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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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.

Topic 3

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.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
Enhancing and cleaning data is an important action during which phase of CPMAI?

A. Phase V
B. Phase III
C. Phase II
D. Phase I
E. Phase IV
F. Phase VI

Answer: B

Explanation:
The CPMAI v7 methodology groups all data-centric preparation activities-including both data cleansing ("Clean data") and data augmentation ("Enhance & Augment data")-into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.
Phase III: Data Preparation is defined in the Workbook's Table of Contents as covering Data Cleansing & Enhancement tasks ("Clean data" and "Enhance & Augment data") .
Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists "Task: Clean data" (bringing data quality to modeling-ready levels) and "Task: Enhance & Augment data" (producing derived attributes and new records) as core activities .
=========

NEW QUESTION # 71
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 Negative results
C. False Positive results
D. True Positive results

Answer: C

Explanation:
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.
=========

NEW QUESTION # 72
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 Nearest Neighbor
B. K-means
C. Neural Network
D. Hyperpersonalization

Answer: A

Explanation:
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.
=========

NEW QUESTION # 73
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

A. Move forward without determining which pattern of AI this falls under.
B. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
C. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
D. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.

Answer: C

Explanation:
In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification-mapping business objectives to one or more of the Seven Patterns of AI-so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .

NEW QUESTION # 74
The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?

A. Phase V
B. Phase III
C. Phase II
D. Phase I
E. Phase IV
F. Phase VI

Answer: B

Explanation:
Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines-including training and inference pipelines-alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots.

NEW QUESTION # 75
......

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