CAIC無料認定試験材料はこちらの73問題 [Q28-Q50]

CAIC無料認定試験材料はこちらの73問題

リアルCAICは100%カバー率リアル試験問題を試そう！

質問 # 28
Which of the following is a CORRECT statement for Fine-tuning?

• A. a, b and c only
• B. The key idea behind fine-tuning is to leverage the knowledge learned from the pre-trained model and fine-tune it to the new task, rather than training a model from scratch.
• C. Fine-tuning is the process of adapting a pre-trained model to a new task.
• D. a and b only
• E. In fine-tuning, the parameters of the pre-trained model are altered.

正解：A

解説：
The correct answer is E. a, b and c only because all three statements accurately describe fine-tuning. Fine- tuning is a machine learning and AI technique where a model that has already been trained on a large dataset is further trained or adapted for a more specific task, domain, or use case. This is common in natural language processing, generative AI, computer vision, and business AI applications.
Statement A is correct because fine-tuning adapts a pre-trained model to a new task. Statement B is also correct because during fine-tuning, some or all model parameters may be updated based on task-specific data.
Statement C is correct because the main advantage of fine-tuning is that it uses the general knowledge already learned by the pre-trained model instead of building a new model from the beginning. This saves time, data, compute resources, and often improves performance on specialized tasks. Therefore, the best answer is E .

質問 # 29
Which of the following is NOT CORRECT for the Elbow method?

• A. None of the above
• B. In K-means clustering, the ideal number of clusters is established using the elbow method.
• C. The Elbow method is used to determine the number of clusters to be formed.
• D. The elbow method is a heuristic used in cluster analysis to estimate the number of clusters present in a dataset.
• E. All of the above

正解：A

解説：
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 .

質問 # 30
Which of the following is NOT a pillar of the GenAI Well-Architected Framework?

• A. Security & Privacy
• B. Operational Excellence
• C. Reliability
• D. None of the above
• E. System Architecture Excellence

正解：E

解説：
The correct answer is D. System Architecture Excellence because it is not normally identified as a standard pillar of a GenAI Well-Architected Framework. Well-architected AI and GenAI frameworks commonly focus on structured pillars such as operational excellence, security and privacy, reliability, performance, cost optimization, responsible AI, and governance-related practices. These pillars help organizations design GenAI solutions that are secure, scalable, reliable, maintainable, and aligned with business and ethical expectations.
Operational excellence is a valid pillar because GenAI systems require proper deployment processes, observability, automation, monitoring, incident response, and lifecycle management. Security and privacy are also essential because GenAI applications often process sensitive data, prompts, outputs, embeddings, and model interactions. Reliability is another valid pillar because GenAI solutions must handle failures, latency, model availability, fallback mechanisms, and consistent service delivery.
"System Architecture Excellence" sounds related to solution design, but it is not a recognized pillar name in the listed framework. Therefore, the option that is NOT a pillar is D .

質問 # 31
Choose the CORRECT statement for ChatGPT.

• A. ChatGPT can maintain the memory of the previous context.
• B. ChatGPT can maintain the memory of the previous context as per the TPU used.
• C. ChatGPT can maintain the memory of the previous context depending upon the algorithm.
• D. None of the above
• E. All of the above

正解：C

解説：
The correct answer is B because ChatGPT's ability to maintain and use previous conversational context depends mainly on its model architecture, algorithmic design, token context window, and how the conversation history is processed. ChatGPT is based on large language model technology that uses patterns in prior text to generate relevant responses. It does not "remember" in the same way a human does; rather, it uses the available previous context within the conversation to predict and generate the next response.
Option A is partially true but incomplete because it says ChatGPT can maintain previous context without explaining the dependency on the model's design and context-handling mechanism. Option C is incorrect because TPU hardware may support model training or inference performance, but it does not determine conversational memory by itself. Since option C is wrong, "All of the above" cannot be correct. "None of the above" is also incorrect because option B correctly describes the concept. Therefore, the best answer is B .

