PASS GUARANTEED SAP - C_AIG_2412 - AUTHORITATIVE SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER VALID TEST SIMS

Pass Guaranteed SAP - C_AIG_2412 - Authoritative SAP Certified Associate - SAP Generative AI Developer Valid Test Sims

Pass Guaranteed SAP - C_AIG_2412 - Authoritative SAP Certified Associate - SAP Generative AI Developer Valid Test Sims

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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
  • 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.
Topic 3
  • 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 4
  • 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 evaluated.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q46-Q51):

NEW QUESTION # 46
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 # 47
Which of the following are grounding principles included in SAP's AI Ethics framework?
Note: There are 3 correct answers to this question.

  • A. Human agency and oversight
  • B. Store all user data for legal proceedings
  • C. Maximize business profits
  • D. Transparency and explainability
  • E. Avoid bias and discrimination

Answer: A,D,E


NEW QUESTION # 48
Which of the following are grounding principles included in SAP's AI Ethics framework? Note: There are 3 correct answers to this question.

  • A. Human agency and oversight
  • B. Store all user data for legal proceedings
  • C. Maximize business profits
  • D. Transparency and explainability
  • E. Avoid bias and discrimination

Answer: A,D,E


NEW QUESTION # 49
Which of the following is a benefit of using Retrieval Augmented Generation?

  • A. It enables LLMs to learn new languages without additional training.
  • B. It allows LLMs to access and utilize information beyond their initial training data.
  • C. It reduces the computational resources required for language modeling.
  • D. It eliminates the need for fine-tuning LLMs for specific tasks.

Answer: B

Explanation:
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Benefits of RAG:
* Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses.
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.


NEW QUESTION # 50
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.

  • A. They ensure workload separation for different tenants or departments.
  • B. They provide isolation for datasets and Al artifacts.
  • C. They enable simultaneous orchestration of Kubernetes clusters.
  • D. They enhance pipeline execution speeds through workload distribution.

Answer: A,B


NEW QUESTION # 51
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