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CPMAI_v7 EXAM PREPARATION

Prepare Smarter for the CPMAI_v7 Exam

Build your exam confidence with flexible preparation resources designed around the latest CPMAI_v7 exam objectives. Practice at your own pace using PDF questions, online exam simulations, or desktop practice software.

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Question #1 (Topic: Demo Questions)

Your team was given a large dataset and has been tasked with organizing the data by type to make better insights from the results. You are facing problems with the approach that the previous project lead used which was a regression algorithm.

What type of algorithm is the best approach for this project?

A.

Multiclass Classification


B.

Regression


C.

Clustering


D.

Binary (or Binomial) Classification

Correct Answer: C
Explanation:

When the goal is to group or organize unlabeled data into meaningful categories, CPMAI specifies the use of unsupervised clustering algorithms—for example, “if it is determined that an unsupervised clustering algorithm such as K-means will be used, the tool should support such needs” . Clustering is the appropriate method for discovering groupings by type.

Question #2 (Topic: Demo Questions)

Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.

What stage of CPMAI is the team currently working on?

A.

Phase I


B.

Phase II


C.

Phase III


D.

Phase IV


E.

Phase V


F.

Phase VI

Correct Answer: A
Explanation:

The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .

Question #3 (Topic: Demo Questions)

During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.

As the project lead, what should you do?

A.

Evaluate your data and use only what you have and build all models in house.


B.

Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.

C.

Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.

D.

Have one team build all models in-house and the other team use pre-trained models and see which team’s models perform better.

Correct Answer: C
Explanation:

The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project’s domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.

Question #4 (Topic: Demo Questions)

Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks “creepy” and they would rather not interact with it. What’s going on here?

A.

Lack of understanding the robot’s usefulness


B.

The bot is falling into The “Uncanny Valley”


C.

Bias towards the robot


D.

Safety and reliability issues that impact bot usefulness

Correct Answer: B
Explanation:

This reaction is a classic example of the Uncanny Valley phenomenon, where a nearly human-like robot triggers discomfort or eeriness in users because it sits in the valley between clearly robotic and convincingly human appearances. Although not explicitly named in the CPMAI glossary, addressing this user experience concern falls under Continuous Improvement and Respect for People, ensuring cognitive solutions are designed for positive user acceptance.

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