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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI in Business & Consulting | 25% | - Implementation Planning & Change Management - Identifying AI Use Cases & Opportunities - AI Strategy & Roadmap Development - AI Project Management & Governance - Cost-Benefit Analysis & ROI Calculation |
| Topic 2: AI Fundamentals & Concepts | 15% | - Introduction to Artificial Intelligence
- AI Lifecycle and Workflow |
| Topic 3: Data Preparation & Engineering | 15% | - Data Quality & Governance - Data Collection & Sources - Feature Engineering & Selection - Data Cleaning, Preprocessing & Transformation |
| Topic 4: AI Technologies & Tools | 15% | - Automation & RPA Integration - AI Applications: NLP, Computer Vision, Predictive Analytics - Cloud AI Platforms (AWS, Azure, Google Cloud) - Frameworks: TensorFlow, PyTorch, Scikit-learn |
| Topic 5: Ethics, Governance & Future Trends | 10% | - Explainable AI & Transparency - Emerging Trends & Future of AI - AI Ethics, Bias & Fairness - Data Privacy, Security & Compliance |
| Topic 6: Machine Learning & Algorithms | 20% | - Types of Machine Learning
- Popular Algorithms
|
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NEW QUESTION # 62
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: B
Explanation:
The correct answer is D. a and b only because AI provides strong benefits across the product development life cycle, especially by improving speed, decision quality, and data-driven design. Statement A is correct because AI can shorten the product development life cycle by automating research, analyzing customer feedback, generating product ideas, supporting rapid prototyping, improving testing, and helping teams identify risks or opportunities earlier.
Statement B is also correct because applying AI throughout the PDLC helps organizations use data consistently at every stage, from ideation and market research to design, testing, launch, and post-launch improvement. This means products are not only based on data at the beginning but continue to reflect data- driven insights throughout development.
Statement C is not the best answer because "increase the product feature" is unclear and grammatically incomplete. AI may help improve features or identify new feature opportunities, but the statement is not as accurate as A and B. Therefore, the best answer is D. a and b only .
NEW QUESTION # 63
Which of the CORRECT cognitive modeling is used in AI applications?
Answer: C
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 # 64
Select the most CORRECT statement.
Answer: D
Explanation:
The correct answer is D. a and c only because dimensionality reduction is the process of reducing the number of variables or features considered in a dataset while trying to preserve the most important information. This is especially useful when working with large datasets that contain many columns, attributes, or variables.
Reducing dimensionality can improve model performance, reduce computational cost, remove noise, and make data easier to visualize and analyze.
Statement A is correct because dimensionality reduction reduces the number of variables considered in the analysis. Statement C is also correct because Principal Component Analysis, or PCA, is one of the most common techniques used to reduce the dimensionality of large datasets. PCA transforms the original variables into a smaller set of principal components that capture most of the important variance in the data.
Statement B is not correct because dimensionality reduction is not about reducing "targetted variables." It focuses mainly on reducing input features or random variables. Therefore, the best answer is D. a and c only .
NEW QUESTION # 65
Select the INCORRECT statement for DevOps architect.
Answer: B
Explanation:
The incorrect statement is B because business development and planning are not core technical components of a robust DevOps architecture. DevOps architecture mainly focuses on automation, CI/CD pipelines, infrastructure management, deployment strategy, monitoring, alerting, scalability, reliability, security, and disaster recovery. While business planning may influence technology priorities, it is not normally listed as an essential DevOps architecture component.
Monitoring and alerting are essential because they help teams detect failures, performance degradation, service outages, and abnormal system behavior. Disaster recovery is also a critical responsibility because DevOps architects must design systems that can recover from failures with minimal downtime and limited data loss. CI/CD pipeline creation and optimization are central DevOps responsibilities because they enable faster, repeatable, and reliable software delivery. Therefore, options A, C, D, and E are valid DevOps architecture statements, while B is the incorrect one.
NEW QUESTION # 66
Which of the following is NOT a type of machine learning?
Answer: E
Explanation:
The correct answer is D. Restricted Learning because it is not commonly recognized as a standard type of machine learning. The main learning approaches include supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, and transfer learning. Supervised learning uses labeled datasets to train models for prediction or classification. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data. Transfer learning reuses knowledge from a pre-trained model and adapts it to a new related task.
"Restricted Learning" is not a standard machine learning category in this context. Although some specific technical terms may include the word "restricted," such as restricted Boltzmann machines, that does not make
"restricted learning" a recognized general type of machine learning. Therefore, the option that is NOT a type of machine learning is D. Restricted Learning .
NEW QUESTION # 67
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