Al-powered one-to-one Customer Engagement implementation project. You are asked to design the Next-Best-Action prioritization expression that balances the customer needs with the business objectives.
What factors do you consider in the prioritization expression?
A) product eligibility rules
B) business levers
C) customer contact rules
D) product compatibility rules
5. evidence an assessment of its viability, the Adaptive Model produces three outputs:
Propensity, Performance and
What is evidence in the context of an Adaptive Model?
A) The number of customers who exhibited statistically similar behavior
B) The number of customers who have responded to the modeled offer
C) The number of statistical bins used to evaluate the response
D) The likelihood of a statistically similar behavior
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
By Alma
