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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning & Algorithms | 20% | - Popular Algorithms
|
| AI Fundamentals & Concepts | 15% | - AI Lifecycle and Workflow - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| Data Preparation & Engineering | 15% | - Feature Engineering & Selection - Data Quality & Governance - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation |
| Ethics, Governance & Future Trends | 10% | - Explainable AI & Transparency - AI Ethics, Bias & Fairness - Data Privacy, Security & Compliance - Emerging Trends & Future of AI |
| AI Technologies & Tools | 15% | - Automation & RPA Integration - Frameworks: TensorFlow, PyTorch, Scikit-learn - Cloud AI Platforms (AWS, Azure, Google Cloud) - AI Applications: NLP, Computer Vision, Predictive Analytics |
| AI in Business & Consulting | 25% | - AI Project Management & Governance - Identifying AI Use Cases & Opportunities - Implementation Planning & Change Management - AI Strategy & Roadmap Development - Cost-Benefit Analysis & ROI Calculation |
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NEW QUESTION # 55
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: B
NEW QUESTION # 56
Which of the following is NOT a pillar of the GenAI Well-Architected Framework?
Answer: A
Explanation:
The correct answer is D. System Architecture Excellence because it is not normally identified as a standard pillar of a GenAI Well-Architected Framework. Well-architected AI and GenAI frameworks commonly focus on structured pillars such as operational excellence, security and privacy, reliability, performance, cost optimization, responsible AI, and governance-related practices. These pillars help organizations design GenAI solutions that are secure, scalable, reliable, maintainable, and aligned with business and ethical expectations.
Operational excellence is a valid pillar because GenAI systems require proper deployment processes, observability, automation, monitoring, incident response, and lifecycle management. Security and privacy are also essential because GenAI applications often process sensitive data, prompts, outputs, embeddings, and model interactions. Reliability is another valid pillar because GenAI solutions must handle failures, latency, model availability, fallback mechanisms, and consistent service delivery.
"System Architecture Excellence" sounds related to solution design, but it is not a recognized pillar name in the listed framework. Therefore, the option that is NOT a pillar is D .
NEW QUESTION # 57
Which is the first useful computer program that came into existence in the AI world?
Answer: E
Explanation:
The correct answer is B. GPS . In artificial intelligence history, GPS stands for General Problem Solver . It was an early AI program developed to simulate human problem-solving behavior. GPS was designed to solve problems by breaking them down into goals, subgoals, operators, and differences between the current state and the desired state. This approach became important because it introduced structured reasoning and symbolic problem solving, which were central ideas in early AI research.
LPS, MLS, AIS, and EPS are not the standard answer for the first useful computer program in the AI world in this context. GPS is widely recognized as one of the earliest useful AI programs because it attempted to model general reasoning rather than solving only one narrow calculation task. It showed how computers could be programmed to search through possible actions and work toward a goal. Therefore, the correct answer is B.
GPS .
NEW QUESTION # 58
Which of the following is CORRECT for Support Vector Machine SVM?
Answer: B
Explanation:
The correct answer is D. a and b only . Support Vector Machine, or SVM, is a supervised machine learning algorithm widely used for classification problems. It works by finding the best separating boundary, called a hyperplane, between different classes in the dataset. The goal is to maximize the margin between the closest data points of each class, known as support vectors, so the model can classify new data more effectively.
Statement B is also correct because SVM can use kernel methods to transform data into higher-dimensional spaces. This helps make complex or non-linearly separable data easier to separate. For example, when data cannot be clearly grouped in a two-dimensional view, a kernel function can map it into a higher-dimensional feature space where a better separating hyperplane may be found.
Statement C is incorrect because SVM does allow dimensional transformation through kernel techniques.
Therefore, the correct choice is D. a and b only .
NEW QUESTION # 59
Which of the following is a CORRECT statement for Fine-tuning?
Answer: E
Explanation:
The correct answer is E. a, b and c only because all three statements accurately describe fine-tuning. Fine- tuning is a machine learning and AI technique where a model that has already been trained on a large dataset is further trained or adapted for a more specific task, domain, or use case. This is common in natural language processing, generative AI, computer vision, and business AI applications.
Statement A is correct because fine-tuning adapts a pre-trained model to a new task. Statement B is also correct because during fine-tuning, some or all model parameters may be updated based on task-specific data.
Statement C is correct because the main advantage of fine-tuning is that it uses the general knowledge already learned by the pre-trained model instead of building a new model from the beginning. This saves time, data, compute resources, and often improves performance on specialized tasks. Therefore, the best answer is E .
NEW QUESTION # 60
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