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SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following describes Large Language Models (LLMs)?

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

Answer: A

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 # 41
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
' --- '.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. Generate random examples for language model training
C. Preprocess a dataset for machine learning
D. Train a language model from scratch

Answer: A

NEW QUESTION # 42
You want to download a json output for a prompt and the response.
Which of the following interfaces can you use in SAP's generative Al hub in SAP AI Launchpad?

A. Prompt Editor
B. Chat
C. Prompt management
D. Administration

Answer: C

Explanation:
* Prompt managementis the section within SAP's generative AI hub (accessed via the SAP AI Launchpad) where you work with prompts, including:
* Creating and saving prompts:You can write and store your prompts for later use.
* Viewing prompt history:You can see a record of your past prompts and the AI's responses.
* Exporting prompts and responses:This is where you'd find the option to download your prompt and its corresponding response in JSON format.
Why the other options are incorrect:
* A. Chat:The chat interface is where you have interactive conversations with the generative AI. While you can see your conversation history there, you wouldn't typically find options to download it in JSON format.
* C. Administration:The administration section focuses on managing settings and configurations for the AI hub, not on individual prompts and responses.
* D. Prompt Editor:While a prompt editor might be used to create and refine prompts, it wouldn't necessarily be the place to manage or download them.

NEW QUESTION # 43
What is the goal of prompt engineering?

A. To replace human decision-making with automated processes
B. To develop new neural network architectures for Al models
C. To craft inputs that guide Al systems in generating desired outputs
D. To optimize hardware performance for Al computations

Answer: C

Explanation:
Prompt engineering involves designing and refining inputs, known as prompts, to effectively guide AI systems, particularly Large Language Models (LLMs), in producing desired outputs.
1. Understanding Prompt Engineering:
* Definition:Prompt engineering is the process of creating and optimizing prompts to elicit specific responses from AI models. It serves as a crucial interface between human intentions and machine- generated content.
* Purpose:The primary goal is to communicate the task requirements clearly to the AI model, ensuring that the generated output aligns with user expectations.
2. Importance in AI Systems:
* Guiding AI Behavior:Well-crafted prompts can direct AI models to perform a wide range of tasks, from answering questions to generating creative content, by setting the context and specifying the desired format of the output.
* Enhancing Output Quality:Effective prompt engineering can improve the relevance, coherence, and accuracy of AI-generated responses, making AI systems more useful and reliable in practical applications.
3. Application in SAP's Generative AI Hub:
* Prompt Management:SAP's Generative AI Hub provides tools for prompt management, allowing developers to create, edit, and manage prompts to interact with various AI models efficiently.
* Exploration and Development:The hub offers features like prompt editors and AI playgrounds, enabling users to experiment with different prompts and models to achieve optimal results for their specific use cases.

NEW QUESTION # 44
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

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

Answer: B,D

NEW QUESTION # 45
......

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C-AIG-2412 Exam Vce Format, Braindumps C-AIG-2412 Downloads

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SAP C-AIG-2412 Exam Syllabus Topics:

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q40-Q45):

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