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| Certification Vendor: | USAII |
|---|---|
| Exam Name: | Certified Artificial Intelligence Consultant |
| Exam Number: | CAIC |
| Certificate Validity Period: | Lifetime |
| Exam Duration: | 100 minutes |
| Passing Score: | 70% |
| Exam Price: | USD 894 |
| Real Exam Qty: | 35 |
| Available Languages: | English |
| Related Certifications: | CAIC™ |
| Exam Format: | Multiple Response, Multiple Choice |
| Sample Questions: | USAII CAIC Sample Questions |
| Exam Way: | Online |
| Pre Condition: | Programming skills are not mandatory to apply for CAIC™ certification. |
| Official Syllabus URL: | https://www.usaii.org/artificial-intelligence-certifications/caic |
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NEW QUESTION # 72
Supervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: D
Explanation:
The correct answer is A. labeled dataset . Supervised learning is a machine learning method in which an algorithm is trained using data that already contains the correct output labels or target values. Each training example includes input features and a known answer, allowing the model to learn the relationship between the inputs and the expected output. Once trained, the model can use that learned relationship to classify or predict outcomes for new data.
An unlabeled dataset is used in unsupervised learning, where the model identifies hidden patterns, clusters, or relationships without predefined labels. An "explained dataset" is not a standard machine learning category.
Option D is incorrect because supervised learning does not learn from both labeled and unlabeled datasets as its primary definition. Option E is also incorrect because "explained dataset" is not the correct term.
Therefore, supervised learning learns from a labeled dataset , making A the correct answer.
NEW QUESTION # 73
Which of the following is NOT a learning category for the ML model?
Answer: E
Explanation:
The correct answer is D. Semi Reinforcement learning because it is not commonly recognized as a standard learning category for machine learning models. The major machine learning categories include supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Supervised learning uses labeled datasets where the model learns from known input-output examples. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Reinforcement learning trains an agent through interaction with an environment using rewards and penalties. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve learning when fully labeled datasets are limited.
"Semi Reinforcement learning" is not normally listed as a core ML learning category in standard AI and machine learning learning paths. Therefore, among the given options, the one that is NOT a learning category for the ML model is D. Semi Reinforcement learning .
NEW QUESTION # 74
Which of the following is a CORRECT NLP task?
Answer: C
Explanation:
The correct answer is E. a, b and c only because tokenization, Part-of-Speech tagging, and Question Answering are all valid natural language processing tasks. NLP focuses on enabling machines to process, analyze, understand, and generate human language for business and technical applications.
Tokenization is a basic NLP task where text is divided into smaller units such as words, subwords, or tokens.
This step helps models process language in a structured way. Part-of-Speech tagging is also an NLP task because it identifies the grammatical role of words, such as nouns, verbs, adjectives, and adverbs. This helps systems understand sentence structure and meaning. Question Answering is another important NLP task where a system interprets a user's question and generates or retrieves the most relevant answer from data, documents, or knowledge sources.
Since all three options represent correct NLP tasks, the best answer is E. a, b and c only .
NEW QUESTION # 75
What is a Large Language Model?
Answer: D
Explanation:
The correct answer is E. a, b and c only because all three statements accurately describe a Large Language Model. An LLM is an AI or machine learning model designed to work with natural language. It can process human language, understand context, generate text, answer questions, summarize content, translate language, classify text, and support conversational applications.
Statement A is correct because LLMs operate within the context of natural languages. Statement B is also correct because LLMs can accept natural language prompts as input and produce natural language responses as output. This is why they are widely used in chatbots, virtual assistants, document analysis, business writing, and knowledge management systems. Statement C is correct because modern LLMs are built using neural network architectures and require significant computing resources, especially during training. They learn patterns from large text datasets and predict likely language outputs based on context.
Since A, B, and C are all correct descriptions of Large Language Models, the correct answer is E. a, b and c only .
NEW QUESTION # 76
Which of the following is a step for the Value Engineering Framework?
Answer: B
NEW QUESTION # 77
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