PEGACPDS24V1 Sample Questions & Answers
Free Certified Pega Data Scientist (PEGACPDS24V1) practice questions with worked answers and explanations. See how the ExamJungle simulator prepares you — then jump into the full test.
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Real questions from the Certified Pega Data Scientist (PEGACPDS24V1) practice test — answers and explanations included. Showing 6 of 12 free samples.
- Question 1
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]Show answer & 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.
- Question 2
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?
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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.
- Question 3Select 2
When an adaptive model is created in Pega, it learns within a specific "model context." Which TWO of the following dimensions are standard components used to define the model context in Customer Decision Hub? (Select TWO)
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Correct answers: A, C
In CDH, an adaptive model's context is typically defined by the business hierarchy: Issue, Group, Name (Action), Channel, and Direction. 'Issue' is the top level of this hierarchy (e.g., Sales, Retention).
The 'Channel' (e.g., Web, Email, Call Center) is a critical part of the model context. A customer might have a high propensity to accept an offer on the Web but a low propensity via Email, so ADM creates separate model instances per channel.
- Question 4
A data science team needs to perform deep offline analysis on the predictor bins and raw learning data generated by their adaptive models over the past 6 months. To access this data, they must utilize the ADM data mart. The correct approach to extract this data for offline analysis is to export the ______ and ______ datasets from the ADM data mart.
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Correct answer: B
The ADM data mart specifically consists of two key datasets: Model Snapshots (containing overall model performance, success rate, and metadata at various points in time) and Predictor Binning (containing the detailed statistical bins, intervals, and behavior of every predictor). Exporting these allows for comprehensive offline analysis.
- Question 5
A system administrator is reviewing the Adaptive Models monitoring tab in Prediction Studio. They notice several models are classified as 'Dormant'. What does this classification indicate about these specific models?
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Correct answer: B
In Prediction Studio, a model is flagged as 'Dormant' if it has not received any response data over a configured period of time. This usually indicates that the action associated with the model is no longer being presented to customers, or there is an issue with the response capture feedback loop.
- Question 6
In Pega Customer Decision Hub, what is the fundamental difference in how Predictive Models and Adaptive Models are trained and deployed?
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Correct answer: A
This is the core distinction. Predictive models require a historical dataset with known outcomes to be trained offline before they are useful. Adaptive models start with no knowledge and update their scoring algorithms dynamically (online learning) as they receive real-time accept/reject feedback from customers.
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