質問 # 32
Which of the following is a CORRECT statement for DevOps architect?

• A. DevOps focuses on creating intelligent systems that possess human-like cognitive abilities and can perform a wide range of tasks across multiple domains.
• B. a, b and c only
• C. The key responsibilities of a DevOps architect are to establish and optimize continuous integration and continuous deployment CI/CD pipelines.
• D. DevOps is a collaborative approach that bridges the gap between development and operations teams, enabling them to work together seamlessly.
• E. a and b only

正解：E

解説：
The correct answer is D. a and b only because statements A and B correctly describe DevOps and the role of a DevOps architect. DevOps is a collaborative approach that connects software development and IT operations so teams can build, test, deploy, monitor, and improve systems more efficiently. It emphasizes automation, communication, continuous delivery, monitoring, reliability, and faster release cycles.
Statement B is also correct because a DevOps architect is responsible for designing and optimizing CI/CD pipelines. These pipelines support continuous integration, automated testing, continuous deployment, infrastructure automation, and reliable software delivery. A DevOps architect may also consider monitoring, security, scalability, performance, and disaster recovery.
Statement C is incorrect because it describes the goal of advanced AI or artificial general intelligence, not DevOps. DevOps does not focus on creating human-like intelligent systems across multiple domains.
Therefore, the best answer is D. a and b only .

質問 # 33
Which of the following statement is CORRECT for RNN?

• A. a, b and c only
• B. Long Short-Term Memory LSTM networks are an extension of RNNs that encapsulate long-term memory.
• C. One prominent drawback of RNNs is that they cannot remember more than a few time steps.
• D. a and b only
• E. RNN lacks long-term memory.

正解：A

解説：
The correct answer is E. a, b and c only because all three statements correctly describe Recurrent Neural Networks and their limitation. RNNs are neural network models designed for sequential data such as text, speech, time-series data, and ordered events. They process information step by step and use previous hidden states to influence later outputs.
Statement A is correct because a major drawback of traditional RNNs is their difficulty in remembering information over many time steps. This happens mainly because of vanishing gradient problems during training. Statement B is also correct because standard RNNs generally struggle with long-term dependencies, meaning they may fail to retain important information from earlier parts of a sequence. Statement C is correct because Long Short-Term Memory networks are a specialized extension of RNNs designed to handle long- term memory more effectively using gates that control what information is stored, forgotten, and passed forward.
Therefore, the best answer is E. a, b and c only .

質問 # 34
Which of the following is a step for the Value Engineering Framework?

• A. Define value creation
• B. Scale value creation
• C. Realize value creation
• D. All of the above
• E. None of the above

正解：D

解説：
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 .

質問 # 35
What type of learning is used when a model is trained with labeled data?

• A. Supervised Learning
• B. Unsupervised Learning
• C. Semi-supervised Learning
• D. Support Vector
• E. Reinforcement Learning

正解：A

解説：
The correct answer is B. Supervised Learning . Supervised learning is the machine learning approach used when a model is trained with labeled data. Labeled data means each training example includes both the input and the correct output or target label. The model studies these examples and learns the relationship between the input features and the expected result. After training, it can make predictions or classifications on new data.
Unsupervised learning is incorrect because it uses unlabeled data and focuses on finding hidden patterns, clusters, or structures without predefined answers. Reinforcement learning is incorrect because it involves an agent learning through actions, rewards, and penalties in an environment. Semi-supervised learning is also not the best answer because it uses a mix of labeled and unlabeled data. Support Vector refers to part of the Support Vector Machine method, not a learning type by itself. Therefore, the correct learning type for labeled data is B. Supervised Learning .

質問 # 36
Select the most CORRECT statement.

• A. b and c only
• B. a and c only
• C. To reduce the dimensionality of huge datasets, a technique known as PCA is used.
• D. Dimensionality reduction is the process of reducing the number of targetted variables considered.
• E. Dimensionality reduction is the process of reducing the number of random variables considered.

