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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q38-Q43):
NEW QUESTION # 38
What is the goal of prompt engineering?
- A. To develop new neural network architectures for Al models
- B. To craft inputs that guide Al systems in generating desired outputs
- C. To optimize hardware performance for Al computations
- D. To replace human decision-making with automated processes
Answer: B
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 # 39
You want to extract useful information from customer emails to augment existing applications in your company.
How can you use generative-ai-hub-sdk in this context?
- A. Train custom models based on the mail data.
- B. Generate random email content and send them to customers.
- C. Generate a new SAP application based on the mail data.
- D. Generate JSON strings based on extracted information.
Answer: D
Explanation:
The generative-ai-hub-sdk in SAP's Generative AI Hub enables developers to interact with large language models (LLMs) for various tasks, including information extraction and data formatting.
1. Extracting Information from Customer Emails:
* Natural Language Processing (NLP):By leveraging LLMs, the SDK can process unstructured email content to identify and extract pertinent information, such as customer inquiries, sentiments, or intents.
2. Generating JSON Strings:
* Structured Data Output:After extracting the necessary information, the SDK can format the data into JSON strings. This structured format is essential for integrating the extracted information into existing applications, facilitating seamless data exchange and processing.
3. Integration into Existing Applications:
* Application Enhancement:The JSON-formatted data can be utilized to augment existing applications, such as customer relationship management (CRM) systems, by providing insights derived from customer emails, thereby improving decision-making and customerinteractions.
NEW QUESTION # 40
What are some SAP recommendations to evaluate pricing and rate information of model usage within SAP's generative Al hub?
Note: There are 2 correct answers to this question.
- A. Adopt best practice pricing strategies, such as outcome-based pricing
- B. Use pricing models that have fixed rates irrespective of the usage patterns
- C. Weigh the cost of using advanced models against the expected return on investment
- D. Avoid subscription-based pricing models
Answer: A,C
Explanation:
When evaluating pricing and rate information for model usage within SAP's Generative AI Hub, SAP recommends:
1. Adopting Best Practice Pricing Strategies:
* Outcome-Based Pricing:Implementing pricing strategies that align costs with achieved outcomes ensures that expenditures are directly tied to the value derived from AI models.
2. Assessing Cost Against Expected Return on Investment (ROI):
* Cost-Benefit Analysis:Carefully evaluating the expenses associated with advanced models in relation to the anticipated ROI helps in making informed decisions about model selection and usage.
Conclusion:
By adopting best practice pricing strategies and assessing costs against expected ROI, businesses can make informed decisions regarding model usage within SAP's Generative AI Hub, ensuring cost-effectiveness and value alignment.
NEW QUESTION # 41
How does SAP deal with vulnerability risks created by generative Al? Note: There are 2 correct answers to this question.
- A. By relying on external vendors to manage security threats.
- B. By identifying human, technical, and exfiltration risks through an Al Security Taskforce.
- C. By implementing responsible Al use guidelines and strong product security standards.
- D. By focusing on technological advancement only.
Answer: B,C
NEW QUESTION # 42
What is one primary benefit of using LLMs in business applications?
- A. They enhance automation and scalability of processes
- B. They replace the need for human decision-making entirely
- C. They require no maintenance or updates once implemented
- D. They eliminate all data privacy concerns in business operations
Answer: A
Explanation:
The primary benefit of using LLMs in business applications is their ability to enhance automation and scalability, making processes more efficient and adaptable to large-scale needs. Option A is incorrect because LLMs augment, rather than fully replace, human decision-making-human oversight remains critical. Option B is false as LLMs do not inherently eliminate privacy concerns; data privacy must still be managed (e.g., through SAP's privacy-preserving techniques like differential privacy). Option C is inaccurate since LLMs require ongoing maintenance, updates, and monitoring to remain effective. Option D is correct because LLMs automate tasks like document processing, content generation, and customer interaction, while their scalability allows businesses to handle increasing data volumes and user demands efficiently, as seen in SAP's integration with tools like Joule and SAP Business AI.
NEW QUESTION # 43
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