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| Certification Vendor: | USAII |
|---|---|
| Exam Name: | Certified Artificial Intelligence Consultant |
| Exam Number: | CAIC |
| Exam Duration: | 100 minutes |
| Related Certifications: | CAIC™ |
| Passing Score: | 70% |
| Available Languages: | English |
| Exam Format: | Multiple Choice, Multiple Response |
| Real Exam Qty: | 35 |
| 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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問題 #21
What is a Large Language Model?
答案：E
解題說明：
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 .
問題 #22
Choose the CORRECT example of Supervised Learning.
答案：A
解題說明：
The correct answer is B. House price prediction . Supervised learning is a machine learning approach where a model is trained using labeled data. In a house price prediction problem, the training data usually contains property features such as size, location, number of rooms, age of the house, and past selling prices. The known selling price acts as the label or target value. The model learns the relationship between the input features and the price, then predicts prices for new houses.
A driverless car is not the best single example because autonomous driving uses a combination of AI techniques, including supervised learning, reinforcement learning, computer vision, sensor fusion, planning, and control systems. ChatGPT is a generative AI language model and is not typically used as the basic example of supervised learning in this context. Since house price prediction directly represents supervised learning with labeled input-output data, the correct answer is B .
問題 #23
Supervised learning is a type of machine learning where the algorithm learns from a ______.
答案：D
解題說明：
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.
問題 #24
Select the most INCORRECT risk-scoring methodology function statement for retrospective/concurrent.
答案：E
解題說明：
The correct answer is D. a and b only because statements A and B are the most incorrect for retrospective
/concurrent risk-scoring methodology. Retrospective/concurrent risk assessment is mainly used to evaluate model risk based on past or present evidence, current model behavior, observed incidents, model performance changes, risk indicators, and investigation findings. It is not primarily a future-prediction method.
Statement A is incorrect because it says retrospective/concurrent methods "predict" model risk after analyzing historical model performance. Historical performance may be reviewed, but retrospective/concurrent risk scoring is more about assessing or investigating past and current risk conditions, not predicting future risk.
Statement B is also incorrect because using current model risk to predict overall model risk for future cycles describes prospective risk, not retrospective/concurrent risk. Statement C is correct because retrospective
/concurrent review is suitable when there are changes in model behavior, risk indicators, attacks, data loss, or investigation needs. Therefore, the most incorrect statements are A and B only .
問題 #25
What is the main advantage of using deep learning over traditional machine learning?
答案：E
解題說明：
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 .
問題 #26
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