正解：B

解説：
The correct answer is D. a and c only because dimensionality reduction is the process of reducing the number of variables or features considered in a dataset while trying to preserve the most important information. This is especially useful when working with large datasets that contain many columns, attributes, or variables.
Reducing dimensionality can improve model performance, reduce computational cost, remove noise, and make data easier to visualize and analyze.
Statement A is correct because dimensionality reduction reduces the number of variables considered in the analysis. Statement C is also correct because Principal Component Analysis, or PCA, is one of the most common techniques used to reduce the dimensionality of large datasets. PCA transforms the original variables into a smaller set of principal components that capture most of the important variance in the data.
Statement B is not correct because dimensionality reduction is not about reducing "targetted variables." It focuses mainly on reducing input features or random variables. Therefore, the best answer is D. a and c only .

質問 # 37
Choose the CORRECT second step in the ML lifecycle?

• A. Data understanding
• B. a and b only
• C. Data acquisition
• D. Data preparation
• E. All of the above

正解：A

解説：
The correct answer is B. Data understanding . In the machine learning lifecycle, after the initial data acquisition or data collection stage, the next major activity is to understand the available data. Data understanding involves exploring the dataset, reviewing data sources, identifying variables, checking patterns, finding missing values, detecting outliers, and understanding whether the data is suitable for the business or operational problem being solved.
Data acquisition is usually an earlier step because the data must first be collected or accessed before it can be analyzed. Data preparation comes after data understanding because teams need to know the data's structure, quality, gaps, and relevance before cleaning, transforming, engineering features, or formatting it for model training. Options D and E are not correct because the question asks for the single second step, not a combination of lifecycle activities. Therefore, the correct second step in the ML lifecycle is B. Data understanding .

質問 # 38
Which of the following is a regulatory requirement?

• A. a, b and c only
• B. CCPA
• C. GDPR
• D. HIPAA
• E. a and b only

正解：A

解説：
The correct answer is E. a, b and c only because HIPAA, GDPR, and CCPA are all regulatory requirements that organizations may need to follow when collecting, processing, storing, sharing, or protecting data. In AI and data-driven business environments, regulatory compliance is important because AI systems often depend on large volumes of personal, sensitive, customer, health, or behavioral data.
HIPAA is a United States healthcare regulation that protects sensitive patient health information. GDPR is a European Union data protection regulation that governs personal data processing, privacy rights, consent, data minimization, and accountability. CCPA is a California privacy law that gives consumers rights related to personal information, including access, deletion, and opt-out rights. These regulations affect how organizations design AI systems, manage data governance, ensure privacy, reduce risk, and maintain responsible AI practices.
Since all three listed items are valid regulatory requirements, the correct answer is E. a, b and c only .

質問 # 39
Which of the following is the CORRECT stage of the Data and AI Analytics Business Model Maturity Index?

• A. Business Monitoring
• B. Business Optimization
• C. Business Insights
• D. Cultural Transformation
• E. All of the above

正解：E

解説：
The correct answer is E. All of the above because the Data and AI Analytics Business Model Maturity Index describes how organizations progress in their ability to use data, analytics, and AI for business value creation.
Business Monitoring is a valid stage because organizations first use data to observe performance, track metrics, and understand what is happening in the business. Business Insights is also a correct stage because analytics then helps organizations explain why things are happening and identify patterns, opportunities, and risks.
Business Optimization is another valid stage because mature organizations use analytics and AI to improve processes, decisions, resources, customer experiences, and operational outcomes. Cultural Transformation is also part of maturity because long-term AI and data success requires a shift in mindset, leadership behavior, decision-making culture, and enterprise-wide adoption of data-driven practices.
Since all listed options represent stages or maturity areas in the Data and AI Analytics Business Model Maturity Index, the correct answer is E. All of the above .

質問 # 40
What is solution architecture?

