Preparing for the PEGACPDS23V1 Certified Pega Data Scientist 23 exam? You're in the right place! To pass the exam with confidence, you'll need to familiarize yourself with essential Pega concepts and practice thoroughly. One effective way to prepare is by using the newest Certified Pega Data Scientist PEGACPDS23V1 exam questions from PassQuestion. These resources are designed to align with the actual exam structure, helping you grasp key topics and pass the certification with ease. With the newest Certified Pega Data Scientist PEGACPDS23V1 Exam Questions, you can confidently pass the exam and advance your career.

What is the Certified Pega Data Scientist Certification?

The Certified Pega Data Scientist certification is intended for data scientists who want to deepen their expertise in applying artificial intelligence (AI) within Pega's suite of solutions. This certification focuses on Pega Process AI, Pega Customer Decision Hub, and Pega Customer Service, offering practical knowledge to make real-time decisions in customer interactions.

The certification emphasizes Pega's next-best-action (NBA) framework, allowing data scientists to build models using predictive, adaptive, and text analytics. Candidates also gain hands-on experience integrating predictions into case management workflows and customer engagement strategies, which are crucial in today's AI-driven business landscape.

Benefits of Becoming a Certified Pega Data Scientist

Earning the PEGACPDS23V1 certification provides several benefits:

• Career Advancement: Certification validates your expertise and can open doors to new job opportunities.
• Enhanced AI Skills: You’ll develop practical skills for creating adaptive and predictive models.
• Improved Customer Engagement: Pega’s tools enable personalized interactions, driving higher customer satisfaction.

Overview of the Exam Information

Here's a quick rundown of the key details for the PEGACPDS23V1 exam:
Exam Code: PEGACPDS23V1
Number of Questions: 50
Duration: 90 minutes
Passing Score: 70%
Language: English
Prerequisites: Candidates should have prior experience as a data scientist.

Detailed Breakdown of Exam Topics

AI for Customer Decision Hub (8%)

This section covers how Pega's Customer Decision Hub enables organizations to deliver personalized customer experiences. The focus is on using AI to provide real-time predictions and decisions that drive meaningful customer interactions. You’ll need to understand how Pega's predictions fit within decision strategies.

Adaptive Analytics (22%)

Adaptive analytics deals with self-learning models that continuously evolve based on new data. As a test-taker, you’ll learn how to build and monitor adaptive models to ensure they stay accurate over time. Another critical aspect is exporting model data to track performance and feed external analytics tools.

Predictive Analytics (26%)

Predictive analytics involves forecasting future events based on past trends and patterns. In this section, you’ll focus on how to create predictions using Pega’s tools and integrate them with decision-making processes. A crucial part of this topic is MLOps (Machine Learning Operations), which ensures that models are maintained and deployed efficiently.

Prediction Patterns (20%)

Prediction patterns help in organizing and defining decision-making strategies. In this area, candidates must demonstrate their ability to design and implement decision strategies that align with business goals. A deep understanding of prediction patterns ensures accurate forecasting and customer engagement.

Governance (2%)

Governance ensures that AI models and decisions comply with business policies and industry regulations. Though this section carries a small weight, it is vital for ensuring the responsible use of AI in data-driven processes.

Pega Process AI (8%)

Pega Process AI is designed to automate workflows and predict potential disruptions, such as fraud detection or missed SLAs (Service-Level Agreements). This topic tests your ability to use AI in operational processes to enhance efficiency and reduce risks.

Pega NLP (14%)

NLP (Natural Language Processing) allows Pega to interpret unstructured data, like text from emails or chat messages. In this section, you’ll need to understand how to use text analytics for routing emails and entity extraction with chatbots to enhance customer service interactions.

View Online Certified Pega Data Scientist PEGACPDS23V1 Free Questions

1. In a decision strategy, the Adaptive Model decision component belongs to the ________.
A. Decision Analytics category
B. Business Rules category
C. Arbitration category
D. Predictive Model category
Answer: A
 
2. As a data scientist, you are tasked with configuring two predictions that are driven by an adaptive model: one for an inbound channel and one for an outbound channel.
To which setting do you need to pay extra attention?
A. Response timeout
B. Adaptive model
C. Predictor fields
D. Control group
Answer: B 
 
3. Which of the following is NOT a use case for NLP in Pega?
A. Sentiment analysis
B. Text classification
C. Speech recognition
D. Entity extraction
Answer: C
 
4. U+ Insurance uses Pega Process AI? and wants straight-through processing of claims with a low fraud risk. As a data scientist, you create a prediction that calculates the probability that a claim is fraudulent.
What type of prediction do you create to meet this requirement?
A. A case management prediction.
B. A text analytics prediction.
C. A Customer Decision Hub prediction.
D. A fraud detection prediction
Answer: D
 
5. What are two results of an Adaptive Model? (Choose Two)
A. Priority
B. Propensity
C. Segment
D. Performance
Answer: C, D
 
6. How can Adaptive Analytics models be deployed in production environments?
A. Manually copying and pasting the code into a production system
B. Ignoring production deployment and only using models for offline analysis
C. Only deploying models in a sandbox environment
D. Using a software development process to test and deploy models
Answer: D
 
7. The standardized model operations process (MLOps) lets you replace a low-performing predictive model that drives a prediction with a new one.
Which feature of MLOps lets you monitor the new model in the production environment without affecting the business outcomes?
A. Change request
B. Shadow mode
C. Historical data capture
D. Connection to machine learning services
Answer: B
 
8. What is the difference between predictive and adaptive analytics?
A. Predictive models can predict a continuous value.
B. Predictive models predict customer behavior.
C. Adaptive models use the customer data as predict*
D. Predictive models have evidence.
Answer: C
 
9. What happens when you increase the performance threshold setting of an Adaptive Model rule?
A. The number of active predictors increases.
B. The correlation threshold decreases.
C. The performance of the model is increased.
D. The number of active predictors may decrease.
Answer: B
 
10. U+ Telecom wants to engage in proactive retention to reduce churn. As a data scientist, you create a prediction that calculates the probability that a client is likely to cancel a subscription.
What type of prediction do you create?
A. Case management_____
B. Customer Decision Hub
C. Text analytics
Answer: B