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Interactive CPMAI_v7 Practice Exam - Download CPMAI_v7 Demo

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 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.

Topic 2

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 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.

Topic 4

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 5

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 Sample Questions (Q33-Q38):

NEW QUESTION # 33
Your team is looking to develop an RPA bot to help with back-office processes such as data entry. What type of bot should your team be creating?

A. RPA is not the right solution to this problem
B. Unattended bot
C. Attended bot
D. Business Process Outsourcing

Answer: B

Explanation:
In CPMAI's examination of AI patterns, Unattended bots are designed to run autonomously in back-office environments without human supervision, executing repetitive tasks like data entry at scale. This contrasts with Attended bots, which require a user to trigger or interact with them in real time.
Thought for 13 seconds

NEW QUESTION # 34
Enhancing and cleaning data is an important action during which phase of CPMAI?

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

Answer: A

Explanation:
Phase III: Data Preparation is dedicated to all data-centric tasks, including cleaning ("Clean data") and enhancement ("Enhance & Augment data") of raw inputs so they're ready for model consumption.
=========

NEW QUESTION # 35
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. Find a source of already labeled data
B. Hire a Contractor Workforce
C. Get your Users to Do it
D. Contract with Third Party Data Labeling Firms

Answer: C

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 # 36
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?

A. You have selected the wrong algorithm
B. You are underfitting the data
C. You have selected the wrong data
D. You are overfitting the data

Answer: D

Explanation:
Overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, causing it to perform well on seen data but poorly on unseen data. The CPMAI Glossary defines overfitting as "a modeling error where a model learns the training data too well, including its noise, resulting in poor performance on new data."
=========

NEW QUESTION # 37
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs. Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

A. Make use of data from different departments
B. Make use of this data from surveillance footage
C. Make use of Synthetic Training Data
D. Make use of this data by having employees pose in the positions required

Answer: C

Explanation:
Synthetic data is "artificially generated data that mimics real-world data, used when actual data is scarce or sensitive." Generating synthetic training images of people in the required poses allows you to rapidly augment your dataset without logistical, privacy, or labeling overhead.
=========

NEW QUESTION # 38
......

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