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| Section | Objectives |
|---|---|
| Deployment & AI Operations | - Model monitoring and drift management - Model deployment pipelines |
| AI Strategy & Business Transformation | - AI-driven business use cases - ROI and value creation with AI |
| AI Fundamentals & Business Applications | - AI fundamentals and architectures - AI applications in business operations |
| Model Evaluation & Metrics | - Model validation and selection - Performance metrics (precision, recall, F1, AUC-ROC) |
| Model Development & Machine Learning | - Model training and optimization - Supervised and unsupervised learning |
| Responsible AI & Ethics | - AI governance and compliance (e.g., GDPR) - Bias detection and mitigation |
| Data Preparation & Feature Engineering | - Data cleaning and preprocessing - Feature engineering techniques |
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NEW QUESTION # 28
Which one of the following should NOT be used while designing the prompt?
Answer: B
Explanation:
The correct answer is E. All of the above because effective prompt design requires clarity, focus, structure, and useful constraints. Information overload should not be used because giving too much unnecessary detail can confuse the model, weaken the main instruction, and reduce the quality of the response. A prompt should include relevant context, but it should avoid excessive or unrelated information.
Open-ended questions should also be avoided when the goal is a specific, controlled, or business-ready answer. Broad prompts often produce vague, incomplete, or inconsistent outputs. Instead, prompts should clearly state the desired task, format, scope, and expected outcome. Lack of constraints is also a poor prompt design practice because constraints guide the model on length, tone, structure, audience, output type, and boundaries. Without constraints, the model may generate responses that are too broad, too long, or misaligned with the user's intent.
Since information overload, overly open-ended questions, and lack of constraints can all weaken prompt quality, the correct answer is E. All of the above .
NEW QUESTION # 29
If humans are unlabeling the data and the machine is correctly labeling current or future data points, it's
______.
Answer: C
Explanation:
Semi-supervised learning is the correct answer because it combines a small amount of labeled data with a larger amount of unlabeled data. In this scenario, humans are not fully labeling the data, but the machine is still able to correctly label current or future data points by learning patterns from the available data. That matches the concept of semi-supervised learning, where the model uses limited human-provided labels and extends learning to unlabeled examples.
Supervised learning is not the best answer because supervised learning depends on clearly labeled training data supplied by humans. Unsupervised learning is also incorrect because it identifies hidden patterns or clusters without using labels, rather than predicting correct labels for future data points. Reinforcement learning is based on rewards, penalties, actions, and an environment, which is not described here. "Semi- reinforcement learning" is not a standard main category in machine learning.
Therefore, the most accurate answer is **E. Semi-supervised learning**.
NEW QUESTION # 30
Which of the following is a step for the Value Engineering Framework?
Answer: C
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 # 31
Unsupervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: C
Explanation:
The correct answer is B. Unlabeled dataset . Unsupervised learning is a machine learning approach where the algorithm works with data that does not contain predefined labels, target outputs, or correct answers. Instead of being told what each data point represents, the model analyzes the structure of the data and identifies hidden patterns, groupings, similarities, or relationships on its own.
This type of learning is commonly used for clustering, association rule mining, anomaly detection, dimensionality reduction, and customer segmentation. For example, an unsupervised learning model may group customers based on buying behavior without being given category labels in advance.
A labeled dataset is used in supervised learning, where the model learns from input-output pairs. An
"explained dataset" is not a standard machine learning category. Since unsupervised learning specifically depends on unlabeled data, the correct answer is B. Unlabeled dataset .
NEW QUESTION # 32
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 # 33
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