QUESTION: 37

A very important aspect of each model is how good a model or a given predictor is in predicting the required behavior.
When building a predictive model, the use of testing and validation samples___________________

is mandatory for segmentation
validates the quality of input data
enables model validation in strategies
increases the accuracy of models

Answer(s): D

Explanation:

A predictive model is a mathematical function that estimates the probability of an outcome based on input data.
When building a predictive model, the use of testing and validation samples increases the accuracy of models123. Testing and validation samples are subsets of data that are used to evaluate how well a model performs on new data that was not used to train the model. Testing and validation samples help prevent overfitting, which is when a model learns too much from the training data and fails to generalize to new data.

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QUESTION: 38

As a data scientist, you want to use a predictive model to detect potential churn for a telecom company.
Which three options do you have? (Choose Three)

Import a third party PMML model
Use a Google ML model
Create a Text extraction model
Create an adaptive self-learning model
Use Pega machine learning to build a model
Use a machine learning service

Answer(s): A,D,E

Explanation:

Import a third party PMML model1: PMML stands for Predictive Model Markup Language, which is an XML-based standard for representing predictive models. You can import a PMML model that was created by another tool or platform into Pega and use it in your strategies. Create an adaptive self-learning model1: An adaptive model is a type of predictive model that learns from customer responses and adapts its predictions over time. You can create an adaptive model in Pega and configure its parameters, such as learning rate, decay rate, and performance goal. Use Pega machine learning to build a model1: Pega machine learning is a feature that allows you to build predictive models using various algorithms, such as decision trees, logistic regression, neural networks, and random forests. You can use Pega machine learning to build a model from your data and evaluate its performance.

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QUESTION: 39

The standardized model operations process (MLOps) lets you replace a low-performing predictive model that drives a prediction with a superior one.
When you place the new model in shadow mode in the production environment, the current model___________

uses the outcomes of the new model as predictors
is automatically replaced
drives the prediction
no longer drives the prediction

Answer(s): C

Explanation:

When you place the new model in shadow mode in the production environment, the current model still drives the prediction, but the new model runs in parallel and collects performance data for comparison.

Reference:

https://academy.pega.com/module/predictive-analytics/topic/mlops

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QUESTION: 40

The management team at U+ Insurance wants to improve the experience of dissatisfied customers.
The customers send the feedback through email.
To detect the sentiment of the incoming emails, which type of prediction do you need to configure in Prediction Studio?

Pega Customer Decision HubTM prediction.
Sentiment detection does not require any predictions.
Case management prediction.
Text analytics prediction.

Answer(s): D

Explanation:

To detect the sentiment of the incoming emails, you need to configure a text analytics prediction1234 in Prediction Studio. A text analytics prediction is a type of prediction that uses natural language processing (NLP) to analyze text data and extract insights, such as topics, entities, and sentiments. You can use a text analytics prediction to detect the sentiment of an email based on its content and assign a score ranging from -1 (negative) to 1 (positive). This can help you improve the customer experience by identifying dissatisfied customers and taking appropriate actions.

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Free PEGACPDS88V1 Exam Braindumps (page: 10)

QUESTION: 37

Explanation:

QUESTION: 38

Explanation:

QUESTION: 39

Explanation:

Reference:

QUESTION: 40

Explanation: