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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q61-Q66):
NEW QUESTION # 61
What are some use cases for fine-tuning of a model?
Note: There are 2 correct answers to this question.
- A. To customize outputs for specific types of inputs
- B. To sanitize model outputs
- C. To quickly create iterations on a new use case
- D. To introduce new knowledge to a model in a resource-efficient way
Answer: A,D
NEW QUESTION # 62
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 deployed in SAP HAN
- B. The model can be deployed for inferencing.
- C. The model's accuracy can be optimized directly in SAP HANA.
- D. The model can be registered in the hyperscaler object store.
Answer: B,D
NEW QUESTION # 63
Why is generative Al gaining significant attention and investment in the current business landscape?
Note: There are 2 correct answers to this question.
- A. It can run entire business operations without human intervention.
- B. It only requires natural language skills to use.
- C. It can replicate complex technical skills without training or quality control.
- D. It lowers barriers to adoption.
Answer: B,D
NEW QUESTION # 64
What are some drivers for the rapid adoption of generative AI? Note: There are 2 correct answers to this question.
- A. Availability of skilled developers
- B. Wide availability
- C. Ease of use
- D. Significant hardware cost savings
Answer: B,C
NEW QUESTION # 65
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
- A. Proprietary LLMs exclusively
- B. Code generation to extend SAP BTP applications
- C. Integration of foundation models into applications
- D. Tools for prompt engineering and experimentation
Answer: C,D
Explanation:
* C. Tools for prompt engineering and experimentation:Generative AI hubs often provide tools and resources to help developers refine their prompts. This is crucial because the quality of the output from a generative AI model heavily depends on how well the prompt is crafted. These tools might include:
* Prompt libraries:Collections of effective prompts for various tasks.
* Prompt testing and analysis:Features to test different prompts and analyze the AI's response.
* Guides and tutorials:Resources to learn about prompt engineering best practices.
* D. Integration of foundation models into applications:Generative AI hubs make it easier for developers to integrate powerful foundation models (large language models like those from Google, OpenAI, etc.) into their own applications. This means developers don't have to build these complex models from scratch. Instead, they can leverage existing models and customize them for their specific needs. This might involve:
* APIs and SDKs:Providing easy-to-use interfaces to access and interact with the foundation models.
* Model customization:Tools to fine-tune existing models on specific datasets or for particular tasks.
* Deployment options:Support for deploying AI models in different environments (cloud, on- premises, etc.).
Why the other options are incorrect:
* A. Proprietary LLMs exclusively:While some generative AI hubs might offer their own proprietary models, they usually provide access to a variety of models, including open-source and those from other providers. This gives developers more flexibility and choice.
* B. Code generation to extend SAP BTP applications:While code generation is a common feature of generative AI, it's not the primary focus of a generative AI hub. The hub's main purpose is to provide access to and facilitate the use of foundation models, not to specifically extend SAP BTP applications.
NEW QUESTION # 66
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