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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning & Algorithms | 20% | - Types of Machine Learning
|
| Data Preparation & Engineering | 15% | - Data Cleaning, Preprocessing & Transformation - Feature Engineering & Selection - Data Collection & Sources - Data Quality & Governance |
| 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% | - Cloud AI Platforms (AWS, Azure, Google Cloud) - Automation & RPA Integration - AI Applications: NLP, Computer Vision, Predictive Analytics - Frameworks: TensorFlow, PyTorch, Scikit-learn |
| AI Fundamentals & Concepts | 15% | - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| AI in Business & Consulting | 25% | - Cost-Benefit Analysis & ROI Calculation - Identifying AI Use Cases & Opportunities - AI Strategy & Roadmap Development - AI Project Management & Governance - Implementation Planning & Change Management |
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NEW QUESTION # 66
Select the most INCORRECT risk-scoring methodology function statement for retrospective/concurrent.
Answer: E
Explanation:
The correct answer is D. a and b only because statements A and B are the most incorrect for retrospective
/concurrent risk-scoring methodology. Retrospective/concurrent risk assessment is mainly used to evaluate model risk based on past or present evidence, current model behavior, observed incidents, model performance changes, risk indicators, and investigation findings. It is not primarily a future-prediction method.
Statement A is incorrect because it says retrospective/concurrent methods "predict" model risk after analyzing historical model performance. Historical performance may be reviewed, but retrospective/concurrent risk scoring is more about assessing or investigating past and current risk conditions, not predicting future risk.
Statement B is also incorrect because using current model risk to predict overall model risk for future cycles describes prospective risk, not retrospective/concurrent risk. Statement C is correct because retrospective
/concurrent review is suitable when there are changes in model behavior, risk indicators, attacks, data loss, or investigation needs. Therefore, the most incorrect statements are A and B only .
NEW QUESTION # 67
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 # 68
Choose the CORRECT example of Reinforcement Learning.
Answer: C
Explanation:
The correct answer is D. All of the above because robotics, game playing, and navigation are all common examples of reinforcement learning. Reinforcement learning is a machine learning approach in which an agent learns by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a policy that helps it maximize long-term reward.
Robotics is a strong example because robots can learn movement, object handling, path planning, and control actions through trial and feedback. Game playing is another classic reinforcement learning example because an AI agent can learn winning strategies by trying actions, observing outcomes, and improving decisions over repeated episodes. Navigation is also a valid example because an agent can learn the best route or movement strategy by receiving feedback about distance, obstacles, time, or success in reaching a goal.
Since all three listed options are valid applications of reinforcement learning, the correct answer is D. All of the above .
NEW QUESTION # 69
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 # 70
What is the main advantage of using deep learning over traditional machine learning?
Answer: B
Explanation:
The correct answer is B. Better performance with large datasets . Deep learning is especially effective when large volumes of data are available because deep neural networks can automatically learn complex patterns, representations, and relationships from data. Unlike many traditional machine learning methods that often depend heavily on manual feature engineering, deep learning models can learn hierarchical features directly from raw or semi-processed data.
Option A is incorrect because deep learning usually requires more data, not less, to perform well. Option C is also incorrect because deep learning typically requires greater computational power, especially for training large models with many layers and parameters. Option D is incorrect because deep learning is not limited to structured data. It is widely used with unstructured data such as images, audio, video, and natural language.
Therefore, the main advantage of deep learning over traditional machine learning is B. Better performance with large datasets .
NEW QUESTION # 71
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