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[General] Valid Test C_AIG_2412 Experience | C_AIG_2412 New Braindumps Sheet

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【General】 Valid Test C_AIG_2412 Experience | C_AIG_2412 New Braindumps Sheet

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q58-Q63):NEW QUESTION # 58
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.
  • A. Tools for prompt engineering and experimentation
  • B. Code generation to extend SAP BTP applications
  • C. Proprietary LLMs exclusively
  • D. Integration of foundation models into applications
Answer: A,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 SDKsroviding 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 # 59
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. User scripts to manually execute pipeline steps
  • B. Infrastructure resources such as CPUs or GPUs
  • C. Pipeline containers to be used
  • D. Deployment templates for SAP AI Launchpad
Answer: B,C

NEW QUESTION # 60
What is a significant risk associated with using LLMs?
  • A. Inability to generate text in multiple languages
  • B. Potential biases in generated content
  • C. Complete elimination of human oversight in content creation
  • D. Unlimited processing power usage without cost control
Answer: B
Explanation:
A significant risk of using LLMs is the potential for biases in generated content, stemming from biases present in their training data. Option A is incorrect because LLMs do not inherently eliminate human oversight; oversight is often maintained, especially in enterprise settings like SAP's. Option B is false as LLMs can generate text in multiple languages, as seen with models like GPT-4. Option D, while a concern in terms of resource management, is not the most significant risk compared to bias, and cost control can be implemented. Option C is correct because biased outputs can lead to unfair decisions or misinformation, a risk SAP mitigates through its AI Ethics framework, which includes principles like avoiding bias and discrimination, ensuring responsible AI deployment.

NEW QUESTION # 61
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 # 62
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. Relevance
  • C. Faithfulness
  • D. Carbon footprint
Answer: B,C
Explanation:
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.

NEW QUESTION # 63
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