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| Certification Vendor: | USAII |
|---|---|
| Exam Name: | Certified Artificial Intelligence Consultant |
| Exam Number: | CAIC |
| Passing Score: | 70% |
| Real Exam Qty: | 35 |
| Exam Format: | Multiple Choice, Multiple Response |
| Available Languages: | English |
| Exam Duration: | 100 minutes |
| Related Certifications: | CAIC™ |
| Certificate Validity Period: | Lifetime |
| Exam Price: | USD 894 |
| 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 # 35
Artificial general intelligence (AGI) is also commonly expressed as ____.
Answer: E
Explanation:
Artificial General Intelligence, or AGI, is commonly referred to as Strong AI because it describes an AI system with human-like cognitive ability across many different tasks and domains. Unlike narrow or weak AI, which is designed to perform a specific task such as image recognition, language translation, recommendation, fraud detection, or chatbot response generation, AGI would be able to understand, learn, reason, adapt, and solve problems broadly in a way similar to human intelligence.
Weak AI is incorrect because it refers to task-specific AI systems that operate within limited boundaries.
General AI is related in meaning, but the commonly used expression for AGI in AI classification is Strong AI.
SuperAI is different because it refers to intelligence that would exceed human intelligence, while ExpertAI is not the standard term for AGI. Therefore, the correct answer is B. Strong AI .
NEW QUESTION # 36
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 # 37
Select the MOST CORRECT statement for Few-shot learning.
Answer: C
Explanation:
The correct answer is E. b and c only because few-shot learning means a model learns or adapts to a new task using only a small number of examples. In generative AI and large language model usage, few-shot prompting often provides a few demonstrations so the model can understand the expected pattern, format, classification logic, or response style. Option B is correct because few-shot learning uses a limited number of examples rather than a large training dataset.
Option C is also correct because few-shot learning depends on the model's prior knowledge learned during pretraining. The model uses that existing knowledge to generalize from the small set of examples and apply the same logic to new inputs. Option A is not the best statement because "a large number of examples" does not match the idea of few-shot learning. Therefore, the most correct answer is E. b and c only .
NEW QUESTION # 38
Which of the following is MLOps?
Answer: E
Explanation:
The correct answer is E. a, b and c only because MLOps includes workflow automation, continuous integration, and continuous deployment as important practices for managing the machine learning lifecycle.
MLOps, or Machine Learning Operations, applies DevOps-style principles to machine learning systems so models can be developed, tested, deployed, monitored, and maintained in a reliable and repeatable way.
Workflow automation is part of MLOps because machine learning pipelines often include data ingestion, data validation, feature engineering, model training, model evaluation, deployment, and monitoring. Continuous integration is also included because ML code, data pipelines, configuration files, and model components need regular testing and validation when changes are made. Continuous deployment is another key part because approved models should be deployed efficiently into production environments with version control, rollback options, and monitoring.
Since all three options describe important MLOps capabilities, the best answer is E. a, b and c only .
NEW QUESTION # 39
Which one of the following is a NOT good attribute of solution architecture?
Answer: A
Explanation:
The correct answer is C. Tightly coupled architecture because a strong solution architecture should promote flexibility, scalability, maintainability, integration readiness, and adaptability. A tightly coupled architecture means system components are highly dependent on one another. This creates problems when teams need to update, scale, replace, test, or modify one part of the system, because changes in one component can easily affect other components. In enterprise AI and software solution design, this increases operational risk, slows innovation, and makes future growth more difficult.
Technology alignment with business requirements is a good attribute because architecture must support business goals and operational needs. Scalability and flexibility are also good attributes because modern solutions must handle growth, changing workloads, and evolving requirements. Risk mitigation is a strong architectural objective because good design reduces security, performance, compliance, and operational risks.
Increased ROI is also a desired outcome when architecture improves efficiency and business value. Therefore, the attribute that is NOT good is C. Tightly coupled architecture .
NEW QUESTION # 40
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