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[General] Pass Guaranteed Updated PMI - Accurate CPMAI_v7 Test

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【General】 Pass Guaranteed Updated PMI - Accurate CPMAI_v7 Test

Posted at before yesterday 07:14      View：10 | Replies：1        Print      Only Author   [Copy Link] 1#
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PMI CPMAI_v7 Exam Syllabus Topics:
TopicDetails
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
  • 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
  • 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 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.
Topic 5
  • 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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q97-Q102):NEW QUESTION # 97
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 simple algorithm such as Gaussian mixture
  • D. Pick a neural network algorithm since you know this works well for supervised learning approaches
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 # 98
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project.
What type of algorithm would you select for this problem?
  • A. Binary (or Binomial) Classification
  • B. Regression
  • C. Multiclass Classification
  • D. Clustering
Answer: A
Explanation:
A spam filter must decide between exactly two categories-spam or not spam-making it a binary (or binomial) classification task. The CPMAI Glossary defines binary classification as "a classification task where data is categorized into one of two classes (e.g., spam vs. not spam)."
=========

NEW QUESTION # 99
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. Senior management
  • B. Data Engineering and Data Scientists
  • C. All roles are critical to staff in the Four different AI Tech environments
  • D. Data Engineering
  • E. Data Scientists
Answer: D
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 # 100
You are working for a large multinational organization and have been assigned to a new project. For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address? (Select all that apply.)
  • A. Securing model data and metadata
  • B. Securely storing all data collected for training purposes
  • C. Securing data at rest
  • D. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
Answer: A,B,C,D
Explanation:
Under Domain VI: Trustworthy AI - Task 2: Implementing AI Privacy and Security, CPMAI mandates that teams must:
Apply data privacy principles and "ensure compliance with General Data Protection Regulation (GDPR)" and other relevant laws regardless of location .
Identify and protect Personally Identifiable Information (PII) and "develop comprehensive AI safety and security protocols," which encompasses securing both model data and metadata and enforcing security monitoring for production systems .
Implement best practices for data anonymization, defense against adversarial attacks, and the secure handling of datasets-this includes securing data at rest and securely storing training data in accordance with organizational and regulatory requirements .
=========

NEW QUESTION # 101
Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?
  • A. Model Drift
  • B. Model reevaluation
  • C. Model retraining
  • D. Model Operationalization
Answer: C
Explanation:
The CPMAI methodology's Model Iteration Approach (Phase V) explicitly calls out that "models will need continuous iteration, especially if they are only marginally providing the desired results" and requires teams to
"detail approach that will be used to iterate this model to improve on any of the results in this Phase" . Failing to include a model retraining pipeline means the model cannot adapt to new data distributions, leading to performance degradation over time.
=========

NEW QUESTION # 102
......
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Posted at before yesterday 20:18        Only Author  2#
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