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| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation & Metrics | - Performance metrics (precision, recall, F1, AUC-ROC) - Model validation and selection |
| Topic 2: Deployment & AI Operations | - Model deployment pipelines - Model monitoring and drift management |
| Topic 3: AI Fundamentals & Business Applications | - AI applications in business operations - AI fundamentals and architectures |
| Topic 4: Data Preparation & Feature Engineering | - Feature engineering techniques - Data cleaning and preprocessing |
| Topic 5: AI Strategy & Business Transformation | - ROI and value creation with AI - AI-driven business use cases |
| Topic 6: Responsible AI & Ethics | - Bias detection and mitigation - AI governance and compliance (e.g., GDPR) |
| Topic 7: Model Development & Machine Learning | - Supervised and unsupervised learning - Model training and optimization |
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NEW QUESTION # 18
Select the BEST choice for ML solutions architecture coverage.
Answer: A
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 # 19
Which of the following is NOT a pillar of the GenAI Well-Architected Framework?
Answer: D
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 # 20
Which of the following statement is CORRECT for RNN?
Answer: A
Explanation:
The correct answer is E. a, b and c only because all three statements correctly describe Recurrent Neural Networks and their limitation. RNNs are neural network models designed for sequential data such as text, speech, time-series data, and ordered events. They process information step by step and use previous hidden states to influence later outputs.
Statement A is correct because a major drawback of traditional RNNs is their difficulty in remembering information over many time steps. This happens mainly because of vanishing gradient problems during training. Statement B is also correct because standard RNNs generally struggle with long-term dependencies, meaning they may fail to retain important information from earlier parts of a sequence. Statement C is correct because Long Short-Term Memory networks are a specialized extension of RNNs designed to handle long- term memory more effectively using gates that control what information is stored, forgotten, and passed forward.
Therefore, the best answer is E. a, b and c only .
NEW QUESTION # 21
Which of the CORRECT cognitive modeling is used in AI applications?
Answer: A
Explanation:
The correct answer is E. All of the above because deep learning, expert systems, natural language processing, and robotics are all connected with AI applications that support or model intelligent behavior. Cognitive modeling in AI is concerned with building systems that can represent, simulate, or support human-like capabilities such as learning, reasoning, decision-making, perception, language understanding, and action.
Deep learning is used to recognize patterns from large amounts of data and is common in speech recognition, image analysis, recommendation systems, and generative AI. Expert systems use knowledge bases and rules to support decision-making in specialized domains. Natural language processing helps AI systems understand, interpret, generate, and respond to human language. Robotics applies AI to physical systems so machines can sense, plan, move, and perform tasks in real-world environments.
Since all the listed options are valid AI application areas related to intelligent and cognitive capabilities, the correct answer is E. All of the above .
NEW QUESTION # 22
Choose the CORRECT statement for ChatGPT.
Answer: E
Explanation:
The correct answer is B because ChatGPT's ability to maintain and use previous conversational context depends mainly on its model architecture, algorithmic design, token context window, and how the conversation history is processed. ChatGPT is based on large language model technology that uses patterns in prior text to generate relevant responses. It does not "remember" in the same way a human does; rather, it uses the available previous context within the conversation to predict and generate the next response.
Option A is partially true but incomplete because it says ChatGPT can maintain previous context without explaining the dependency on the model's design and context-handling mechanism. Option C is incorrect because TPU hardware may support model training or inference performance, but it does not determine conversational memory by itself. Since option C is wrong, "All of the above" cannot be correct. "None of the above" is also incorrect because option B correctly describes the concept. Therefore, the best answer is B .
NEW QUESTION # 23
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