SAP C_AIG_2412 VALID MOCK EXAM - VALID EXAM C_AIG_2412 PRACTICE

SAP C_AIG_2412 Valid Mock Exam - Valid Exam C_AIG_2412 Practice

SAP C_AIG_2412 Valid Mock Exam - Valid Exam C_AIG_2412 Practice

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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'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 3
  • 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 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 (Q46-Q51):

NEW QUESTION # 46
What are some examples of generative Al technologies?
Note: There are 2 correct answers to this question.

  • A. Al models that generate new content based on training data
  • B. Robotic process automation
  • C. Foundation models
  • D. Rule-based algorithms

Answer: A,C


NEW QUESTION # 47
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. Define the LLM
  • C. Create the Orchestration Configuration
  • D. Run the Orchestration Request

Answer: B

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 # 48
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
- "urgency" as one of {{?urgency}}
- "sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
'n---n'.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])

  • A. Evaluate the performance of a language model using few-shot learning
  • B. Preprocess a dataset for machine learning
  • C. Generate random examples for language model training
  • D. Train a language model from scratch

Answer: A


NEW QUESTION # 49
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

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

Answer: A,C


NEW QUESTION # 50
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.

  • A. Integration of foundation models into applications
  • B. Code generation to extend SAP BTP applications
  • C. Proprietary LLMs exclusively
  • D. Tools for prompt engineering and experimentation

Answer: B,D


NEW QUESTION # 51
......

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