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| Section | Objectives |
|---|---|
| Deployment and Maintenance | - Development, deployment, testing, and maintenance of AI applications |
| AI Solutions and Workflows | - Fraud prevention with Cloud AI solutions - AI workflows - Sentiment analysis - Advanced robotics |
| Neural Networks and Technical Applications | - Designing, developing, and troubleshooting technical applications on neural networks - Enhancing approaches to problems such as translation, speech recognition, and image classification |
| AI Fundamentals and Data Systems | - Developing and deploying data systems related to AI - Fundamentals of Artificial Intelligence and Machine Learning |
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NEW QUESTION # 54
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: D
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 # 55
Choose the CORRECT example of Reinforcement Learning.
Answer: E
Explanation:
The correct answer is D. All of the above because robotics, game playing, and navigation are all common examples of reinforcement learning. Reinforcement learning is a machine learning approach in which an agent learns by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a policy that helps it maximize long-term reward.
Robotics is a strong example because robots can learn movement, object handling, path planning, and control actions through trial and feedback. Game playing is another classic reinforcement learning example because an AI agent can learn winning strategies by trying actions, observing outcomes, and improving decisions over repeated episodes. Navigation is also a valid example because an agent can learn the best route or movement strategy by receiving feedback about distance, obstacles, time, or success in reaching a goal.
Since all three listed options are valid applications of reinforcement learning, the correct answer is D. All of the above .
NEW QUESTION # 56
Which type of AI mimics the human brain by building networks of artificial neurons?
Answer: C
Explanation:
The correct answer is C. Neural Networks . Neural networks are AI and machine learning models inspired by the structure and function of the human brain. They are built using interconnected artificial neurons, also called nodes, which process input data, apply weights and activation functions, and pass information through layers to produc e an output. This structure allows neural networks to learn complex patterns from data.
Fuzzy logic is incorrect because it deals with reasoning under uncertainty using degrees of truth rather than artificial neurons. Genetic algorithms are optimization methods inspired by natural selection and evolution, not the brain's neuron-based structure. Expert systems use rules and knowledge bases to make decisions, but they do not mimic the brain through networks of artificial neurons. Since the question specifically asks about AI that mimics the human brain by building artificial neuron networks, the correct answer is C. Neural Networks .
NEW QUESTION # 57
What is the main advantage of using deep learning over traditional machine learning?
Answer: C
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 # 58
What type of learning is used when a model is trained with labeled data?
Answer: C
Explanation:
The correct answer is B. Supervised Learning . Supervised learning is the machine learning approach used when a model is trained with labeled data. Labeled data means each training example includes both the input and the correct output or target label. The model studies these examples and learns the relationship between the input features and the expected result. After training, it can make predictions or classifications on new data.
Unsupervised learning is incorrect because it uses unlabeled data and focuses on finding hidden patterns, clusters, or structures without predefined answers. Reinforcement learning is incorrect because it involves an agent learning through actions, rewards, and penalties in an environment. Semi-supervised learning is also not the best answer because it uses a mix of labeled and unlabeled data. Support Vector refers to part of the Support Vector Machine method, not a learning type by itself. Therefore, the correct learning type for labeled data is B. Supervised Learning .
NEW QUESTION # 59
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