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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation & Engineering | 15% | - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation - Feature Engineering & Selection - Data Quality & Governance |
| AI in Business & Consulting | 25% | - Identifying AI Use Cases & Opportunities - Cost-Benefit Analysis & ROI Calculation - AI Project Management & Governance - AI Strategy & Roadmap Development - Implementation Planning & Change Management |
| AI Fundamentals & Concepts | 15% | - AI Lifecycle and Workflow - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| Machine Learning & Algorithms | 20% | - Model Training, Evaluation & Optimization - Types of Machine Learning
|
| Ethics, Governance & Future Trends | 10% | - Emerging Trends & Future of AI - AI Ethics, Bias & Fairness - Explainable AI & Transparency - Data Privacy, Security & Compliance |
| AI Technologies & Tools | 15% | - Cloud AI Platforms (AWS, Azure, Google Cloud) - Frameworks: TensorFlow, PyTorch, Scikit-learn - AI Applications: NLP, Computer Vision, Predictive Analytics - Automation & RPA Integration |
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NEW QUESTION # 27
Which of the following is NOT a CORRECT element of the Planning and execution phase in the risk framework?
Answer: E
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 # 28
Deep Learning is a subset of ____.
Answer: B
Explanation:
The correct answer is A. Machine Learning . Deep learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers to learn patterns from data. These layered neural networks can automatically discover features and representations from large datasets, which makes deep learning especially useful for image recognition, speech recognition, natural language processing, recommendation systems, and generative AI applications.
Artificial intelligence is the broader field that includes machine learning, expert systems, reasoning systems, robotics, natural language processing, and other intelligent technologies. Machine learning is a branch within artificial intelligence, and deep learning is a further subset within machine learning. Artificial Narrow Intelligence refers to AI systems designed for specific tasks, while Artificial General Intelligence refers to a theoretical system with broad human-like intelligence. Since deep learning is most directly and correctly classified as a subset of machine learning, the best answer is A .
NEW QUESTION # 29
An AI agent learns to play a game by taking actions, receiving rewards for good moves, and penalties for poor moves. Over time, it improves its strategy to maximize total reward. This is an example of ______.
Answer: D
Explanation:
Reinforcement learning is the correct answer because the AI agent learns by interacting with an environment and improving its behavior based on rewards and penalties. The goal of reinforcement learning is to learn a policy or strategy that maximizes cumulative reward over time. This differs from supervised learning, where the model learns from labeled input-output examples. It also differs from unsupervised learning, where the model searches for hidden patterns without labels or rewards. Semi-supervised learning is incorrect because the scenario does not involve a mix of labeled and unlabeled data. Regression learning is also incorrect because regression predicts continuous numerical values, while this example focuses on action selection and reward optimization. Therefore, the correct answer is C. reinforcement learning .
NEW QUESTION # 30
Which of the following is a CORRECT statement for the Data and AI Analytics Business Model Maturity Index?
Answer: D
Explanation:
The correct answer is D. a and b only because the Data and AI Analytics Business Model Maturity Index is mainly used to guide and assess how effectively an organization uses data, analytics, and AI to improve business and operational models. Option A is correct because a maturity index provides a roadmap that helps organizations understand where they are currently and what capabilities they need to develop next. This supports better use of analytics, data-driven decision-making, and AI-enabled transformation.
Option B is also correct because a maturity index works as a benchmark. Organizations can compare their current maturity level against defined stages, measure progress, identify gaps, and evaluate improvement in analytics capabilities over time.
Option C is not the best statement because "focus on ROI and team" is too narrow and incomplete. ROI and team capability may be considered in analytics planning, but they do not fully define the purpose of the maturity index. Therefore, the best answer is D. a and b only .
NEW QUESTION # 31
Which of the following is a step for the Value Engineering Framework?
Answer: D
NEW QUESTION # 32
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