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The Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam (CPMAI_v7) certification exam is one of the top-rated career advancement certifications in the market. This Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam (CPMAI_v7) exam dumps have been inspiring beginners and experienced professionals since its beginning. There are several personal and professional benefits that you can gain after passing the PMI CPMAI_v7 Exam. The validation of expertise, more career opportunities, salary enhancement, instant promotion, and membership of PMI certified professional community.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind. At what phase of CPMAI would Trustworthy AI be considered?

A. None of the phases
B. Phase VI
C. All phases
D. Phase V
E. Phase III
F. Phase IV
G. Phase I
H. Phase II

Answer: C

Explanation:
Trustworthy AI is not confined to a single phase but is woven throughout the entire CPMAI lifecycle:
The CPMAI Exam Content Outline under Domain VI: Trustworthy AI specifies tasks such as "Apply ethical AI concepts throughout the development lifecycle," "Ensure compliance with privacy/security requirements," and "Implement transparency and explainability" at every stage .
The CPMAI Workbook's Task Group: Trustworthy AI Requirements (covering transparency, explainability, ethics, compliance, and responsible-AI frameworks) appears as an overarching set of artifacts and considerations that map back to multiple phases-beginning with Business Understanding and continuing through Model Operationalization .
Thus, Trustworthy AI considerations apply across all CPMAI phases.

NEW QUESTION # 60
Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected.
What could have been done better to get the team to feel comfortable with this technology and use it? (Select all that apply.)

A. Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
B. Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.
C. Have the team that built the technology relay to employees this tool is to augment, and not replace their jobs.
D. Have upper management relay to employees this tool is to augment, and not replace their jobs.

Answer: A,B,C,D

Explanation:
The Continuous Improvement and Respect for People principle in CPMAI stresses involving end users early- gathering their pain points (A), clarifying that AI will augment rather than replace roles (B & C), and providing thorough training to build confidence (D). Engaging stakeholders throughout the project lifecycle and prioritizing user-centered design are key to adoption.

NEW QUESTION # 61
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data-centric projects.
What's the next best course of action?

A. Bring in data centric methodology best practices to get this project back on track
B. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
C. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
D. Hire an entirely new team making sure there is at least one data scientist on this new team

Answer: A

Explanation:
Domain II of the CPMAI Exam Content Outline highlights the need to "adapt traditional methodologies for data-centric projects" and "implement continuous AI project lifecycles" rather than treating AI as conventional software development. Bringing in CPMAI's data-centric best practices-phased, iterative, and focused on data understanding/preparation-directly addresses the root causes of AI project failures and realigns the team to proven AI project management frameworks.

NEW QUESTION # 62
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. Regression
B. Binary (or Binomial) Classification
C. Multiclass Classification
D. Clustering

Answer: D

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.

NEW QUESTION # 63
You have an Anomaly Detection project you're working on and you need a simple approach of clustering data into classified groups. Which algorithm is the best choice given this situation?

A. Neural Network
B. K-Means Clustering
C. Hidden Markov Model
D. Decision Tree

Answer: B

Explanation:
Clustering is defined as "an unsupervised process that partitions data into groups (clusters) based on similarity without preassigned labels." K-Means is the canonical unsupervised clustering algorithm, iteratively assigning points to K centroids to minimize within-cluster variance. This makes K-Means the simplest and most direct choice for grouping data in an anomaly-detection context.
=========

NEW QUESTION # 64
......

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