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| Certification Vendor: | USAII (United States Artificial Intelligence Institute) |
|---|---|
| Exam Name: | Certified Artificial Intelligence Consultant |
| Exam Number: | CAIC |
| Related Certifications: | CAIEโข (Certified Artificial Intelligence Engineer) CAISโข (Certified Artificial Intelligence Specialist) |
| Real Exam Qty: | 70 |
| Exam Format: | Single or multiple correct answers, Multiple-choice, Computer-based |
| Passing Score: | 70% |
| Certificate Validity Period: | 3 years |
| Exam Price: | US $894 |
| Exam Duration: | 100 minutes |
| Available Languages: | English |
| Recommended Training: | Official CAIC Learning Material |
| Exam Registration: | USAII Official Registration |
| Sample Questions: | USAII CAIC Sample Questions |
| Exam Way: | Online remote proctored or onsite computer-based exam |
| Pre Condition: | 4 eligibility paths: 1) Associate/Diploma + 6 years programming experience; 2) Bachelor's + 2 years relevant experience; 3) Master's (current/completed) + basic proficiency preferred; 4) CAIE certification + 1โ4 years experience (depending on degree) |
| Official Syllabus URL: | https://www.usaii.org/artificial-intelligence-certifications/certified-artificial-intelligence-consultant |
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NEW QUESTION # 29
A healthcare organization has a small number of labeled medical images and a much larger number of unlabeled images. The AI model uses both datasets to improve disease classification accuracy. This is an example of ______.
Answer: A
Explanation:
Semi-supervised learning is the correct answer because the model is trained using a combination of labeled and unlabeled data. This approach is useful when labeled data is expensive, time-consuming, or difficult to obtain, which is common in healthcare because medical images often require expert annotation. The small labeled dataset provides guidance, while the larger unlabeled dataset helps the model learn broader patterns and improve classification performance. Supervised learning is not the best answer because the scenario does not rely only on labeled data. Unsupervised learning is incorrect because the goal is disease classification, and some labeled examples are available. Reinforcement learning is incorrect because there are no rewards, actions, or environment-based feedback. Rule-based learning is also incorrect because the model is learning from data, not from manually coded rules. Therefore, the correct answer is D. semi-supervised learning .
NEW QUESTION # 30
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 # 31
Which of the following is NOT a common supervised learning model/algorithm?
Answer: C
Explanation:
The correct answer is E. None of the above because K-nearest neighbors, random forest, and decision trees are all common supervised learning models or algorithms. Supervised learning uses labeled data to train a model so it can predict an output label or target value for new data.
K-nearest neighbors is a supervised learning algorithm commonly used for classification and regression. It predicts outcomes by comparing a new data point with the most similar labeled examples in the training data.
Random forest is also a supervised learning algorithm. It builds multiple decision trees and combines their results to improve prediction accuracy and reduce overfitting. Decision trees are supervised models that split data based on feature values to make classification or regression predictions.
Since options A, B, and C are all valid supervised learning algorithms, none of them is the correct example of a model that is NOT commonly supervised. Therefore, the correct answer is E. None of the above .
NEW QUESTION # 32
Artificial narrow intelligence ANI is also commonly expressed as ____.
Answer: B
Explanation:
The correct answer is A. Weak AI . Artificial Narrow Intelligence, or ANI, is commonly called Weak AI because it is designed to perform a specific task or a limited set of tasks within a defined domain. Examples include recommendation engines, search engines, spam filters, facial recognition systems, voice assistants, fraud detection tools, and chatbots. These systems can perform their assigned functions effectively, but they do not possess general intelligence, consciousness, self-awareness, or human-like understanding across all domains.
Strong AI and General AI refer to Artificial General Intelligence, which would be capable of broad reasoning, learning, and problem-solving across many tasks like a human. SuperAI refers to a theoretical level of intelligence beyond human capability. ExpertAI is not the standard expression for ANI. Since ANI is task- specific and limited in scope, it is correctly expressed as Weak AI .
NEW QUESTION # 33
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: A
NEW QUESTION # 34
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