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SAP C_AIG_2412 認定試験の出題範囲：
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質問 # 39
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.
- A. To follow a specific schema - human input, Al processing, and output for human consumption.
- B. To produce outputs based on software input.
- C. To interpret human instructions and control software systems without necessarily producing output for human consumption.
- D. To interpret human instructions and control software systems always producing output for human consumption.
正解：C、D
質問 # 40
How can few-shot learning enhance LLM performance?
- A. By reducing overfitting through regularization techniques
- B. By providing a large training set to improve generalization
- C. By enhancing the model's computational efficiency
- D. By offering input-output pairs that exemplify the desired behavior
正解：D
質問 # 41
Which of the following is a principle of effective prompt engineering?
- A. Combine multiple complex tasks into a single prompt.
- B. Write vague and open-ended instructions to encourage creativity.
- C. Keep prompts as short as possible to avoid confusion.
- D. Use precise language and providing detailed context in prompts.
正解：D
解説：
Effective prompt engineering is crucial for guiding AI models to produce accurate and relevant outputs.
1. Importance of Precision and Context:
* Clarity:Using precise language in prompts minimizes ambiguity, ensuring the AI model comprehends the exact requirements.
* Detailed Context:Providing comprehensive context helps the model understand the background and nuances of the task, leading to more accurate and tailored responses.
2. Best Practices in Prompt Engineering:
* Specificity:Clearly define the desired outcome, including any constraints or specific formats required.
* Instruction Inclusion:Incorporate explicit instructions within the prompt to guide the model's behavior effectively.
* Avoiding Ambiguity:Steer clear of vague or open-ended language that could lead to varied interpretations.
3. Benefits of Effective Prompt Engineering:
* Enhanced Output Quality:Well-crafted prompts lead to responses that closely align with user expectations.
* Efficiency:Reduces the need for iterative refinements, saving time and computational resources.
質問 # 42
How can few-shot learning enhance LLM performance?
- A. By reducing overfitting through regularization techniques
- B. By providing a large training set to improve generalization
- C. By enhancing the model's computational efficiency
- D. By offering input-output pairs that exemplify the desired behavior
正解：D
解説：
Few-shot learning enhances the performance of Large Language Models (LLMs) by providing them with a limited number of input-output examples that demonstrate the desired task behavior.
1. Mechanism of Few-Shot Learning:
* Exemplification:By supplying a few examples, the model gains insight into the task requirements, enabling it to generalize from these instances to handle new, unseen inputs effectively.
* Adaptability:This approach allows LLMs to adapt to specific tasks without extensive retraining, making them versatile across various applications.
2. Benefits in Performance Enhancement:
* Improved Accuracy:With clear examples, the model's predictions align more closely with the desired outcomes, reducing errors.
* Efficiency:Few-shot learning minimizes the need for large datasets, accelerating the development process and conserving computational resources.
質問 # 43
What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question.
- A. The model can be registered in the hyperscaler object store.
- B. The model can be deployed in SAP HAN
- C. The model's accuracy can be optimized directly in SAP HANA.
- D. The model can be deployed for inferencing.
正解：A、D
質問 # 44
Which of the following executables in generative Al hub works with Anthropic models?
- A. AWS Bedrock
- B. GCP Vertex Al
- C. SAP AI Core
- D. Azure OpenAl Service
正解：A
解説：
In SAP's Generative AI Hub, the integration with Anthropic models is facilitated through specific executables:
1. AWS Bedrock:
* Integration with Anthropic Models:AWS Bedrock provides access to Anthropic's Claude models, enabling developers to utilize these models within their applications.
* Execution via Generative AI Hub:Through the Generative AI Hub, developers can select AWS Bedrock as the executable to work with Anthropic models, integrating them into their AI solutions.
Conclusion:
To work with Anthropic models within SAP's Generative AI Hub, developers should utilize the AWS Bedrock executable, which provides access to these models for integration into their applications.
質問 # 45
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.
- A. Speed
- B. Carbon footprint
- C. Faithfulness
- D. Relevance
正解：C、D
解説：
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.
質問 # 46
How do resource groups in SAP AI Core improve the management of machine learning workloads?
Note: There are 2 correct answers to this question.
- A. They enhance pipeline execution speeds through workload distribution.
- B. They ensure workload separation for different tenants or departments.
- C. They provide isolation for datasets and Al artifacts.
- D. They enable simultaneous orchestration of Kubernetes clusters.
正解：B、C
質問 # 47
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.
- A. They enhance pipeline execution speeds through workload distribution.
- B. They ensure workload separation for different tenants or departments.
- C. They provide isolation for datasets and Al artifacts.
- D. They enable simultaneous orchestration of Kubernetes clusters.
正解：B、C
解説：
Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.
1. Ensuring Workload Separation for Different Tenants or Departments:
* Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.
* Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.
質問 # 48
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system?
Note: There are 2 correct answers to this question.
- A. Speed
- B. Carbon footprint
- C. Faithfulness
- D. Relevance
正解：C、D
質問 # 49
What is a part of LLM context optimization?
- A. Enhancing the computational speed of the model
- B. Adjusting the model's output format and style
- C. Providing the model with domain-specific knowledge needed to solve a problem
- D. Reducing the model's size to improve efficiency
正解：C
解説：
LLM context optimization involves tailoring a Large Language Model's (LLM) input context to enhance its performance on specific tasks, particularly by incorporating domain-specific knowledge.