• A. a, b and c only
• B. A solution architecture encompasses the entire system, including aspects such as system infrastructure, networking, security, compliance requirements, system operation, cost considerations, and reliability.
• C. A solutions architecture is a blueprint that not only ensures that the solution meets the present needs of the business but also lays the groundwork for its future growth and success.
• D. A solution architecture creates a comprehensive blueprint that guides the development and implementation of the solution.
• E. a and b only

正解：A

解説：
Solution architecture is the structured design blueprint that explains how a business or technology solution will be built, integrated, operated, secured, and scaled. Option A is correct because solution architecture guides development and implementation by defining components, workflows, integrations, platforms, data flows, and technical decisions. Option B is also correct because a complete solution architecture considers the whole system, including infrastructure, networking, security, compliance, operations, cost, performance, and reliability. These elements are necessary to ensure that the solution can work in a real enterprise environment.
Option C is also correct because solution architecture does not only address current business requirements. It also supports future growth by planning for scalability, maintainability, adaptability, and long-term business success. Since all three statements accurately describe solution architecture, the most complete and correct answer is E. a, b and c only .

質問 # 41
Deep Learning is a subset of ____.

• A. Artificial narrow intelligence ANI
• B. Artificial general intelligence AGI
• C. Artificial Intelligence
• D. All of the above
• E. Machine Learning

正解：E

解説：
The correct answer is A. Machine Learning . Deep learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers to learn patterns from data. These layered neural networks can automatically discover features and representations from large datasets, which makes deep learning especially useful for image recognition, speech recognition, natural language processing, recommendation systems, and generative AI applications.
Artificial intelligence is the broader field that includes machine learning, expert systems, reasoning systems, robotics, natural language processing, and other intelligent technologies. Machine learning is a branch within artificial intelligence, and deep learning is a further subset within machine learning. Artificial Narrow Intelligence refers to AI systems designed for specific tasks, while Artificial General Intelligence refers to a theoretical system with broad human-like intelligence. Since deep learning is most directly and correctly classified as a subset of machine learning, the best answer is A .

質問 # 42
What is a prompt?

• A. For ChatGPT and OpenAI models, prompts mainly refer to a piece of text in natural language.
• B. Prompts are the only way users can control the output generated by those models.
• C. Prompt refers to the input the user provides to the generative models.
• D. a, b and c only
• E. a and b only

正解：E

解説：
The correct answer is D. a and b only because a prompt is the input provided by a user to a generative AI model. In natural language systems such as ChatGPT and other language models, the prompt is usually written as text in natural language. It may be a question, instruction, command, description, context, example, or task requirement that guides the model toward producing a response.
Statement A is correct because prompts are the user-provided input that generative models use to produce outputs. Statement B is also correct because, for ChatGPT and similar models, prompts commonly appear as natural language text. Statement C is not fully correct because prompts are an important way to guide model output, but they are not the only possible control mechanism. Outputs can also be influenced by system instructions, model settings, retrieval context, fine-tuning, guardrails, and application design. Therefore, the best answer is D. a and b only .

質問 # 43
Select the MOST CORRECT statement for Few-shot learning.

• A. In Few-shot learning, the model is given a small number of examples typically between 3 and 5 of each new task it is asked to perform.
• B. b and c only
• C. a and b only
• D. In Few-shot learning, the model is given a large number of examples typically 10 or more of each new task it is asked to perform.
• E. In Few-shot learning, the model must use its prior knowledge to generalize from examples to perform the task.

正解：B

解説：
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 .

質問 # 44
A model is trained using historical customer records where each record already contains the correct outcome, such as "churn" or "not churn." The model then predicts whether future customers are likely to churn. This is an example of ______.

