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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 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.

Topic 2

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.

Topic 3

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.

Topic 4

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.

Topic 5

Domain VI Trustworthy AI: This section is designed for the Project Manager and focuses on ethical, responsible, and transparent AI development. It covers building trustworthy systems, dispelling misconceptions, evaluating real-world ethical concerns, defining responsible frameworks, and implementing mitigation tactics for unintended harms. It addresses data privacy, GDPR compliance, protection of PII, anonymization techniques, security against adversarial threats, and monitoring.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
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. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
D. The use of computing technology to enable machines to gain cognitive intelligence.

Answer: A

Explanation:
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.") .
=========

NEW QUESTION # 32
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?

A. Data Engineering
B. Senior management
C. Data Scientists
D. Data Engineering and Data Scientists
E. All roles are critical to staff in the Four different AI Tech environments

Answer: A

Explanation:
CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams "create a reusable data pipeline to collect, ingest, and prepare data for training" and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists.
While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.

NEW QUESTION # 33
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

A. Pick a simple algorithm such as naive bayes
B. Pick an ensemble method since you're not sure which algorithm will perform best
C. Pick a neural network algorithm since you know this works well for supervised learning approaches
D. Pick a simple algorithm such as Gaussian mixture

Answer: A

Explanation:
Naive Bayes classifiers are a family of "simple probabilistic classifiers based on Bayes' theorem with the
'naive' assumption of feature independence," making them fast to train and easy to interpret-ideal for teams new to AI tackling binary tasks like spam detection .
=========

NEW QUESTION # 34
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data?
As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

A. Get your Users to Do it
B. Hire a Contractor Workforce
C. Find a source of already labeled data
D. Contract with Third Party Data Labeling Firms

Answer: A

Explanation:
The Data Labeling task in Phase III: Data Preparation specifies that teams should identify labeling methods such as using internal staff, contracting third-party labelers, leveraging pre-existing labeled datasets, or combining those modes. Soliciting end-users to label data falls outside these recommended approaches and introduces uncontrolled variability and quality issues .
=========

NEW QUESTION # 35
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months.
What's wrong with this timeframe?

A. It's underestimating the project timeline by 3 months
B. It's not accounting for data preparation timelines
C. It fits into a waterfall timeframe, but not an agile project timeframe
D. It's not accounting for potential project delays

Answer: B

Explanation:
CPMAI's Phase III: Data Preparation is a distinct phase that encompasses data cleansing, augmentation, labeling, and pipeline construction. Because data engineering often accounts for the majority of AI project effort, omitting this phase from initial estimates leads to significant timeline underestimation. Project timelines must explicitly include Phase III activities to be realistic .
=========

NEW QUESTION # 36
......

The PMI CPMAI_v7 certification is a valuable credential that plays a significant role in advancing the PMI professional's career in the tech industry. With the Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam (CPMAI_v7) certification exam you can demonstrate your skills and knowledge level and get solid proof of your expertise. You can use this proof to advance your career. The PMI CPMAI_v7 Certification Exam enables you to increase job opportunities, promotes professional development, and higher salary potential, and helps you to gain a competitive edge in your job search.

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