C_AIG_2412 STUDY MATERIALS REVIEW | VALID C_AIG_2412 EXAM SIMS

C_AIG_2412 Study Materials Review | Valid C_AIG_2412 Exam Sims

C_AIG_2412 Study Materials Review | Valid C_AIG_2412 Exam Sims

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Tags: C_AIG_2412 Study Materials Review, Valid C_AIG_2412 Exam Sims, C_AIG_2412 Exam Actual Questions, Real C_AIG_2412 Braindumps, C_AIG_2412 Prep Guide

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q38-Q43):

NEW QUESTION # 38
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:

Explanation:

Explanation:
A screenshot of a computer Description automatically generated


NEW QUESTION # 39
Which of the following describes Large Language Models (LLMs)?

  • A. They can only process numerical data and are not capable of understanding text
  • B. They generate responses based on pre-defined templates without learning from data
  • C. They utilize deep learning to process and generate human-like text
  • D. They rely on traditional rule-based algorithms to generate responses

Answer: C

Explanation:
Large Language Models (LLMs) are advanced AI systems that leverage deep learning techniques, specifically transformer architectures with self-attention mechanisms, to process and generate human-like text. Option A is incorrect because LLMs do not rely on traditional rule-based systems; they learn patterns from vast datasets. Option C is false as LLMs are designed for text processing, not limited to numerical data. Option D is also inaccurate since LLMs generate responses based on learned patterns, not static templates. Option B is correct, reflecting how LLMs, like those accessible via SAP's Generative AI Hub, use deep learning to understand context, semantics, and generate coherent text for applications such as chatbots, translations, and content creation.


NEW QUESTION # 40
Where can you configure language models in generative Al hub?

  • A. The Configuration tab within ML Operations in SAP AI Launchpad
  • B. The Configuration tab of the SAP BTP cockpit
  • C. The Orchestration tab in SAP AI Launchpad
  • D. The Models tab in Prompt Editor

Answer: A


NEW QUESTION # 41
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. Carbon footprint
  • D. Faithfulness

Answer: B,D

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 # 42
Which of the following executables in generative Al hub works with Anthropic models?

  • A. SAP AI Core
  • B. AWS Bedrock
  • C. GCP Vertex Al
  • D. Azure OpenAl Service

Answer: B

Explanation:
In SAP's Generative AI Hub, the integration with Anthropic models is facilitated through specific executables:
1. AWS Bedrock:
* Integration with Anthropic Models:AWS Bedrock provides access to Anthropic's Claude models, enabling developers to utilize these models within their applications.
* Execution via Generative AI Hub:Through the Generative AI Hub, developers can select AWS Bedrock as the executable to work with Anthropic models, integrating them into their AI solutions.
Conclusion:
To work with Anthropic models within SAP's Generative AI Hub, developers should utilize the AWS Bedrock executable, which provides access to these models for integration into their applications.


NEW QUESTION # 43
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