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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ethics, Governance & Future Trends | 10% | - AI Ethics, Bias & Fairness - Data Privacy, Security & Compliance - Emerging Trends & Future of AI - Explainable AI & Transparency |
| Topic 2: AI Technologies & Tools | 15% | - Frameworks: TensorFlow, PyTorch, Scikit-learn - Automation & RPA Integration - Cloud AI Platforms (AWS, Azure, Google Cloud) - AI Applications: NLP, Computer Vision, Predictive Analytics |
| Topic 3: Machine Learning & Algorithms | 20% | - Popular Algorithms
- Types of Machine Learning
|
| Topic 4: AI Fundamentals & Concepts | 15% | - Introduction to Artificial Intelligence
- AI Lifecycle and Workflow |
| Topic 5: AI in Business & Consulting | 25% | - Implementation Planning & Change Management - Identifying AI Use Cases & Opportunities - Cost-Benefit Analysis & ROI Calculation - AI Strategy & Roadmap Development - AI Project Management & Governance |
| Topic 6: Data Preparation & Engineering | 15% | - Feature Engineering & Selection - Data Cleaning, Preprocessing & Transformation - Data Collection & Sources - Data Quality & Governance |
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NEW QUESTION # 58
What type of learning is used when a model is trained with labeled data?
Answer: A
Explanation:
The correct answer is B. Supervised Learning . Supervised learning is the machine learning approach used when a model is trained with labeled data. Labeled data means each training example includes both the input and the correct output or target label. The model studies these examples and learns the relationship between the input features and the expected result. After training, it can make predictions or classifications on new data.
Unsupervised learning is incorrect because it uses unlabeled data and focuses on finding hidden patterns, clusters, or structures without predefined answers. Reinforcement learning is incorrect because it involves an agent learning through actions, rewards, and penalties in an environment. Semi-supervised learning is also not the best answer because it uses a mix of labeled and unlabeled data. Support Vector refers to part of the Support Vector Machine method, not a learning type by itself. Therefore, the correct learning type for labeled data is B. Supervised Learning .
NEW QUESTION # 59
Why is prompt important?
Answer: D
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 # 60
Choose the BEST key components of workflow automation.
Answer: C
Explanation:
Workflow automation in an AI or machine learning environment involves designing, running, tracking, and maintaining automated processes across the model lifecycle. Pipeline design and management is a key component because AI workflows often require structured pipelines for data ingestion, preprocessing, model training, validation, deployment, and updates. Pipeline execution and monitoring is also essential because automated workflows must be executed reliably, and teams need visibility into job status, failures, performance issues, and operational bottlenecks.
Model monitoring configuration is also a necessary component in AI workflow automation because deployed models must be observed for performance degradation, data drift, prediction quality, and operational reliability. Without monitoring, an automated AI workflow may continue producing poor or outdated results without detection. Since all three options support the implementation, operation, and governance of automated AI pipelines, the best and most complete answer is E. a, b, and c only .
NEW QUESTION # 61
What is a Large Language Model?
Answer: C
Explanation:
The correct answer is E. a, b and c only because all three statements accurately describe a Large Language Model. An LLM is an AI or machine learning model designed to work with natural language. It can process human language, understand context, generate text, answer questions, summarize content, translate language, classify text, and support conversational applications.
Statement A is correct because LLMs operate within the context of natural languages. Statement B is also correct because LLMs can accept natural language prompts as input and produce natural language responses as output. This is why they are widely used in chatbots, virtual assistants, document analysis, business writing, and knowledge management systems. Statement C is correct because modern LLMs are built using neural network architectures and require significant computing resources, especially during training. They learn patterns from large text datasets and predict likely language outputs based on context.
Since A, B, and C are all correct descriptions of Large Language Models, the correct answer is E. a, b and c only .
NEW QUESTION # 62
Choose the CORRECT benefit of solution architecture.
Answer: C
Explanation:
Solution architecture provides the structured blueprint needed to move from a business or technical concept to a working implementation. It defines how different systems, applications, data flows, technologies, security requirements, and business needs will fit together. Therefore, it gives teams a solid foundation for developing enterprise software solutions.
A well-defined solution architecture is also valuable when projects become large, complex, or distributed across multiple teams and locations. It creates a common understanding of design decisions, integration points, responsibilities, and technical standards, which supports collaboration and long-term sustainability. In addition, solution architecture helps ensure that the final solution meets business expectations, technical requirements, quality standards, scalability needs, security controls, and operational goals.
Since options A, B, and C all describe valid benefits of solution architecture, the most complete and correct answer is E. All of the above .
NEW QUESTION # 63
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