MINIMUM SAP C-AIG-2412 PASS SCORE - LATEST C-AIG-2412 EXAM QUESTION

Minimum SAP C-AIG-2412 Pass Score - Latest C-AIG-2412 Exam Question

Minimum SAP C-AIG-2412 Pass Score - Latest C-AIG-2412 Exam Question

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Minimum C-AIG-2412 Pass Score & Free PDF SAP SAP Certified Associate - SAP Generative AI Developer Realistic Latest Exam Question

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 2
  • Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 3
  • SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.
Topic 4
  • SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?

  • A. Few-shot prompting
  • B. Chain-of-thought prompting
  • C. Meta prompting
  • D. One-shot prompting

Answer: C

Explanation:
Meta prompting is a technique in prompt engineering where a prompt is designed to generate or refine subsequent prompts.
1. Definition and Purpose:
* Streamlining Prompt Development:Meta prompting automates the creation of effective prompts by utilizing AI to generate or enhance them, thereby streamlining the prompt development process.
* Guiding AI Model Responses:By generating refined prompts, meta prompting effectively guides AI models to produce more accurate and contextually relevant responses.
2. Application in SAP's Generative AI Hub:
* Prompt Engineering Tools:SAP's Generative AI Hub provides tools that support advanced prompt engineering techniques, including meta prompting, to enhance AI model interactions.


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 only requires natural language skills to use.
  • B. It can run entire business operations without human intervention.
  • C. It can replicate complex technical skills without training or quality control.
  • D. It lowers barriers to adoption.

Answer: A,D


NEW QUESTION # 64
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. Adopt best practice pricing strategies, such as outcome-based pricing
  • C. Weigh the cost of using advanced models against the expected return on investment
  • D. Use pricing models that have fixed rates irrespective of the usage patterns

Answer: B,C


NEW QUESTION # 65
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

  • A. The embedding model stores the generated ans wers for future reference.
  • B. The LLM directly ans wers the user's question without accessing external information.
  • C. The user's questi on is used to search a knowledge base or a set of documents.
  • D. The retrieved content is combined with the LLM's capabilities to generate a response.

Answer: C,D

Explanation:
Retrieval-Augmented Generation (RAG) is a process that enhances the capabilities of Large Language Models (LLMs) by integrating external knowledge sources into the response generation process.
1. Understanding the RAG Process:
* User Query:The process begins with a user's question or prompt, which serves as the input for the system.
* Retrieval Step:The system uses the user's query to search a knowledge base or a set of documents, retrieving relevant information that can inform the response.
* Integration with LLM:The retrieved content is then combined with the LLM's inherent knowledge and language generation capabilities to produce a comprehensive and contextually relevant response.
2. Benefits of the RAG Process:
* Enhanced Accuracy:By incorporating up-to-date and domain-specific information from external sources, RAG improves the accuracy of AI-generated responses.
* Contextual Relevance:The integration of retrieved data ensures that the responses are more aligned with the specific context of the user's query.
3. Application in SAP's Generative AI Hub:
* Generative AI Hub SDK:SAP provides a Generative AI Hub SDK that facilitates the implementation of RAG by enabling seamless integration of retrieval mechanisms with LLMs.
* Tutorials and Resources:SAP offers tutorials, such as "Retrieval Augmented Generation using generative-ai-hub-sdk and HANA vector search," to guide developers in implementing RAG systems effectively.


NEW QUESTION # 66
What can be done once the training of a machine learning model has been completed in SAP AICore? Note:
There are 2 correct answers to this question.

  • A. The model's accuracy can be optimized directly in SAP HANA.
  • B. The model can be deployed for inferencing.
  • C. The model can be registered in the hyperscaler object store.
  • D. The model can be deployed in SAP HANA.

Answer: B,C

Explanation:
Once the training of a machine learning model has been completed in SAP AI Core, several post-training actions can be undertaken to operationalize and manage the model effectively.
1. Deploying the Model for Inferencing:
* Deployment Process:After training, the model can be deployed as a service to handle inference requests. This involves setting up a model server that exposes an endpoint for applications to send data and receive predictions.
* Integration:The deployed model can be integrated into business applications, enabling real-time decision-making based on the model's predictions.


NEW QUESTION # 67
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