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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 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

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 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q10-Q15):

NEW QUESTION # 10
What advantage can you gain by leveraging different models from multiple providers through the SAP's generative Al hub?

A. Design new product interfaces for SAP applications
B. Enhance the accuracy and relevance of Al applications that use SAP's data assets
C. Train new models using SAP and non-SAP data
D. Get more training data for new models

Answer: B

NEW QUESTION # 11
Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?

A. Create a basic prompt in SAP AI Launchpad
*Scale the solution using generative-ai-hub-sdk
*Create a baseline evaluation method for the simple prompt
*Enhance the prompts
*Evaluate various models for the problem using generative-ai-hub-sdk
B. Create a basic prompt in SAP AI Launchpad
*Enhance the prompts
*Create a baseline evaluation method for the simple prompt
*Evaluate various models for the problem using generative-ai-hub-sdk
*Scale the solution using generative-ai-hub-sdk
C. Create a basic prompt in SAP AI Launchpad
*Evaluate various models for the problem using generative-ai-hub-sdk
*Scale the solution using generative-ai-hub-sdk
*Create a baseline evaluation method for the simple prompt
*Enhance the prompts.

Answer: B

Explanation:
SAP recommends the following sequence of steps to effectively solve a business problem using the Generative AI Hub:
1. Create a Basic Prompt in SAP AI Launchpad:
* Initiation:Begin by formulating a simple prompt within SAP AI Launchpad to address the business problem. This serves as the foundation for subsequent refinements.
2. Enhance the Prompts:
* Refinement:Iteratively improve the initial prompt to better capture the nuances of the business problem, ensuring clarity and relevance.
3. Create a Baseline Evaluation Method for the Simple Prompt:
* Establish Metrics:Develop an evaluation framework to assess the performance of the prompt, setting a baseline for comparison as enhancements are made.
4. Evaluate Various Models for the Problem Using generative-ai-hub-sdk:
* Model Assessment:Utilize the generative-ai-hub-sdk to test different large language models (LLMs) against the refined prompt, identifying the model that delivers optimal results.
5. Scale the Solution Using generative-ai-hub-sdk:
* Deployment:Once the optimal model and prompt are determined, employ the generative-ai-hub-sdk to scale the solution, integrating it into the business workflow for widespread application.
Conclusion:
Following this structured approach ensures a methodical development and deployment of AI-driven solutions, enhancing their effectiveness in addressing specific business challenges.

NEW QUESTION # 12
What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.

A. To eliminate the need for human oversight
B. To improve the quality of results
C. To streamline LLM workflows
D. To guarantee accurate decision making in complex scenarios

Answer: B,C

Explanation:
Incorporating agents into the training and deployment of Large Language Models (LLMs) offers notable advantages:
1. Improving the Quality of Results:
* Specialized Task Handling:Agents can be designed to manage specific tasks or subtasks within a larger process, ensuring that each component is handled with expertise, thereby enhancing the overall quality of the output.
* Error Reduction:By delegating particular functions to specialized agents, the likelihood of errors decreases, leading to more accurate and reliable results.
2. Streamlining LLM Workflows:
* Process Automation:Agents can automate repetitive or time-consuming tasks within the LLM workflow, increasing efficiency and allowing human resources to focus on more complex aspects of model development and deployment.
* Workflow Management:Agents facilitate the coordination of various stages in the LLM pipeline, ensuring seamless transitions between tasks and improving overall workflow efficiency.
3. Enhancing Model Performance:
* Adaptive Learning:Agents can monitor model performance and implement adjustments in real-time, promoting continuous improvement and adaptability to new data or requirements.
* Resource Optimization:By managing specific tasks, agents help in optimizing computational resources, ensuring that the LLM operates efficiently without unnecessary expenditure of processing power.

NEW QUESTION # 13
Which of the following steps is NOT a requirement to use the Orchestration service?

A. Create a deployment for orchestration
B. Modify the underlying Al models
C. Create an instance of an Al model
D. Get an auth token for orchestration

Answer: B

NEW QUESTION # 14
What is the goal of prompt engineering?

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

Answer: C

NEW QUESTION # 15
......

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