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| Section | Weight | Objectives |
|---|---|---|
| Ethics, Governance & Future Trends | 10% | - Emerging Trends & Future of AI - Explainable AI & Transparency - AI Ethics, Bias & Fairness - Data Privacy, Security & Compliance |
| AI in Business & Consulting | 25% | - Cost-Benefit Analysis & ROI Calculation - AI Project Management & Governance - AI Strategy & Roadmap Development - Identifying AI Use Cases & Opportunities - Implementation Planning & Change Management |
| Machine Learning & Algorithms | 20% | - Model Training, Evaluation & Optimization - Types of Machine Learning
|
| Data Preparation & Engineering | 15% | - Data Cleaning, Preprocessing & Transformation - Feature Engineering & Selection - Data Quality & Governance - Data Collection & Sources |
| AI Technologies & Tools | 15% | - AI Applications: NLP, Computer Vision, Predictive Analytics - Automation & RPA Integration - Frameworks: TensorFlow, PyTorch, Scikit-learn - Cloud AI Platforms (AWS, Azure, Google Cloud) |
| AI Fundamentals & Concepts | 15% | - AI vs Traditional Programming - AI Lifecycle and Workflow - Introduction to Artificial Intelligence
|
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NEW QUESTION # 52
Which of the following is NOT a CORRECT element of the Planning and execution phase in the risk framework?
Answer: A
Explanation:
The correct answer is D. Ethics because ethics is not best treated as a single operational element of the planning and execution phase. In an AI risk framework, the planning and execution phase usually focuses on practical implementation activities such as defining the AI use case, aligning the solution with strategy, assessing financial feasibility, designing the product or solution, and preparing it for release. These activities help convert an AI concept into a working business or technical solution.
Conceptualization of the AI use case is correct because every AI initiative must begin with a clearly defined problem, objective, and intended business value. Strategy is also correct because the AI solution must align with organizational goals and risk appetite. Finance is relevant because organizations must consider cost, investment, expected return, and resource allocation. Design and release of the final product or solution is also part of execution.
Ethics is important across the entire AI lifecycle, but it is not the specific planning and execution element listed here. Therefore, the best answer is D. Ethics .
NEW QUESTION # 53
Select the BEST choice for ML solutions architecture coverage.
Answer: E
Explanation:
The correct answer is E. a, b and c only because ML solution architecture must cover the complete path from business need to technical implementation. Business understanding is essential because an ML solution should begin with a clear problem statement, business objective, success criteria, expected value, and operational impact. Without business understanding, the model may solve the wrong problem or fail to create measurable value.
Identification and verification of ML techniques are also part of ML solution architecture because teams must choose suitable algorithms, validate model approaches, compare methods, and confirm that the selected technique fits the data, use case, performance expectations, and business constraints. System architecture of the ML technology platform is equally important because ML solutions require data pipelines, infrastructure, compute resources, model deployment environments, monitoring, security, scalability, and integration with enterprise systems.
Since all three areas are important parts of ML solution architecture coverage, the best answer is E .
NEW QUESTION # 54
Select the most CORRECT risk-scoring methodology function statement for prospective risk.
Answer: B
Explanation:
The correct answer is C because prospective risk is forward-looking. It focuses on estimating future model risk by using the most current risk condition, present indicators, and existing risk posture of the model. In AI governance and model risk management, prospective risk assessment helps organizations anticipate possible future issues such as performance degradation, bias, drift, compliance exposure, operational failure, or business impact before those risks become actual problems.
Option A is not the most correct because analyzing historical model performance is more closely linked with retrospective risk assessment. Historical performance can support risk analysis, but it does not fully define prospective risk. Option B is not accurate because "upcoming model performance" is not directly available for analysis; future performance must be predicted, not already analyzed. Option E is incorrect because A and B are not both accurate statements. Therefore, the most correct statement is C. Prospective risk leverages the most current risk of the model to predict the overall model risk for future cycles .
NEW QUESTION # 55
Which of the following is an example of AGI?
Answer: D
Explanation:
The correct answer is E. None of the above because Artificial General Intelligence, or AGI, refers to an AI system that can understand, learn, reason, adapt, and perform intellectual tasks across many domains at a human-like level. AGI is different from narrow AI, which is designed to perform specific tasks within limited boundaries.
Google's search engine is not AGI because it is built to retrieve, rank, and organize information based on search queries. Amazon's recommendation engine is also not AGI because it is designed for a specific purpose: recommending products based on user behavior, preferences, and patterns. ChatGPT is a powerful generative AI and language model, but it is still not AGI because it does not possess true general intelligence, consciousness, self-awareness, or independent human-like reasoning across all domains.
Since none of the listed systems qualifies as Artificial General Intelligence, the correct answer is E. None of the above .
NEW QUESTION # 56
Supervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: A
Explanation:
The correct answer is A. labeled dataset . Supervised learning is a machine learning method in which an algorithm is trained using data that already contains the correct output labels or target values. Each training example includes input features and a known answer, allowing the model to learn the relationship between the inputs and the expected output. Once trained, the model can use that learned relationship to classify or predict outcomes for new data.
An unlabeled dataset is used in unsupervised learning, where the model identifies hidden patterns, clusters, or relationships without predefined labels. An "explained dataset" is not a standard machine learning category.
Option D is incorrect because supervised learning does not learn from both labeled and unlabeled datasets as its primary definition. Option E is also incorrect because "explained dataset" is not the correct term.
Therefore, supervised learning learns from a labeled dataset , making A the correct answer.
NEW QUESTION # 57
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