Pegasystems PEGACPDS25V1 問題練習

Question No : 1
Which component can you use to integrate a prediction into a strategy?

• A.Set Property
• B.Prediction component
• C.Decision Table
• D.Data Page

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正解:
Explanation:
The Prediction component is added to decision strategies to invoke predictions. It connects the strategy to Prediction Studio models and applies the prediction results in real time.

Question No : 2
Why would a data scientist choose a binary classifier over a scorecard?

• A.More interpretability
• B.Better for rule-based systems
• C.Higher scalability for complex tasks
• D.Lower processing speed

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正解:
Explanation:
Binary classifiers scale well with complex, large datasets. They offer better performance and flexibility compared to static scorecards, which are rule-heavy and less adaptive.

Question No : 3
Which of the following are valid output types for Pega prediction models?

• A.Text explanation
• B.Propensity score
• C.Recommendation ID
• D.Outcome class

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正解:
Explanation:
Prediction models generate outputs such as propensity scores (probabilities) and classified outcomes (e.g., churn/retain). These outputs guide strategy decisions.

Question No : 4
Which of the following can improve the accuracy of a predictive model? (Choose two)

• A.Drop all categorical variables
• B.Engineer new features
• C.Remove noisy predictors
• D.Set AUC to zero

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正解:
Explanation:
Feature engineering and removing noisy predictors can significantly enhance model accuracy. These steps improve signal clarity and reduce confusion in the learning process.

Question No : 5
How is lift used in evaluating a prediction?

• A.To reduce AUC
• B.To normalize predictors
• C.To compare model with random chance
• D.To create outcome rules

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正解:
Explanation:
Lift measures how much better the model performs compared to random selection. A higher lift indicates a stronger ability to identify positive outcomes.

Question No : 6
Why should you review a confusion matrix for your prediction?

• A.To understand UI rules
• B.To determine model scoring rules
• C.To analyze true and false positives/negatives
• D.To track deployment status

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正解:
Explanation:
The confusion matrix summarizes model predictions and outcomes. It helps analyze the balance between true/false positives and negatives, aiding in tuning and threshold setting.

Question No : 7
What are some common causes for poor model performance? (Choose two)

• A.Too many model thresholds
• B.Overfitting
• C.Data imbalance
• D.Excess predictors

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正解:
Explanation:
Poor performance can stem from overfitting (model learns noise) or class imbalance (skewed outcome distribution), leading to inaccurate or biased predictions.

Question No : 8
What does AUC indicate about a prediction model?

• A.Model latency
• B.Feature engineering strength
• C.Discriminatory power
• D.Execution speed

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正解:
Explanation:
AUC (Area Under the Curve) measures how well the model separates positive and negative outcomes. A value closer to 1 indicates high predictive power.

Question No : 9
Which steps are involved in deploying a prediction? (Choose two)

• A.Creating a new engagement policy
• B.Mapping the prediction to a strategy
• C.Configuring predictors manually
• D.Connecting it to an outcome path

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正解:
Explanation:
To deploy a prediction, it must be mapped into a decision strategy and linked to an outcome path. This allows the model to influence real-time action decisions.

Question No : 10
Which metric indicates the balance between precision and recall?

• A.F1-score
• B.Lift
• C.Gini coefficient
• D.AUC

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正解:
Explanation:
The F1-score is the harmonic mean of precision and recall. It provides a balanced measure of model accuracy when false positives and false negatives are both costly.

Question No : 11
What does the term “training data” refer to in prediction models?

• A.UI interaction logs
• B.Test environments
• C.Historical labeled data
• D.Predictor type templates

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正解:
Explanation:
Training data consists of historical cases with known outcomes. This data is used by the model to learn patterns and make accurate future predictions.

Question No : 12
Which two elements must be mapped when setting up outcomes in a prediction? (Choose two)

• A.Positive label
• B.Source model ID
• C.Prediction threshold
• D.Negative label

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正解:
Explanation:
Outcome mapping requires identifying which values from the input data represent positive and negative responses. This allows the model to learn the success/failure patterns.

Question No : 13
How can you validate the quality of a new prediction model?

• A.Run adaptive tuning
• B.Export the evidence
• C.Evaluate ROC and AUC scores
• D.Publish to CDH

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正解:
Explanation:
ROC (Receiver Operating Characteristic) and AUC (Area Under Curve) scores are used to evaluate model quality. They indicate how well the model discriminates between classes.

Question No : 14
Which of the following can be used as a predictor in a prediction model? (Choose two)

• A.Treatment ID
• B.Age
• C.Last purchase amount
• D.Case ID

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正解:
Explanation:
Predictors are variables that correlate with outcomes. Attributes like age or purchase amount are common predictors that help inform the model’s decision-making.

Question No : 15
Why would a data scientist use a scorecard over a binary classifier?

• A.To build rule-based thresholds
• B.To analyze model history
• C.To remove predictors
• D.To deploy adaptive models

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正解:
Explanation:
Scorecards use rule-based scoring logic, making them interpretable and easier to validate. They're useful for regulated environments needing transparent decision logic.