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Certified Pega Data Scientist (PEGACPDS26V1) 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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PEGACPDS26V1 Sample Questions

  1. Question 1

    Intermediate

    AI for Customer Decision Hub · Customer Decision Hub overview

    A data scientist is reviewing the Next-Best-Action Designer arbitration configuration for a telecommunications company using Pega Customer Decision Hub '26. The company wants to ensure that a retention offer is prioritized over a cross-sell offer when a customer is highly likely to churn, even if the cross-sell offer has a higher historical conversion rate. Which component of the arbitration formula must the data scientist adjust to achieve this specific outcome?

    Answer and explanation

    Correct answer: B

    Business levers allow you to apply business logic to prioritize certain actions over others under specific conditions. By applying a business lever (such as a weighting factor when a high churn propensity is detected), the arbitration formula will artificially boost the priority of the retention offer over the cross-sell offer, satisfying the business requirement.

  2. Question 2

    Beginner

    AI for Customer Decision Hub · Customer Decision Hub predictions

    An organization is deploying the out-of-the-box Predict Web Propensity model in Pega Customer Decision Hub. The marketing team notices that a small percentage of web visitors are not receiving the AI-driven next best actions, but instead receive a random selection of actions. What is the primary purpose of this behavior?

    Answer and explanation

    Correct answer: B

    In Pega CDH, a control group is a small, randomly selected audience segment that receives random actions rather than AI-optimized next best actions. This allows the organization to measure the effectiveness (lift) of the AI model by comparing the response rates of the target group against the control group.

  3. Question 3

    Intermediate

    Adaptive Analytics · Adaptive models

    A retail bank uses Adaptive Decision Manager (ADM) to personalize credit card offers. A data scientist introduces 50 new predictors, including several highly correlated demographic fields (e.g., 'Age' and 'Years in Workforce'). How does ADM natively handle these highly correlated predictors during model learning?

    Answer and explanation

    Correct answer: B

    To prevent issues associated with multicollinearity, Pega's Adaptive Decision Manager (ADM) automatically identifies highly correlated predictors and places them into predictor groups. During scoring, ADM only activates and uses the single best-performing predictor from each group, ignoring the others.

  4. Question 4

    Beginner

    Adaptive Analytics · Adaptive models

    True or False: In Pega Infinity '26, adaptive models must be taken offline periodically so that a data scientist can manually retrain them using the latest batch of customer response data.

    Answer and explanation

    Correct answer: B

    False. Adaptive models in Pega are self-learning. They continuously and automatically update their internal scoring algorithms in real-time as customer responses are captured, without requiring offline manual retraining by a data scientist.

  5. Question 5

    AdvancedMultiple answers

    Adaptive Analytics · Monitoring adaptive models

    While reviewing the Adaptive Model monitor tab in Prediction Studio, a data scientist examines the Bubble chart plotting Model Performance (AUC) on the Y-axis and Success Rate on the X-axis. Several models appear clustered in the bottom-right quadrant. Which TWO conclusions can be drawn about these specific models? (Select TWO)

    quadrantChart title Model Performance vs Success Rate x-axis Low Success Rate --> High Success Rate y-axis Low AUC --> High AUC quadrant-1 Stars quadrant-2 Niche quadrant-3 Dead quadrant-4 Cash Cows Model A: [0.8, 0.2] Model B: [0.9, 0.3]
    Answer and explanation

    Correct answers: A, C

    Models in the bottom-right quadrant have a high Success Rate (X-axis) but low Performance/AUC (Y-axis). Because they have a high success rate, they are generating positive responses and driving business value. However, their low AUC indicates they are not effectively differentiating between who will and will not accept the offer, essentially acting like a static rule or benefiting from a universally popular offer.

  6. Question 6

    Intermediate

    Adaptive Analytics · Adaptive models

    When configuring an adaptive model in Prediction Studio, a data scientist must define the outcome mapping. If the business wants the model to predict the likelihood of a customer clicking a web banner, how should the outcomes be categorized?

    Answer and explanation

    Correct answer: A

    In ADM outcome mapping, the behavior you want to predict (the positive outcome) is mapped as the 'Target' behavior. The negative or non-response (such as an impression without a click, or an explicit ignore) is mapped as the 'Alternative' behavior. The model calculates the propensity of the target behavior occurring.

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