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| Certification Vendor: | USAII (United States Artificial Intelligence Institute) |
|---|---|
| Exam Name: | Certified Artificial Intelligence Consultant |
| Exam Number: | CAIC |
| Exam Duration: | 100 minutes |
| Exam Price: | US $894 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple-choice, Single or multiple correct answers, Computer-based |
| Passing Score: | 70% |
| Real Exam Qty: | 70 |
| Available Languages: | English |
| Related Certifications: | CAIE™ (Certified Artificial Intelligence Engineer) CAIS™ (Certified Artificial Intelligence Specialist) |
| 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 + 1–4 years experience (depending on degree) |
| Official Syllabus URL: | https://www.usaii.org/artificial-intelligence-certifications/certified-artificial-intelligence-consultant |
>> CAIC Valid Exam Discount <<
The online CAIC practice exam has all specifications of the desktop software. It is compatible with Chrome, Firefox, MS Edge, Safari, Opera, etc. The Certified Artificial Intelligence Consultant (CAIC) practice exam will save your progress and give you an overview of your mistakes, which will benefit your overall preparation. All operating systems support this Certified Artificial Intelligence Consultant (CAIC) practice test.
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NEW QUESTION # 68
If humans are labeling the data and the machine is correctly labeling current or future data points, it's ______.
Answer: B
Explanation:
The correct answer is A. supervised learning because supervised learning uses labeled data to train a machine learning model. In this method, humans or existing systems provide correct labels for the training examples, and the model learns the relationship between input data and the expected output labels. After training, the machine can apply what it has learned to correctly classify or label current and future data points.
Unsupervised learning is incorrect because it works with unlabeled data and discovers hidden patterns, groups, or structures without human-provided labels. Reinforcement learning is also incorrect because it is based on actions, rewards, penalties, and learning through interaction with an environment. Semi-supervised learning uses a combination of a small amount of labeled data and a larger amount of unlabeled data, but the question clearly states that humans are labeling the data. "Semi Reinforcement learning" is not the standard answer here. Therefore, the correct choice is A. supervised learning .
NEW QUESTION # 69
Supervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: B
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 # 70
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 # 71
Which of the following is NOT CORRECT for the Elbow method?
Answer: C
Explanation:
The correct answer is E. None of the above because all three statements about the Elbow method are correct.
The Elbow method is commonly used in unsupervised learning, especially with K-means clustering, to help estimate an appropriate number of clusters. It works by running clustering with different values of K and measuring the within-cluster variation or distortion. As K increases, the error usually decreases, but after a certain point the improvement becomes much smaller. That point is visually interpreted as the "elbow." Statement A is correct because the Elbow method helps determine how many clusters should be formed.
Statement B is also correct because it is widely used with K-means clustering to select a suitable value of K.
Statement C is correct because the method is a heuristic, meaning it is a practical estimation technique rather than an exact mathematical guarantee. Since A, B, and C are all correct, none of them is NOT correct.
Therefore, the correct answer is E. None of the above .
NEW QUESTION # 72
Which of the following is NOT a learning category for the ML model?
Answer: A
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 # 73
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