• A. reinforcement learning
• B. unsupervised learning
• C. supervised learning
• D. generative learning
• E. clustering

正解：C

解説：
Supervised learning is used when a machine learning model is trained on labeled data. In this case, the historical customer records already include the correct outcome labels, such as "churn" or "not churn." The model learns the relationship between customer attributes and the known outcome, then applies that learned relationship to predict outcomes for new customers. This is a classic classification problem. Unsupervised learning is incorrect because it works with unlabeled data and is commonly used for clustering or discovering hidden patterns. Reinforcement learning is incorrect because there is no reward-based decision-making environment described. Generative learning is not the best answer because the task is prediction, not creating new content. Therefore, the correct answer is A. supervised learning .

質問 # 45
Which of the CORRECT cognitive modeling is used in AI applications?

• A. Robotics
• B. Natural language processing
• C. Expert systems
• D. All of the above
• E. Deep learning

正解：D

解説：
The correct answer is E. All of the above because deep learning, expert systems, natural language processing, and robotics are all connected with AI applications that support or model intelligent behavior. Cognitive modeling in AI is concerned with building systems that can represent, simulate, or support human-like capabilities such as learning, reasoning, decision-making, perception, language understanding, and action.
Deep learning is used to recognize patterns from large amounts of data and is common in speech recognition, image analysis, recommendation systems, and generative AI. Expert systems use knowledge bases and rules to support decision-making in specialized domains. Natural language processing helps AI systems understand, interpret, generate, and respond to human language. Robotics applies AI to physical systems so machines can sense, plan, move, and perform tasks in real-world environments.
Since all the listed options are valid AI application areas related to intelligent and cognitive capabilities, the correct answer is E. All of the above .

質問 # 46
Choose the CORRECT example of Reinforcement Learning.

• A. robotics
• B. navigation
• C. All of the above
• D. game playing
• E. None of the above

正解：C

解説：
The correct answer is D. All of the above because robotics, game playing, and navigation are all common examples of reinforcement learning. Reinforcement learning is a machine learning approach in which an agent learns by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a policy that helps it maximize long-term reward.
Robotics is a strong example because robots can learn movement, object handling, path planning, and control actions through trial and feedback. Game playing is another classic reinforcement learning example because an AI agent can learn winning strategies by trying actions, observing outcomes, and improving decisions over repeated episodes. Navigation is also a valid example because an agent can learn the best route or movement strategy by receiving feedback about distance, obstacles, time, or success in reaching a goal.
Since all three listed options are valid applications of reinforcement learning, the correct answer is D. All of the above .

質問 # 47
Choose the CORRECT option for conjoint analysis.

• A. Conjoint analysis is used in product management, marketing, and advertising.
• B. Conjoint analysis is an approach to product and pricing research.
• C. Conjoint analysis identifies customer preferences and makes use of that knowledge to choose product features.
• D. Conjoint analysis evaluates price sensitivity and anticipates market shares.
• E. All of the above

正解：E

解説：
The correct answer is E. All of the above because each statement accurately describes conjoint analysis and its business use. Conjoint analysis is a research technique used to understand how customers value different product or service attributes. It helps organizations evaluate trade-offs customers make between features, pricing, brand, quality, service levels, and other product characteristics.
Statement A is correct because conjoint analysis is commonly used in product and pricing research. Statement B is also correct because it identifies customer preferences and helps businesses decide which product features are most valuable to different customer segments. Statement C is correct because conjoint analysis can evaluate price sensitivity and estimate how changes in product features or pricing may affect demand and market share. Statement D is also correct because the method is widely used in product management, marketing strategy, advertising, product positioning, and go-to-market planning.
Since all listed statements are correct, the best answer is E. All of the above .

質問 # 48
Artificial narrow intelligence ANI is also commonly expressed as ____.

• A. Strong AI
• B. Weak AI
• C. General AI
• D. SuperAI
• E. ExpertAI

正解：B

解説：
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 .

質問 # 49
Which one of the following is a NOT good attribute of solution architecture?

• A. Risk mitigation
• B. Technology in alignment with business requirements
• C. Scalability and flexibility
• D. Tightly coupled architecture
• E. Increased ROI

正解：D

解説：
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 .

質問 # 50
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