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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Fundamentals & Concepts | 15% | - AI Lifecycle and Workflow - Introduction to Artificial Intelligence
|
| Topic 2: Ethics, Governance & Future Trends | 10% | - Data Privacy, Security & Compliance - Explainable AI & Transparency - Emerging Trends & Future of AI - AI Ethics, Bias & Fairness |
| Topic 3: AI Technologies & Tools | 15% | - AI Applications: NLP, Computer Vision, Predictive Analytics - Automation & RPA Integration - Cloud AI Platforms (AWS, Azure, Google Cloud) - Frameworks: TensorFlow, PyTorch, Scikit-learn |
| Topic 4: Data Preparation & Engineering | 15% | - Feature Engineering & Selection - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation - Data Quality & Governance |
| Topic 5: AI in Business & Consulting | 25% | - AI Project Management & Governance - Implementation Planning & Change Management - Cost-Benefit Analysis & ROI Calculation - AI Strategy & Roadmap Development - Identifying AI Use Cases & Opportunities |
| Topic 6: Machine Learning & Algorithms | 20% | - Types of Machine Learning
- Popular Algorithms
|
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NEW QUESTION # 62
Select the most CORRECT risk-scoring methodology function statement for prospective risk.
Answer: C
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 # 63
Select the MOST CORRECT statement for Few-shot learning.
Answer: A
Explanation:
The correct answer is E. b and c only because few-shot learning means a model learns or adapts to a new task using only a small number of examples. In generative AI and large language model usage, few-shot prompting often provides a few demonstrations so the model can understand the expected pattern, format, classification logic, or response style. Option B is correct because few-shot learning uses a limited number of examples rather than a large training dataset.
Option C is also correct because few-shot learning depends on the model's prior knowledge learned during pretraining. The model uses that existing knowledge to generalize from the small set of examples and apply the same logic to new inputs. Option A is not the best statement because "a large number of examples" does not match the idea of few-shot learning. Therefore, the most correct answer is E. b and c only .
NEW QUESTION # 64
Artificial narrow intelligence ANI is also commonly expressed as ____.
Answer: A
Explanation:
The correct answer is A. Weak AI . Artificial Narrow Intelligence, or ANI, is commonly called Weak AI because it is designed to perform a specific task or a limited set of tasks within a defined domain. Examples include recommendation engines, search engines, spam filters, facial recognition systems, voice assistants, fraud detection tools, and chatbots. These systems can perform their assigned functions effectively, but they do not possess general intelligence, consciousness, self-awareness, or human-like understanding across all domains.
Strong AI and General AI refer to Artificial General Intelligence, which would be capable of broad reasoning, learning, and problem-solving across many tasks like a human. SuperAI refers to a theoretical level of intelligence beyond human capability. ExpertAI is not the standard expression for ANI. Since ANI is task- specific and limited in scope, it is correctly expressed as Weak AI .
NEW QUESTION # 65
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 # 66
A model is trained using historical customer records where each record already contains the correct outcome, such as "churn" or "not churn." The model then predicts whether future customers are likely to churn. This is an example of ______.
Answer: C
Explanation:
Supervised learning is used when a machine learning model is trained on labeled data. In this case, the historical customer records already include the correct outcome labels, such as "churn" or "not churn." The model learns the relationship between customer attributes and the known outcome, then applies that learned relationship to predict outcomes for new customers. This is a classic classification problem. Unsupervised learning is incorrect because it works with unlabeled data and is commonly used for clustering or discovering hidden patterns. Reinforcement learning is incorrect because there is no reward-based decision-making environment described. Generative learning is not the best answer because the task is prediction, not creating new content. Therefore, the correct answer is A. supervised learning .
NEW QUESTION # 67
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