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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation & Engineering | 15% | - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation - Data Quality & Governance - Feature Engineering & Selection |
| Topic 2: Ethics, Governance & Future Trends | 10% | - Explainable AI & Transparency - AI Ethics, Bias & Fairness - Data Privacy, Security & Compliance - Emerging Trends & Future of AI |
| Topic 3: AI in Business & Consulting | 25% | - Cost-Benefit Analysis & ROI Calculation - Implementation Planning & Change Management - AI Project Management & Governance - Identifying AI Use Cases & Opportunities - AI Strategy & Roadmap Development |
| Topic 4: Machine Learning & Algorithms | 20% | - Popular Algorithms
- Types of Machine Learning
|
| Topic 5: AI Technologies & Tools | 15% | - Frameworks: TensorFlow, PyTorch, Scikit-learn - AI Applications: NLP, Computer Vision, Predictive Analytics - Cloud AI Platforms (AWS, Azure, Google Cloud) - Automation & RPA Integration |
| Topic 6: AI Fundamentals & Concepts | 15% | - AI vs Traditional Programming - AI Lifecycle and Workflow - Introduction to Artificial Intelligence
|
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NEW QUESTION # 25
Choose the CORRECT benefit of solution architecture.
Answer: B
Explanation:
Solution architecture provides the structured blueprint needed to move from a business or technical concept to a working implementation. It defines how different systems, applications, data flows, technologies, security requirements, and business needs will fit together. Therefore, it gives teams a solid foundation for developing enterprise software solutions.
A well-defined solution architecture is also valuable when projects become large, complex, or distributed across multiple teams and locations. It creates a common understanding of design decisions, integration points, responsibilities, and technical standards, which supports collaboration and long-term sustainability. In addition, solution architecture helps ensure that the final solution meets business expectations, technical requirements, quality standards, scalability needs, security controls, and operational goals.
Since options A, B, and C all describe valid benefits of solution architecture, the most complete and correct answer is E. All of the above .
NEW QUESTION # 26
Which one of the following should NOT be used while designing the prompt?
Answer: E
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 # 27
What is the main advantage of using deep learning over traditional machine learning?
Answer: A
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 # 28
Which of the following is NOT CORRECT for the Elbow method?
Answer: E
Explanation:
The correct answer is E. None of the above because all three statements about the Elbow method are correct.
The Elbow method is commonly used in unsupervised learning, especially with K-means clustering, to help estimate an appropriate number of clusters. It works by running clustering with different values of K and measuring the within-cluster variation or distortion. As K increases, the error usually decreases, but after a certain point the improvement becomes much smaller. That point is visually interpreted as the "elbow." Statement A is correct because the Elbow method helps determine how many clusters should be formed.
Statement B is also correct because it is widely used with K-means clustering to select a suitable value of K.
Statement C is correct because the method is a heuristic, meaning it is a practical estimation technique rather than an exact mathematical guarantee. Since A, B, and C are all correct, none of them is NOT correct.
Therefore, the correct answer is E. None of the above .
NEW QUESTION # 29
Which of the following is a CORRECT statement for Few-shot learning?
Answer: A
Explanation:
The correct answer is D. a and b only because few-shot learning is a machine learning technique that allows a model to learn or adapt to a new task using only a small number of labeled examples. It is especially useful when collecting large labeled datasets is expensive, slow, or difficult. Instead of requiring thousands or millions of labeled records, few-shot learning depends on prior knowledge learned by the model and applies that knowledge to new examples with limited supervision.
Statement A is correct because few-shot learning is recognized as a machine learning approach. Statement B is also correct because the core idea of few-shot learning is learning from very limited labeled data. Statement C is not correct because learning from unlabeled data is more closely associated with unsupervised learning or semi-supervised learning, not the standard definition of few-shot learning. Therefore, the correct answer is D.
a and b only .
NEW QUESTION # 30
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