VALID SAP C_AIG_2412 EXAM NOTES & C_AIG_2412 VALID EXAM DURATION

Valid SAP C_AIG_2412 Exam Notes & C_AIG_2412 Valid Exam Duration

Valid SAP C_AIG_2412 Exam Notes & C_AIG_2412 Valid Exam Duration

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Tags: Valid C_AIG_2412 Exam Notes, C_AIG_2412 Valid Exam Duration, C_AIG_2412 Interactive Practice Exam, Valid C_AIG_2412 Test Dumps, New C_AIG_2412 Study Plan

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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 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 2
  • 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 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
  • 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.

>> Valid SAP C_AIG_2412 Exam Notes <<

C_AIG_2412 Valid Exam Duration | C_AIG_2412 Interactive Practice Exam

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q54-Q59):

NEW QUESTION # 54
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. One-shot prompting
  • D. Meta prompting

Answer: D

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 # 55
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. Run the Orchestration Request
  • C. Define the LLM
  • D. Create the Orchestration Configuration

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 # 56
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. One-shot prompting
  • D. Meta prompting

Answer: D


NEW QUESTION # 57
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

  • A. To interpret human instructions and control software systems without necessarily producing output for human consumption.
  • B. To follow a specific schema - human input, Al processing, and output for human consumption.
  • C. To interpret human instructions and control software systems always producing output for human consumption.
  • D. To produce outputs based on software input.

Answer: A,C


NEW QUESTION # 58
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.

  • A. Store the dataset in the SAP HANA Vector Engine.
  • B. Provide the storage secret to access the dataset.
  • C. Store the dataset in a hyperscaler object store.
  • D. Grant access rights to the SAP BTP cockpit.

Answer: B,C

Explanation:
Before connecting to a dataset for training a machine learning model in SAP AI Core, the following steps are necessary:
* Store the dataset in a hyperscaler object store:Ensure that the dataset is stored in a compatible object storage service provided by a cloud hyperscaler (e.g., AWS S3, Azure Blob Storage) to facilitate seamless access during training.
* Provide the storage secret to access the dataset:Configure the necessary access credentials (storage secrets) within SAP AI Core to securely connect to and retrieve the dataset from the object store.
These steps are essential to establish a secure and efficient connection to the dataset, enabling successful model training within SAP AI Core.


NEW QUESTION # 59
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