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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
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'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 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 evaluated.
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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q52-Q57):

NEW QUESTION # 52
Why would a user include formatting instructions within a prompt?

A. To redirect the output to another software program
B. To force the model to separate relevant and irrelevant output
C. To increase the faithfulness of the output
D. To ensure the model's response follows a desired structure or style

Answer: D

Explanation:
Including formatting instructions within a prompt is a technique used in prompt engineering to guide AI models, such as Large Language Models (LLMs), to produce outputs that adhere to a specific structure or style.
1. Purpose of Formatting Instructions in Prompts:
* Structured Outputs:By embedding formatting directives within a prompt, users can instruct the AI model to generate responses in a predetermined format, such as JSON, XML, or tabular data. This is particularly useful when the output needs to be machine-readable or integrated into other applications.
* Consistent Style:Formatting instructions can also dictate the stylistic elements of the response, ensuring consistency in tone, language, or presentation, which is essential for maintaining brand voice or meeting specific communication standards.
2. Implementation in SAP's Generative AI Hub:
* Prompt Management:SAP's Generative AI Hub offers tools for creating and managing prompts, allowing developers to include specific formatting instructions to control the output of AI models effectively.
* Prompt Editor and Management:The hub provides features like prompt editors, enabling users to experiment with different prompts and formatting instructions to achieve optimal results for their specific use cases.
3. Benefits of Using Formatting Instructions:
* Enhanced Usability:Well-formatted outputs are easier to interpret and can be directly utilized in various applications without additional processing.
* Improved Integration:Structured responses facilitate seamless integration with other systems, APIs, or workflows, enhancing overall efficiency.
* Reduced Ambiguity:Clear formatting guidelines minimize the risk of ambiguous outputs, ensuring that the AI model's responses meet user expectations precisely.

NEW QUESTION # 53
How can few-shot learning enhance LLM performance?

A. By enhancing the model's computational efficiency
B. By reducing overfitting through regularization techniques
C. By offering input-output pairs that exemplify the desired behavior
D. By providing a large training set to improve generalization

Answer: C

Explanation:
Few-shot learning enhances the performance of Large Language Models (LLMs) by providing them with a limited number of input-output examples that demonstrate the desired task behavior.
1. Mechanism of Few-Shot Learning:
* Exemplification:By supplying a few examples, the model gains insight into the task requirements, enabling it to generalize from these instances to handle new, unseen inputs effectively.
* Adaptability:This approach allows LLMs to adapt to specific tasks without extensive retraining, making them versatile across various applications.
2. Benefits in Performance Enhancement:
* Improved Accuracy:With clear examples, the model's predictions align more closely with the desired outcomes, reducing errors.
* Efficiency:Few-shot learning minimizes the need for large datasets, accelerating the development process and conserving computational resources.

NEW QUESTION # 54
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note:
There are 2 correct answers to this question.

A. They provide isolation for datasets and Al artifacts.
B. They enable simultaneous orchestration of Kubernetes clusters.
C. They enhance pipeline execution speeds through workload distribution.
D. They ensure workload separation for different tenants or departments.

Answer: A,D

Explanation:
Resource groups in SAP AI Core play a vital role in managing machine learning workloads by offering mechanisms for separation and isolation, which are essential for maintaining efficiency and security.
1. Ensuring Workload Separation for Different Tenants or Departments:
* Multitenancy Support:Resource groups enable the segregation of workloads among various tenants or departments within an organization, ensuring that each unit's processes are isolated and managed independently.
* Operational Efficiency:This separation prevents interference between workloads, allowing for tailored resource allocation and management strategies that meet the specific needs of each tenant or department.

NEW QUESTION # 55
What does SAP recommend you do before you start training a machine learning model in SAP AI Core?
Note: There are 3 correct answers to this question.

A. Register the input dataset in SAP AI Core.
B. Perform manual data integration with SAP HANA.
C. Configure the training pipeline using templates.
D. Configure the model deployment in SAP Al Launchpad.
E. Define the required infrastructure resources for training.

Answer: A,C,E

Explanation:
Before initiating the training of a machine learning model in SAP AI Core, SAP recommends the following steps:
* Configure the training pipeline using templates:Utilize predefined templates to set up the training pipeline, ensuring consistency and efficiency in the training process.
* Define the required infrastructure resources for training:Specify the computational resources, such as CPUs or GPUs, necessary for the training job to ensure optimal performance.
* Register the input dataset in SAP AI Core:Ensure that the dataset intended for training is properly registered within SAP AI Core, facilitating seamless access during the training process.
These preparatory steps are crucial for the successful training of machine learning models within the SAP AI Core environment.

NEW QUESTION # 56
What advantage can you gain by leveraging different models from multiple providers through the SAP's generative Al hub?

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

Answer: B

Explanation:
Leveraging different models from multiple providers through SAP's Generative AI Hub offers significant advantages:
1. Access to a Diverse Range of Large Language Models (LLMs):
* Integration with Multiple Providers:SAP's Generative AI Hub provides instant access to a broad spectrum of LLMs from various providers, such as GPT-4 by Azure OpenAI andopen-source models like Falcon-40b.
2. Enhancing Accuracy and Relevance:
* Model Selection Flexibility:By offering a variety of models, developers can select the most suitable one for their specific use cases, thereby enhancing the accuracy and relevance of AI applications that utilize SAP's data assets.
3. Seamless Orchestration and Integration:
* Orchestration Capabilities:The Generative AI Hub enables the orchestration of multiple models, allowing for seamless integration into SAP solutions like SAP S/4HANA and SAP SuccessFactors.

NEW QUESTION # 57
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C_AIG_2412 FRESH DUMPS | C_AIG_2412 POPULAR EXAMS

C_AIG_2412 Fresh Dumps | C_AIG_2412 Popular Exams