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| Section | Weight | Objectives |
|---|---|---|
| AI in Business & Consulting | 25% | - Implementation Planning & Change Management - AI Project Management & Governance - Identifying AI Use Cases & Opportunities - Cost-Benefit Analysis & ROI Calculation - AI Strategy & Roadmap Development |
| AI Fundamentals & Concepts | 15% | - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| Ethics, Governance & Future Trends | 10% | - Emerging Trends & Future of AI - Data Privacy, Security & Compliance - Explainable AI & Transparency - AI Ethics, Bias & Fairness |
| AI Technologies & Tools | 15% | - Cloud AI Platforms (AWS, Azure, Google Cloud) - AI Applications: NLP, Computer Vision, Predictive Analytics - Frameworks: TensorFlow, PyTorch, Scikit-learn - Automation & RPA Integration |
| Machine Learning & Algorithms | 20% | - Popular Algorithms
|
| Data Preparation & Engineering | 15% | - Data Quality & Governance - Data Collection & Sources - Feature Engineering & Selection - Data Cleaning, Preprocessing & Transformation |
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NEW QUESTION # 15
If humans are unlabeling the data and the machine is correctly labeling current or future data points, it's
______.
Answer: B
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 # 16
Unsupervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: B
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 # 17
Which of the following is MLOps?
Answer: C
Explanation:
The correct answer is E. a, b and c only because MLOps includes workflow automation, continuous integration, and continuous deployment as important practices for managing the machine learning lifecycle.
MLOps, or Machine Learning Operations, applies DevOps-style principles to machine learning systems so models can be developed, tested, deployed, monitored, and maintained in a reliable and repeatable way.
Workflow automation is part of MLOps because machine learning pipelines often include data ingestion, data validation, feature engineering, model training, model evaluation, deployment, and monitoring. Continuous integration is also included because ML code, data pipelines, configuration files, and model components need regular testing and validation when changes are made. Continuous deployment is another key part because approved models should be deployed efficiently into production environments with version control, rollback options, and monitoring.
Since all three options describe important MLOps capabilities, the best answer is E. a, b and c only .
NEW QUESTION # 18
Choose the CORRECT statement for GenAI.
Answer: B
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 # 19
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: C
NEW QUESTION # 20
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