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PMI CPMAI_v7 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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 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

Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam CPMAI_v7 Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?

A. The "less is better" approach
B. There is no correct approach
C. The "Goldilocks" approach
D. The "more is better" approach

Antwort: C

Begründung:
Phase II: Data Understanding centers on identifying just the right amount of data for model training-neither too little (risking underfitting) nor too much (wasting resources and introducing noise). This balanced-
"Goldilocks"-approach ensures you collect sufficient high-quality, relevant records to meet cognitive objectives without incurring unnecessary cost or complexity.
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13. Frage
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

A. Data preparation
B. Data Mining or Data Analytics
C. Data Integration
D. Data Governance

Antwort: B

Begründung:
CPMAI defines analytics as "the use of statistical and computational methods to extract meaningful insights from data." When the goal is to discover and highlight patterns and insights within existing datasets, applying data mining or analytics techniques is the appropriate action.

14. Frage
Which of the following best describes the technical definition of Machine Learning?

A. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
B. The application of pre-defined rules and algorithms to solve complex problems.
C. The use of computing technology to enable machines to gain cognitive intelligence.
D. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.

Antwort: A

Begründung:
Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
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15. Frage
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?

A. K-means
B. Neural Network
C. K Nearest Neighbor
D. Hyperpersonalization

Antwort: C

Begründung:
CPMAI's Generic Task Group: Select Modeling Technique in Phase IV: Model Development outlines common cognitive algorithms. For recommendation systems-which rely on finding similar user or item profiles-the K-Nearest Neighbor algorithm is the canonical choice, using customer purchase vectors to locate "nearest neighbors." In contrast, K-means is purely unsupervised clustering, Neural Networks are more complex and not necessary for basic collaborative filtering, and Hyperpersonalization is an AI pattern, not an algorithm.
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16. Frage
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time.
However, what's the one area you should not have the AutoML tool help with?

A. Automatic model assessment
B. Automatic algorithm selection
C. Iterative modeling and evaluation
D. Automatic hyperparameter tuning
E. Automatic model selection

Antwort: C

Begründung:
CPMAI's Usage of AutoML task instructs teams to "Document how AutoML tools will be used for model creation" and to verify that the output can be integrated into the overall I/O flow . While AutoML excels at automating algorithm selection, model selection, hyperparameter tuning, and even preliminary performance metrics, CPMAI places iterative modeling and evaluation squarely under the manual Model Evaluation phase-where teams must interpret results against business success criteria and decide on next steps.
Entrusting that high-level, iterative decision-making to an AutoML black box would undermine the human- centric evaluation that CPMAI mandates.
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17. Frage
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