CPMAI_V7 PREMIUM EXAM, VALID CPMAI_V7 MOCK EXAM

CPMAI_v7 Premium Exam, Valid CPMAI_v7 Mock Exam

CPMAI_v7 Premium Exam, Valid CPMAI_v7 Mock Exam

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Tags: CPMAI_v7 Premium Exam, Valid CPMAI_v7 Mock Exam, CPMAI_v7 Questions Answers, Latest CPMAI_v7 Exam Review, CPMAI_v7 Certification Torrent

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
When building your model you need to make sure you're not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

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

Answer: E

Explanation:
Phase V: Model Evaluation is where you validate not only technical performance but also alignment with the business success criteria defined in Phase I. Within this phase, the KPI Measurement task focuses on
"measuring and evaluating the model against Phase I objectives," ensuring the solution meets its intended business outcomes before moving forward.
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NEW QUESTION # 25
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. Contract with Third Party Data Labeling Firms
  • C. Hire a Contractor Workforce
  • D. Find a source of already labeled data

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 .
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NEW QUESTION # 26
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. Compliance with Data Privacy Laws even if they are out of your physical jurisdiction
  • B. Securely storing all data collected for training purposes
  • C. Securing data at rest
  • D. Securing model data and metadata

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 .
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NEW QUESTION # 27
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?

  • A. Explainable AI
  • B. Transparent AI
  • C. Responsible AI
  • D. Governed AI

Answer: C

Explanation:
Under Domain VI: Trustworthy AI in the CPMAI Exam Content Outline, Responsible AI covers establishing policies, guidelines, and governance that ensure AI solutions are developed for positive, ethical use and prevent misuse. Defining proper-use guidelines and embedding ethical intent into facial recognition directly align with Responsible AI practices .


NEW QUESTION # 28
You have been tasked at your organization to manage a large language model (LLM) project. Identify what LLMs are useful for. (Select all that apply.)

  • A. Classify and categorize content
  • B. Improve search quality
  • C. Code generation
  • D. Machine Translation
  • E. Text summarization
  • F. Process automation

Answer: A,B,C,D,E

Explanation:
Large language models (LLMs) excel at generating, understanding, and manipulating text. According to the CPMAI Glossary:
Content summarization is a core NLP function: "the process of using AI/ML techniques to generate a concise overview of a larger body of text." Machine translation: "the use of AI to automatically translate text or speech from one language to another." Classification: LLMs can assign content to categories via fine-tuned classification heads ("classifier" term), making them suitable for content categorization.
Code generation: As generative AI, LLMs can produce new content, including code snippets, by pattern learning from programming corpora ("generative AI" term).
Search quality improvement: LLMs can rephrase queries, expand keywords, and rank results to enhance search relevance. Though not explicitly detailed in the glossary, this capability derives directly from their generative and understanding strengths.
LLMs are not designed for pure process automation (option A), which is handled by RPA or orchestrators rather than by text-centric models.
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NEW QUESTION # 29
......

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