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Itexamdump는 모든 IT관련 인증시험자료를 제공할 수 있는 사이트입니다. 우리Itexamdump는 여러분들한테 최고 최신의 자료를 제공합니다. Itexamdump을 선택함으로 여러분은 이미USAII CAIC시험을 패스하였습니다. 우리 자료로 여러분은 충분히USAII CAIC를 패스할 수 있습니다. 만약 시험에서 떨어지셨다면 우리는 백프로 환불은 약속합니다. 그리고 갱신이 된 최신자료를 보내드립니다. 하지만 이런사례는 거이 없었습니다.모두 한번에 패스하였기 때문이죠. Itexamdump는 여러분이USAII CAIC인증시험 패스와 추후사업에 모두 도움이 되겠습니다. Pass4Tes의 선택이야말로 여러분의 현명한 선택이라고 볼수 있습니다. Pass4Tes선택으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트버전을 바을수 있는 기회를 얻을수 있습니다.
질문 # 64
Which is the first useful computer program that came into existence in the AI world?
정답：D
설명：
The correct answer is B. GPS . In artificial intelligence history, GPS stands for General Problem Solver . It was an early AI program developed to simulate human problem-solving behavior. GPS was designed to solve problems by breaking them down into goals, subgoals, operators, and differences between the current state and the desired state. This approach became important because it introduced structured reasoning and symbolic problem solving, which were central ideas in early AI research.
LPS, MLS, AIS, and EPS are not the standard answer for the first useful computer program in the AI world in this context. GPS is widely recognized as one of the earliest useful AI programs because it attempted to model general reasoning rather than solving only one narrow calculation task. It showed how computers could be programmed to search through possible actions and work toward a goal. Therefore, the correct answer is B.
GPS .
질문 # 65
What is the main advantage of using deep learning over traditional machine learning?
정답：A
질문 # 66
Which one of the following is a CORRECT benefit for using AI in product development?
정답：A
설명：
The correct answer is D. a and b only because AI provides strong benefits across the product development life cycle, especially by improving speed, decision quality, and data-driven design. Statement A is correct because AI can shorten the product development life cycle by automating research, analyzing customer feedback, generating product ideas, supporting rapid prototyping, improving testing, and helping teams identify risks or opportunities earlier.
Statement B is also correct because applying AI throughout the PDLC helps organizations use data consistently at every stage, from ideation and market research to design, testing, launch, and post-launch improvement. This means products are not only based on data at the beginning but continue to reflect data- driven insights throughout development.
Statement C is not the best answer because "increase the product feature" is unclear and grammatically incomplete. AI may help improve features or identify new feature opportunities, but the statement is not as accurate as A and B. Therefore, the best answer is D. a and b only .
질문 # 67
Which of the following is a step for the Value Engineering Framework?
정답：A
설명：
The correct answer is E. All of the above because the Value Engineering Framework focuses on identifying, delivering, and expanding measurable business value from data and AI initiatives. "Define value creation" is a key step because organizations must first clarify the business problem, expected outcomes, success metrics, stakeholders, and value drivers before investing in an AI solution.
"Realize value creation" is also correct because value must be converted from a planned objective into actual operational or financial impact. This may involve deploying the solution, measuring results, improving processes, reducing cost, increasing revenue, improving risk management, or enhancing customer outcomes.
"Scale value creation" is correct because successful AI initiatives should not remain limited to isolated pilots.
Organizations need to scale proven use cases across teams, business units, workflows, and enterprise platforms to maximize return on investment and long-term impact. Since all three options represent steps in value engineering, the best answer is E. All of the above .
질문 # 68
Which of the following is NOT a common supervised learning model/algorithm?
정답：B
설명：
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 .
질문 # 69
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