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| Section | Objectives |
|---|---|
| Responsible AI & Ethics | - AI governance and compliance (e.g., GDPR) - Bias detection and mitigation |
| Deployment & AI Operations | - Model monitoring and drift management - Model deployment pipelines |
| Model Development & Machine Learning | - Supervised and unsupervised learning - Model training and optimization |
| Data Preparation & Feature Engineering | - Data cleaning and preprocessing - Feature engineering techniques |
| Model Evaluation & Metrics | - Performance metrics (precision, recall, F1, AUC-ROC) - Model validation and selection |
| AI Fundamentals & Business Applications | - AI applications in business operations - AI fundamentals and architectures |
| AI Strategy & Business Transformation | - AI-driven business use cases - ROI and value creation with AI |
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NEW QUESTION # 42
Choose the CORRECT statement for GenAI.
Answer: D
Explanation:
The correct answer is E. All of the above because all three statements describe the broader idea of general intelligence in AI systems. GenAI in this question is presented as intelligence that goes beyond narrow, task- specific AI and aims to support broader reasoning, learning, adaptation, and performance across multiple domains.
Statement A is correct because general AI focuses on systems that can demonstrate human-like cognitive abilities and perform different kinds of tasks rather than being limited to one predefined function. Statement B is also correct because architects working on such advanced AI systems must design solutions that go beyond specific use cases and support more general intelligence capabilities. Statement C is correct because general AI systems are expected to learn from limited data, transfer knowledge across domains, adapt to changing environments, and perform reliably in uncertain situations.
Since A, B, and C are all correct, the best answer is E. All of the above .
NEW QUESTION # 43
Which of the following is a step for the Value Engineering Framework?
Answer: E
Explanation:
The correct answer is E. All of the above because the Value Engineering Framework focuses on identifying, delivering, and expanding measurable business value from data and AI initiatives. "Define value creation" is a key step because organizations must first clarify the business problem, expected outcomes, success metrics, stakeholders, and value drivers before investing in an AI solution.
"Realize value creation" is also correct because value must be converted from a planned objective into actual operational or financial impact. This may involve deploying the solution, measuring results, improving processes, reducing cost, increasing revenue, improving risk management, or enhancing customer outcomes.
"Scale value creation" is correct because successful AI initiatives should not remain limited to isolated pilots.
Organizations need to scale proven use cases across teams, business units, workflows, and enterprise platforms to maximize return on investment and long-term impact. Since all three options represent steps in value engineering, the best answer is E. All of the above .
NEW QUESTION # 44
A model is trained using historical customer records where each record already contains the correct outcome, such as "churn" or "not churn." The model then predicts whether future customers are likely to churn. This is an example of ______.
Answer: B
Explanation:
Supervised learning is used when a machine learning model is trained on labeled data. In this case, the historical customer records already include the correct outcome labels, such as "churn" or "not churn." The model learns the relationship between customer attributes and the known outcome, then applies that learned relationship to predict outcomes for new customers. This is a classic classification problem. Unsupervised learning is incorrect because it works with unlabeled data and is commonly used for clustering or discovering hidden patterns. Reinforcement learning is incorrect because there is no reward-based decision-making environment described. Generative learning is not the best answer because the task is prediction, not creating new content. Therefore, the correct answer is A. supervised learning .
NEW QUESTION # 45
Which of the following is NOT a learning category for the ML model?
Answer: E
Explanation:
The correct answer is D. Semi Reinforcement learning because it is not commonly recognized as a standard learning category for machine learning models. The major machine learning categories include supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Supervised learning uses labeled datasets where the model learns from known input-output examples. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Reinforcement learning trains an agent through interaction with an environment using rewards and penalties. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve learning when fully labeled datasets are limited.
"Semi Reinforcement learning" is not normally listed as a core ML learning category in standard AI and machine learning learning paths. Therefore, among the given options, the one that is NOT a learning category for the ML model is D. Semi Reinforcement learning .
NEW QUESTION # 46
Which of the following is not a CORRECT common unsupervised learning model/algorithm?
Answer: C
Explanation:
The correct answer is C. K-nearest neighbors KNNs because KNN is commonly used as a supervised learning algorithm, not an unsupervised learning algorithm. In supervised learning, the model uses labeled data to classify or predict outcomes for new data points. KNN works by comparing a new data point with nearby labeled examples and assigning a class or value based on those neighbors.
K-means clustering is a common unsupervised learning algorithm because it groups unlabeled data into clusters based on similarity. Principal Component Analysis PCA is also commonly associated with unsupervised learning because it reduces data dimensions by finding important patterns or directions of variance without requiring labeled outputs.
Since options A and B are valid unsupervised learning techniques, they are not the answer. The option that is not a correct common unsupervised learning model or algorithm is C. K-nearest neighbors KNNs .
NEW QUESTION # 47
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