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Get Latest SAP C-AIG-2412 Exam Dumps [2025]
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q27-Q32):
NEW QUESTION # 27
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.
- A. Create chat responses and embeddings
- B. Customize SAP AI Launchpad
- C. Configure SAP BTP credentials
- D. Interact with LLMs
Answer: A,D
NEW QUESTION # 28
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 evaluate the faithfulness and relevance of generated Answers
- D. To store vector representations of documents and search for relevant passages
Answer: A
Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.
NEW QUESTION # 29
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.
- A. It ensures relevant, reliable, and responsible business Al.
- B. It provides instant access to a wide range of large language models (LLMs).
- C. It operates independently of SAP's partners and ecosystem.
- D. It only supports traditional machine learning models.
Answer: A,B
NEW QUESTION # 30
Which of the following is a principle of effective prompt engineering?
- A. Keep prompts as short as possible to avoid confusion.
- B. Combine multiple complex tasks into a single prompt.
- C. Write vague and open-ended instructions to encourage creativity.
- D. Use precise language and providing detailed context in prompts.
Answer: D
Explanation:
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.
NEW QUESTION # 31
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. Avoid subscription-based pricing models
- B. Use pricing models that have fixed rates irrespective of the usage patterns
- C. Adopt best practice pricing strategies, such as outcome-based pricing
- D. Weigh the cost of using advanced models against the expected return on investment
Answer: C,D
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 # 32
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