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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q25-Q30):

NEW QUESTION # 25
What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question.

A. Monitoring and retraining models in SAP Al Core
B. Continuous delivery and tenant isolation for scalability
C. Integration of Al services with business applications using a standardized API
D. Management of SAP S/4HANA cloud infrastructure
E. Orchestration of Al workflows such as model training and inference

Answer: B,C,E

Explanation:
You're asking about the key functionalities of SAP AI Core. Here's a breakdown of the correct answers:
* A. Integration of AI services with business applications using a standardized API:SAP AI Core provides a standardized way to connect AI models and services to your existing business applications.
This means you can easily integrate AI capabilities into your core business processes, regardless of the specific AI technology you're using. This is done through APIs (Application Programming Interfaces), which allow different software systems to communicate with each other.
* B. Continuous delivery and tenant isolation for scalability:
* Continuous delivery:AI Core supports continuous delivery, which means you can quickly and easily deploy and update your AI models. This allows you to adapt to changing business needs and keep your AI solutions up-to-date.
* Tenant isolation:AI Core provides tenant isolation, which is important for security and scalability. This means that different users or departments within your organization can have their own separate AI environments, preventing interference and ensuring data privacy.
* C. Orchestration of AI workflows such as model training and inference:AI Core helps you manage the entire lifecycle of your AI models, including:
* Training:Automating the process of training your AI models on large datasets.
* Inference:Deploying your trained models and using them to make predictions or generate insights.
* Monitoring:Tracking the performance of your AI models over time.
Why the other options are incorrect:
* D. Management of SAP S/4HANA cloud infrastructure:While AI Core can be used with S/4HANA, it's not specifically designed to manage the cloud infrastructure of S/4HANA. That's handled by other SAP services.
* E. Monitoring and retraining models in SAP AI Core:While AI Core supports monitoring, the retraining of models is typically done using other tools and services within the SAP AI ecosystem.

NEW QUESTION # 26
What are some functionalities provided by SAP Al Core?
Note: There are 3 correct answers to this question.

A. Monitoring and retraining models in SAP Al Core
B. Continuous delivery and tenant isolation for scalability
C. Integration of Al services with business applications using a standardized API
D. Management of SAP S/4HANA cloud infrastructure
E. Orchestration of Al workflows such as model training and inference

Answer: B,C,E

NEW QUESTION # 27
What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note:
There are 3 correct answers to this question.

A. Creating custom evaluators that meet specific business needs
B. Providing metrics to quantitatively assess response quality
C. Maintaining data privacy by using data masking techniques
D. Automating prompt testing across various scenarios
E. Supporting low code evaluations using graphical user interface

Answer: A,B,D

Explanation:
Utilizing an SDK for evaluating prompts within the context of generative AI offers several benefits:
1. Creating Custom Evaluators That Meet Specific Business Needs:
* Tailored Evaluation Metrics:An SDK allows developers to design and implement custom evaluation metrics that align with specific business objectives, ensuring that prompt assessments are relevant and meaningful.
* Flexibility in Evaluation Criteria:Developers can define criteria that reflect the unique requirements of their applications, leading to more accurate and business-aligned evaluations.
2. Automating Prompt Testing Across Various Scenarios:
* Scalability:An SDK enables the automation of prompt testing across multiple scenarios, facilitating large-scale evaluations without manual intervention.
* Consistency:Automated testing ensures consistent application of evaluation criteria, reducing the potential for human error and increasing reliability.
3. Providing Metrics to Quantitatively Assess Response Quality:
* Objective Assessment:The SDK can generate quantitative metrics, such as accuracy, relevance, and coherence scores, providing an objective basis for evaluating prompt performance.
* Performance Monitoring:These metrics enable continuous monitoring and improvement of prompt quality, ensuring that AI models deliver optimal results.

NEW QUESTION # 28
What are the benefits of SAP's generative Al hub? Note: There are 2 correct answers to this question.

A. Accelerate Al development with flexible access to a broad range of models
B. Provide libraries for no-code development
C. Send your data to various LLM providers for training feedback
D. Build custom Al solutions and extend SAP applications

Answer: A,D

Explanation:
SAP's Generative AI Hub offers several benefits that enhance AI development and integration within business processes:
1. Accelerate AI Development with Flexible Access to a Broad Range of Models:
* Diverse Model Access:The Generative AI Hub provides instant access to a wide array of large language models (LLMs) from various providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b.
* Flexible Integration:This access allows developers to select and utilize the most suitable models for their specific use cases, thereby accelerating AI development and deployment.
2. Build Custom AI Solutions and Extend SAP Applications:
* Custom AI Solutions:The hub offers a comprehensive toolset for building custom AI solutions, including prompt engineering tools, SDKs, and fine-tuning services.
* Extending SAP Applications:Developers can leverage these tools to create AI-powered extensions for SAP applications like SAP S/4HANA and SAP SuccessFactors, enhancing their functionality and adaptability.

NEW QUESTION # 29
Which statement best describes the Chain-of-Thought (COT) prompting technique?

A. Writing a series of connected prompts creating a chain of related information.
B. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.
C. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.
D. Connecting related concepts by having the LLM generate chains of ideas.

Answer: B

Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.

NEW QUESTION # 30
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C_AIG_2412 TEST FREE, C_AIG_2412 VALID TEST VCE

C_AIG_2412 Test Free, C_AIG_2412 Valid Test Vce