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[Hardware] C_AIG_2412 Latest Test Cram & Latest C_AIG_2412 Cram Materials

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【Hardware】 C_AIG_2412 Latest Test Cram & Latest C_AIG_2412 Cram Materials

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SAP C_AIG_2412 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the practical use of large language models in SAP environments. Candidates are expected to understand how LLMs can be applied to automate tasks, enhance decision-making, and improve user interaction within SAP systems. The exam evaluates knowledge of handling model selection, fine-tuning, and adapting LLMs to specific business cases.
Topic 2
  • Advanced AI Techniques with SAP’s Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers advanced techniques available through SAP’s Generative AI Hub. Candidates are assessed on their ability to design, optimize, and scale generative AI solutions that go beyond basic implementations. The focus includes applying sophisticated strategies to integrate advanced models, manage performance, and align AI-driven outcomes with complex enterprise goals.
Topic 3
  • SAP AI Core: This section of the exam measures the skills of AI Developers and covers the fundamental components of SAP AI Core. Candidates are assessed on their ability to work with the core services that allow machine learning models to be deployed and managed within SAP environments. The focus is on understanding how AI Core fits into SAP’s ecosystem and ensures smooth integration with enterprise applications.
Topic 4
  • SAP's Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers SAP’s Generative AI Hub, which acts as the central layer for designing and managing generative AI solutions. The exam tests knowledge of building, deploying, and connecting AI models to business scenarios through the Hub. Emphasis is placed on leveraging the Hub to streamline workflows and ensure scalable solutions that align with organizational needs.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q36-Q41):NEW QUESTION # 36
Which of the following is a benefit of using Retrieval Augmented Generation?
  • A. It eliminates the need for fine-tuning LLMs for specific tasks.
  • B. It reduces the computational resources required for language modeling.
  • C. It allows LLMs to access and utilize information beyond their initial training data.
  • D. It enables LLMs to learn new languages without additional training.
Answer: C
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 # 37
What is Machine Learning (ML)?
  • A. A statistical method for data processing that does not involve any Al techniques.
  • B. A subset of Al that focuses on enabling computer systems to learn and improve from experience or data.
  • C. A form of Al that only focuses on creating new content, including text, images, sound, and videos.
  • D. A technology that equips machines with human-like capabilities such as problem-solving, visual perception, and decision-making.
Answer: B
Explanation:
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that empowers computer systems to learn from data and experiences, enhancing their performance over time without explicit programming for each task.
1. Definition and Core Concept:
* Learning from Data:ML algorithms process and analyze large datasets to identify patterns and make informed decisions or predictions based on new, unseen data.
* Improvement Over Time:Through iterative processes, ML models refine their accuracy and efficiency as they are exposed to more data, leading to continuous performance enhancement.
2. Types of Machine Learning:
* Supervised Learning:Models are trained on labeled datasets, where the desired output is known, to make predictions or classifications.
* Unsupervised Learning:Models work with unlabeled data to identify inherent structures or patterns without predefined outcomes.
* Reinforcement Learning:Systems learn by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting actions accordingly.
3. Applications in SAP's AI Solutions:
* SAP AI Core and AI Launchpad:SAP provides a unified framework for managing and deploying ML models, facilitating seamless integration into business processes.
* Generative AI Hub:This platform offers access to a variety of large language models (LLMs) and supports the orchestration of AI tasks, enabling the development of AI-driven applications.

NEW QUESTION # 38
You want to use the orchestration service through SAP's generative-Al-hub-sdk. What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM 11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
  • A. Define the Template and Default Input Values
  • B. Create the Orchestration Configuration
  • C. Define the LLM
  • D. Run the Orchestration Request
Answer: C
Explanation:
The provided code snippet defines a Large Language Model (LLM) within the SAP Generative AI Hub SDK's orchestration service:
from gen_ai_hub.orchestration.models.llm import LLM
llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
1. Importing the LLM Class:
* Code:from gen_ai_hub.orchestration.models.llm import LLM
* Purpose:Imports the LLM class from the SDK, enabling the creation of an LLM instance.
2. Defining the LLM Instance:
* Code:llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature":
0.2})
* Parameters:
* name:Specifies the model's name, in this case, "gpt-40".
* version:Indicates the model version, set to "latest" to use the most recent version.
* parameters:A dictionary defining model-specific parameters:
* max_tokens:Sets the maximum number of tokens (words or word pieces) the model can generate, here limited to 256 tokens.
* temperature:Controls the randomness of the output; a lower value like 0.2 results in more deterministic responses.
3. Role in Orchestration Pipeline:
* Function:This definition is a crucial step in the orchestration pipeline, specifying which LLM to use and configuring its behavior for subsequent tasks.
Conclusion:
The code snippet defines an LLM named "gpt-40" with specific parameters, preparing it for integration into an AI-driven workflow within SAP's Generative AI Hub.

NEW QUESTION # 39
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. One-shot prompting
  • B. Chain-of-thought prompting
  • C. Meta prompting
  • D. Few-shot prompting
Answer: C

NEW QUESTION # 40
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note: There are 2 correct answers to this question.
  • A. Faithfulness
  • B. Relevance
  • C. Carbon footprint
  • D. Speed
Answer: A,B

NEW QUESTION # 41
......
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