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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning & Algorithms | 20% | - Popular Algorithms
|
| AI Technologies & Tools | 15% | - AI Applications: NLP, Computer Vision, Predictive Analytics - Frameworks: TensorFlow, PyTorch, Scikit-learn - Cloud AI Platforms (AWS, Azure, Google Cloud) - Automation & RPA Integration |
| AI Fundamentals & Concepts | 15% | - AI Lifecycle and Workflow - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| Data Preparation & Engineering | 15% | - Feature Engineering & Selection - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation - Data Quality & Governance |
| Ethics, Governance & Future Trends | 10% | - AI Ethics, Bias & Fairness - Explainable AI & Transparency - Data Privacy, Security & Compliance - Emerging Trends & Future of AI |
| AI in Business & Consulting | 25% | - AI Strategy & Roadmap Development - AI Project Management & Governance - Cost-Benefit Analysis & ROI Calculation - Implementation Planning & Change Management - Identifying AI Use Cases & Opportunities |
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NEW QUESTION # 27
Which of the following is NOT a type of machine learning?
Answer: A
Explanation:
The correct answer is D. Restricted Learning because it is not commonly recognized as a standard type of machine learning. The main learning approaches include supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, and transfer learning. Supervised learning uses labeled datasets to train models for prediction or classification. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data. Transfer learning reuses knowledge from a pre-trained model and adapts it to a new related task.
"Restricted Learning" is not a standard machine learning category in this context. Although some specific technical terms may include the word "restricted," such as restricted Boltzmann machines, that does not make
"restricted learning" a recognized general type of machine learning. Therefore, the option that is NOT a type of machine learning is D. Restricted Learning .
NEW QUESTION # 28
A retail company has a large dataset of customer purchases but no predefined labels. The AI system groups customers into segments based on similar buying behavior. This is an example of ______.
Answer: A
Explanation:
Unsupervised learning is the correct answer because the dataset does not contain predefined labels or known target outcomes. The AI system is identifying natural patterns in the data and grouping customers with similar purchasing behavior. This type of task is commonly called clustering, which is one of the most common applications of unsupervised learning. Supervised learning is incorrect because there are no labeled examples telling the model which customer belongs to which segment. Reinforcement learning is incorrect because the system is not learning through rewards or penalties. Transfer learning involves reusing knowledge from one trained model for another related task, which is not described here. Semi-supervised learning would involve both labeled and unlabeled data, but this scenario only mentions unlabeled data. Therefore, the correct answer is B. unsupervised learning .
NEW QUESTION # 29
Which of the following is the CORRECT basic brain components?
Answer: C
Explanation:
The correct answer is E. All of the above because the listed terms are associated with major functional or structural areas of the human brain often discussed when explaining cognition, intelligence, decision-making, emotions, perception, and behavior. The neocortex is linked with higher-order thinking, reasoning, language, planning, perception, and conscious decision-making. The limbic system is strongly associated with emotion, memory, motivation, and behavioral responses. The midbrain is involved in sensory processing, movement control, visual and auditory responses, and alertness. The brainstem supports essential automatic functions such as breathing, heart rate, sleep-wake regulation, and basic survival processes.
In artificial intelligence learning, these brain components are often introduced to help explain how human intelligence works and how AI attempts to model certain cognitive functions. Since neocortex, limbic, midbrain, and brainstem are all valid basic brain-related components, the best answer is E. All of the above .
NEW QUESTION # 30
Which of the following is the CORRECT first step in the Machine Learning lifecycle?
Answer: D
Explanation:
The correct answer is B. Business understanding . The first step in the machine learning lifecycle is to understand the business problem, objective, expected outcome, and success criteria. Before collecting data, selecting algorithms, or preparing models, the organization must clearly define what problem the ML solution is intended to solve and how success will be measured. This may include identifying business goals such as cost reduction, revenue improvement, risk mitigation, customer experience improvement, operational efficiency, or decision automation.
Data understanding comes after business understanding because data exploration should be guided by the business objective. Algorithm use understanding is also not the first step because choosing or evaluating algorithms should happen only after the problem, data, and intended outcome are clear. Options D and E are incorrect because the question asks for the single first step. Therefore, the correct first step in the machine learning lifecycle is B. Business understanding .
NEW QUESTION # 31
Choose the INCORRECT statement for Industry Architect.
Answer: D
Explanation:
The incorrect statement is B because it describes DevOps, not an Industry Architect. A collaborative approach that bridges development and operations teams is the core idea of DevOps, where software development, IT operations, automation, continuous integration, continuous deployment, monitoring, and delivery practices are aligned to improve speed and reliability.
An Industry Architect, on the other hand, focuses on designing technology and business solutions for a specific industry or vertical, such as healthcare, finance, retail, manufacturing, or telecommunications. This role requires strong domain knowledge, awareness of industry regulations, understanding of business processes, and the ability to translate industry-specific requirements into practical technical solutions. Industry Architects work with executives, subject matter experts, business teams, and technology teams to ensure that solutions meet business goals and industry expectations. Therefore, options A, C, D, and E correctly describe the Industry Architect role, while B is the incorrect statement.
NEW QUESTION # 32
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