1. Understanding LLM Context Optimization:
* Definition:Context optimization refers to the process of adjusting the input provided to an LLM to ensure it includes relevant information, thereby enabling the model to generate more accurate and contextually appropriate outputs.
* Domain-Specific Knowledge Integration:By embedding domain-specific information into the model's context, the LLM can better understand and address specialized queries, leading to improved problem- solving capabilities.
2. Importance of Domain-Specific Knowledge:
* Enhanced Relevance:Providing domain-specific context ensures that the model'sresponses are pertinent to the particular field or subject matter, increasing the utility of the generated content.
* Improved Accuracy:With access to specialized knowledge, the LLLM is less likely to produce generic or incorrect answers, thereby enhancing the overall quality of its outputs.
3. Methods of Context Optimization:
* Prompt Engineering:Crafting prompts that include necessary domain-specific information to guide the model towards generating desired responses.
* Retrieval-Augmented Generation (RAG):Incorporating external data sources into the model's context to provide up-to-date and relevant information pertinent to the domain.
質問 # 50
How can Joule improve workforce productivity?
Note: There are 2 correct answers to this question.
- A. By maintaining strict adherence to data privacy regulations.
- B. By offering generic task recommendations unrelated to specific roles.
- C. By providing context-based role-specific task assistance.
- D. By resolving hardware malfunctions.
正解：A、C
質問 # 51
What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.
- A. To streamline LLM workflows
- B. To guarantee accurate decision making in complex scenarios
- C. To improve the quality of results
- D. To eliminate the need for human oversight
正解：A、C
解説：
Incorporating agents into the training and deployment of Large Language Models (LLMs) offers notable advantages:
1. Improving the Quality of Results:
* Specialized Task Handling:Agents can be designed to manage specific tasks or subtasks within a larger process, ensuring that each component is handled with expertise, thereby enhancing the overall quality of the output.
* Error Reduction:By delegating particular functions to specialized agents, the likelihood of errors decreases, leading to more accurate and reliable results.
2. Streamlining LLM Workflows:
* Process Automation:Agents can automate repetitive or time-consuming tasks within the LLM workflow, increasing efficiency and allowing human resources to focus on more complex aspects of model development and deployment.
* Workflow Management:Agents facilitate the coordination of various stages in the LLM pipeline, ensuring seamless transitions between tasks and improving overall workflow efficiency.
3. Enhancing Model Performance:
* Adaptive Learning:Agents can monitor model performance and implement adjustments in real-time, promoting continuous improvement and adaptability to new data or requirements.
* Resource Optimization:By managing specific tasks, agents help in optimizing computational resources, ensuring that the LLM operates efficiently without unnecessary expenditure of processing power.
質問 # 52
Which of the following are grounding principles included in SAP's AI Ethics framework?
Note: There are 3 correct answers to this question.
- A. Maximize business profits
- B. Human agency and oversight
- C. Store all user data for legal proceedings
- D. Transparency and explainability
- E. Avoid bias and discrimination
正解：B、D、E
質問 # 53
What is the primary function of the embedding model in a RAG system?
- A. To encode queries and documents into vector representations for comparison
- B. To generate responses based on retrieved documents and user queries
- C. To store vector representations of documents and search for relevant passages
- D. To evaluate the faithfulness and relevance of generated answers
正解：A
質問 # 54
What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question.
- A. Infrastructure resources such as CPUs or GPUs
- B. Deployment templates for SAP AI Launchpad
- C. User scripts to manually execute pipeline steps
- D. Pipeline containers to be used
正解：A、D
質問 # 55
How does SAP deal with vulnerability risks created by generative Al?
Note: There are 2 correct answers to this question.
- A. By identifying human, technical, and exfiltration risks through an Al Security Taskforce.
- B. By focusing on technological advancement only.
- C. By implementing responsible Al use guidelines and strong product security standards.
- D. By relying on external vendors to manage security threats.
正解：A、C
質問 # 56
What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note: There are 3 correct answers to this question.
- A. Maintaining data privacy by using data masking techniques
- B. Creating custom evaluators that meet specific business needs
- C. Automating prompt testing across various scenarios
- D. Providing metrics to quantitatively assess response quality
- E. Supporting low code evaluations using graphical user interface
正解：B、C、D
質問 # 57
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.
- A. Customize SAP AI Launchpad
- B. Configure SAP BTP credentials
- C. Create chat responses and embeddings
- D. Interact with LLMs
正解：C、D
解説：
The Generative AI Hub SDK offers functionalities that empower developers to:
1. Interact with Large Language Models (LLMs):
* Model Access:The SDK provides a developer-friendly way to consume foundational models available in the SAP Generative AI Hub, facilitating seamless interactions with these models.
2. Create Chat Responses and Embeddings:
* Natural Language Processing:With this SDK, developers can interact with models to create natural language completions, chat responses, and embeddings, enabling the development of sophisticated AI- driven applications.
Conclusion:
The Generative AI Hub SDK enables developers to interact with LLMs and create chat responses and embeddings, supporting the development of advanced AI functionalities within applications.
質問 # 58
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