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CPMAI_v7 Fragenpool & CPMAI_v7 Fragen Und Antworten

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PMI CPMAI_v7 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Machine Learning: This section is aimed at the Data
AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.

Thema 2

Data for AI: This domain targets the Data
AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.

Thema 3

AI Fundamentals: This section measures the abilities of a Project Manager and explores foundational AI concepts, including its definition, links to human cognition, and differences across AGI, Strong, Weak, and Narrow AI. It includes understanding the Turing Test and cognitive computing, dispelling myths, and applying augmented intelligence in business contexts. The historical progression of AI, such as AI winters, symbolic logic, expert systems, and fuzzy logic, is examined along with reasons for AI's current prominence and its role in digital transformation. The section continues to assess the identification of suitable AI use cases, understanding limitations, and adoption patterns like conversational AI, speech processing, anomaly detection, RPA, goal-driven systems, and integrated AI solutions.

Thema 4

CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam CPMAI_v7 Prüfungsfragen mit Lösungen (Q88-Q93):

88. Frage
You're being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you're actually solving a real business problem.
During Phase I: Business Understanding, you should consider at least one of the following (Select all that apply):

A. Has the "cool" factor
B. Enhance revenue
C. Explores a proof of concept for an AI project
D. Solves an already solved problem but does it better and cheaper
E. Solves a previously unsolved problem
F. Improve company competitiveness in the market

Antwort: B,D,E,F

Begründung:
Phase I begins with Determine Business Objectives to ensure the AI initiative aligns to real business needs.
Teams evaluate whether the solution will enhance revenue, solve a previously unsolved problem, improve competitive positioning, or "provide enough ROI/impact...and be better/more impactful than the current, non- cognitive, heuristic approach" (i.e., do something already addressed but do it better and cheaper). Proof-of- concepts (A) and "cool" factors (E) are not valid business objectives under CPMAI's Business Understanding tasks.

89. Frage
You just joined a new company and they want to start their first AI project. Senior management thinks the best approach is to just buy AI from a vendor. You know that AI is something you do, not something you buy.
What is your next best course of action to address this?

A. Share prior experiences with how your last team addressed this problem and their data quality issues
B. Share prior experiences with how your last team addressed this problem and how you solved it
C. Say nothing and let the team figure it out for themselves
D. Help senior management do research on AI vendors

Antwort: B

Begründung:
CPMAI's Differentiate AI Project Management Approaches task stresses that effective AI adoption requires building internal capabilities and understanding domain-specific challenges. By sharing your own team's past experiences-how you diagnosed the problem, structured the data, and developed AI solutions-you guide leadership toward establishing a homegrown, iterative AI practice rather than simply purchasing a black-box product .

90. Frage
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?

A. Use data from a previous project
B. Use cloud-based technology
C. Use brute force method
D. Use or extend a pre-trained model

Antwort: D

Begründung:
The Transfer Learning task in Phase IV: Model Development recommends leveraging a pre-trained model as the starting point for a new, related task. By fine-tuning rather than training from scratch, teams dramatically reduce compute time and data requirements-ideal for tight iteration cycles.

91. Frage
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
B. Collect enormous amounts of data - the more data the better.
C. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
D. Understanding which algorithms are best for your data needs.

Antwort: C

Begründung:
CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.

92. Frage
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:

A. False Negative results
B. True Positive results
C. True Negative results
D. False Positive results

Antwort: D

Begründung:
A false positive occurs when the model predicts the positive class (e.g., "tree") but the actual label is negative (e.g., "flower"). The confusion matrix definition confirms that mislabeling a negative instance as positive maps to the false positive count.
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93. Frage
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