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Certified Pega Data Scientist (PEGACPDS24V1) Free Sample Questions

Description

Covers Customer Decision Hub predictions, adaptive model monitoring, building and evaluating predictive models with MLOps, prediction patterns, governance, Pega Process AI, and Pega NLP analytics.

12 free sample questions150 in the full practice test

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PEGACPDS24V1 Sample Questions

  1. Question 1

    Intermediate

    AI for Customer Decision Hub · Next-Best-Action paradigm and the CDH decisioning framework

    A retail bank is implementing Pega Customer Decision Hub to optimize their 1:1 customer engagement strategy. During the arbitration phase, the Next-Best-Action (NBA) engine must determine the final priority of various credit card offers. Which combination of factors does the standard NBA arbitration formula use to rank these offers?

    Answer and explanation

    Correct answer: A

    In Pega Customer Decision Hub, the standard Next-Best-Action arbitration formula prioritizes actions by multiplying Propensity (likelihood to accept), Context weighting (relevance in the moment), Action value (financial impact), and Business levers (strategic priority). This comprehensive approach ensures that both customer needs and business objectives are balanced in real-time.

  2. Question 2

    Beginner

    AI for Customer Decision Hub · Out-of-the-box CDH predictions and their role in arbitration

    A marketing team is relying on Pega Customer Decision Hub's out-of-the-box predictions to drive their retention campaigns. They want to ensure that the AI automatically identifies customers who are at risk of leaving so that proactive retention offers can be prioritized. Which out-of-the-box prediction directly provides this capability?

    Answer and explanation

    Correct answer: C

    Predict Churn Risk (or Likelihood to Churn) is a standard prediction used to assess how likely a customer is to leave. This propensity is then used in arbitration to prioritize retention offers over cross-sell offers for at-risk customers.

  3. Question 3

    Beginner

    AI for Customer Decision Hub · Next-Best-Action paradigm and the CDH decisioning framework

    True or False: In Pega Customer Decision Hub, engagement policies (such as Eligibility and Applicability) are evaluated after AI models calculate the propensity for all available actions to ensure the highest propensity offer is always presented.

    Answer and explanation

    Correct answer: B

    False. Engagement policies are evaluated first to filter the set of actions. AI models (propensity calculations) and arbitration are only applied to the subset of actions that pass the Eligibility, Applicability, and Suitability rules. This saves computational resources and ensures business compliance.

  4. Question 4

    Intermediate

    Adaptive Analytics · How adaptive models self-learn from customer responses

    A data scientist is monitoring a newly launched adaptive model in Prediction Studio. The model is intended to predict the likelihood of a customer accepting a premium credit card offer. Because the model was launched with no historical data, it currently relies on self-learning. What underlying mathematical approach does the Adaptive Decision Manager (ADM) use to update the model's scoring dynamically as new customer responses arrive?

    Answer and explanation

    Correct answer: B

    Pega's Adaptive Decision Manager (ADM) fundamentally relies on a Naive Bayes algorithm and Bayesian scoring techniques. This approach is highly efficient for online learning, allowing the model to instantly update predictor bins and probabilities as each new customer response (accept/reject) is recorded.

  5. Question 5

    Advanced

    Adaptive Analytics · Interpreting adaptive model reports and predictor performance

    When reviewing the performance of an adaptive model in the Prediction Studio Bubble Chart, a data scientist notices that a specific model has a very high success rate (Y-axis) but a low model performance / AUC (X-axis). What is the most likely business implication of this scenario?

    quadrantChart title Adaptive Model Bubble Chart Analysis x-axis "Low Performance (AUC)" --> "High Performance (AUC)" y-axis Low Success Rate --> High Success Rate quadrant-1 High Value / Needs Review quadrant-2 Optimal Models quadrant-3 Dormant / Poor Models quadrant-4 Niche / Target Refinement Current Model: [0.2, 0.8]
    Answer and explanation

    Correct answer: C

    A high success rate combined with a low AUC (near 50) means that almost every customer is accepting the offer, making it impossible for the model to find differentiating predictors. This often happens when an offer is 'too good to be true' (e.g., free money), indicating the business might be giving away value unnecessarily.

  6. Question 6

    Beginner

    Adaptive Analytics · How adaptive models self-learn from customer responses

    A consultant is optimizing the predictors for an adaptive model in Pega Customer Decision Hub. They notice that the ADM automatically organizes similar predictors into clusters. What is the primary purpose of predictor grouping in Pega Adaptive Decision Manager?

    Answer and explanation

    Correct answer: B

    Predictor grouping in ADM identifies highly correlated predictors (e.g., 'Age' and 'Date of Birth') and groups them together. When calculating propensity, the model only uses the single most predictive active variable from each group. This prevents the Naive Bayes algorithm from double-counting correlated evidence, which would skew the propensity score.

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