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In the PDF version, real C-AIG-2412 exam questions are available. These SAP C-AIG-2412 real questions are printable and portable. You can take this PDF document anywhere and study for the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) exam without time restrictions. Lead2PassExam regularly make changes in the C-AIG-2412 PDF format when required. C-AIG-2412 questions in this format are relevant to the actual test.
NEW QUESTION # 13
Which of the following are grounding principles included in SAP's AI Ethics framework? Note: There are 3 correct answers to this question.
Answer: A,B,C
Explanation:
SAP's AI Ethics framework is built upon several grounding principles to ensure responsible AI development and deployment:
1. Transparency and Explainability:
* Definition:Ensuring that AI systems are understandable and their decision-making processes can be clearly explained to stakeholders.
* Implementation:SAP commits to making AI systems transparent, providing clearinformation about how decisions are made to build trust and facilitate accountability.
2. Human Agency and Oversight:
* Definition:Maintaining human control over AI systems, ensuring that humans can intervene or oversee AI operations as necessary.
* Implementation:SAP emphasizes the importance of human oversight in AI applications, ensuring that AI augments human decision-making rather than replacing it.
3. Avoid Bias and Discrimination:
* Definition:Preventing AI systems from perpetuating or amplifying biases, ensuring fair and equitable treatment for all users.
* Implementation:SAP strives to develop AI systems that are free from bias, implementing measures to detect and mitigate discriminatory outcomes.
NEW QUESTION # 14
How can Joule improve workforce productivity? Note: There are 2 correct answers to this question.
Answer: A,B
NEW QUESTION # 15
What is the goal of prompt engineering?
Answer: D
Explanation:
Prompt engineering involves designing and refining inputs, known as prompts, to effectively guide AI systems, particularly Large Language Models (LLMs), in producing desired outputs.
1. Understanding Prompt Engineering:
* Definition:Prompt engineering is the process of creating and optimizing prompts to elicit specific responses from AI models. It serves as a crucial interface between human intentions and machine- generated content.
* Purpose:The primary goal is to communicate the task requirements clearly to the AI model, ensuring that the generated output aligns with user expectations.
2. Importance in AI Systems:
* Guiding AI Behavior:Well-crafted prompts can direct AI models to perform a wide range of tasks, from answering questions to generating creative content, by setting the context and specifying the desired format of the output.
* Enhancing Output Quality:Effective prompt engineering can improve the relevance, coherence, and accuracy of AI-generated responses, making AI systems more useful and reliable in practical applications.
3. Application in SAP's Generative AI Hub:
* Prompt Management:SAP's Generative AI Hub provides tools for prompt management, allowing developers to create, edit, and manage prompts to interact with various AI models efficiently.
* Exploration and Development:The hub offers features like prompt editors and AI playgrounds, enabling users to experiment with different prompts and models to achieve optimal results for their specific use cases.
NEW QUESTION # 16
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.
Answer: A,D
Explanation:
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.
NEW QUESTION # 17
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
-"urgency" as one of {{?urgency}}
-"sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
' --- '.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])
Answer: C
Explanation:
The provided code is designed to evaluate the performance of a language model in assigning urgency and sentiment categories to customer emails by utilizing few-shot learning within SAP's Generative AI Hub.
1. Few-Shot Learning in Prompt Engineering:
* Definition:Few-shot learning involves providing a language model with a limited number of examples to enable it to perform a specific task effectively. In this context, the model isgiven a few examples of categorized messages to learn how to assign urgency and sentiment to new, unseen emails.
2. Code Functionality:
* Prompt Template Creation:The prompt_test variable defines a template that instructs the model to extract and categorize messages, specifying the desired output format as a JSON string.
* Example Selection:The code randomly selects a subset of examples from a development set (dev_set) to include in the prompt, demonstrating the expected input-output pairs to the model.
* Model Interaction:The function f_test sends the constructed prompt, along with the input message, to the language model for processing.
* Response Handling:The model's response is expected to be a JSON string containing the assigned urgency, sentiment, and categories for the input message.
3. Purpose of the Code:
* Performance Evaluation:By using few-shot learning, the code evaluates how well the language model can generalize from the provided examples to accurately categorize new customer emails. This approach assesses the model's ability to understand and apply the categorization criteria based on minimal training data.
NEW QUESTION # 18
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