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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ethics, Governance & Future Trends | 10% | - Data Privacy, Security & Compliance - AI Ethics, Bias & Fairness - Explainable AI & Transparency - Emerging Trends & Future of AI |
| Topic 2: AI in Business & Consulting | 25% | - Implementation Planning & Change Management - AI Project Management & Governance - Cost-Benefit Analysis & ROI Calculation - Identifying AI Use Cases & Opportunities - AI Strategy & Roadmap Development |
| Topic 3: Machine Learning & Algorithms | 20% | - Model Training, Evaluation & Optimization - Popular Algorithms
|
| Topic 4: AI Technologies & Tools | 15% | - Automation & RPA Integration - Cloud AI Platforms (AWS, Azure, Google Cloud) - Frameworks: TensorFlow, PyTorch, Scikit-learn - AI Applications: NLP, Computer Vision, Predictive Analytics |
| Topic 5: Data Preparation & Engineering | 15% | - Feature Engineering & Selection - Data Quality & Governance - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation |
| Topic 6: AI Fundamentals & Concepts | 15% | - Introduction to Artificial Intelligence
- AI Lifecycle and Workflow |
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NEW QUESTION # 55
Why is prompt important?
Answer: A
Explanation:
The correct answer is E. a, b and c only because all three statements explain why prompt design is important when working with generative AI and language models. A prompt is the instruction, question, or context given to an AI system to guide its response. When the prompt is clear, specific, and well-structured, the model is more likely to produce useful, relevant, and accurate output. This supports statement A because well- defined prompts help create a successful and productive conversation.
Statement B is also correct because poorly-defined prompts can make the conversation less useful. If the prompt is vague, incomplete, or confusing, the model may produce broad, irrelevant, or low-quality responses. Statement C is correct because unclear prompts can also lead to misleading content, especially when the model fills in missing details or interprets the request incorrectly. Therefore, prompt quality directly affects response quality, usefulness, and reliability, making E the best answer.
NEW QUESTION # 56
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: D
Explanation:
The correct answer is D. a and b only because AI provides strong benefits across the product development life cycle, especially by improving speed, decision quality, and data-driven design. Statement A is correct because AI can shorten the product development life cycle by automating research, analyzing customer feedback, generating product ideas, supporting rapid prototyping, improving testing, and helping teams identify risks or opportunities earlier.
Statement B is also correct because applying AI throughout the PDLC helps organizations use data consistently at every stage, from ideation and market research to design, testing, launch, and post-launch improvement. This means products are not only based on data at the beginning but continue to reflect data- driven insights throughout development.
Statement C is not the best answer because "increase the product feature" is unclear and grammatically incomplete. AI may help improve features or identify new feature opportunities, but the statement is not as accurate as A and B. Therefore, the best answer is D. a and b only .
NEW QUESTION # 57
XAI stands for ______.
Answer: C
Explanation:
XAI stands for Explainable Artificial Intelligence. It refers to AI systems, models, and methods that help humans understand how an AI model reaches a decision, prediction, or recommendation. In business and responsible AI contexts, explainability is important because leaders, users, regulators, and stakeholders need to know why an AI system produced a specific result, especially in high-impact areas such as finance, healthcare, hiring, insurance, and public services.
Explainable AI supports transparency, accountability, trust, auditability, and risk management. It helps identify whether a model is relying on appropriate factors or producing biased, unfair, or unreliable outcomes.
"Extensible Artificial Intelligence" and "Exceptional Artificial Intelligence" are not standard meanings of XAI in artificial intelligence documentation. Since the accepted and correct expansion of XAI is Explainable Artificial Intelligence, the correct answer is B .
NEW QUESTION # 58
Which of the following models is called a black box as the outcomes cannot be directly linked to the model architecture and explained?
Answer: A
Explanation:
The correct answer is A. Neural network . Neural networks, especially deep neural networks, are often described as black box models because their internal decision-making process can be difficult to interpret directly. These models learn through many interconnected layers, weights, activation functions, and hidden representations. Although they may produce highly accurate predictions, it is often hard to clearly explain how a specific input led to a specific output in simple human-understandable terms.
Computer vision is not the best answer because it is an AI application area, not a specific model type. Support vector machines can also be complex in some cases, but neural networks are the most commonly associated with black box behavior in AI explainability discussions. Unsupervised learning is a learning approach, not a specific black box model. "Semi unsupervised learning" is not a standard primary machine learning category.
Because neural networks are widely known for limited transparency and difficult interpretability, the correct answer is A .
NEW QUESTION # 59
Which of the following is not a CORRECT common unsupervised learning model/algorithm?
Answer: E
Explanation:
The correct answer is C. K-nearest neighbors KNNs because KNN is commonly used as a supervised learning algorithm, not an unsupervised learning algorithm. In supervised learning, the model uses labeled data to classify or predict outcomes for new data points. KNN works by comparing a new data point with nearby labeled examples and assigning a class or value based on those neighbors.
K-means clustering is a common unsupervised learning algorithm because it groups unlabeled data into clusters based on similarity. Principal Component Analysis PCA is also commonly associated with unsupervised learning because it reduces data dimensions by finding important patterns or directions of variance without requiring labeled outputs.
Since options A and B are valid unsupervised learning techniques, they are not the answer. The option that is not a correct common unsupervised learning model or algorithm is C. K-nearest neighbors KNNs .
NEW QUESTION # 60
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