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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 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 2
- 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 3
- 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.
Topic 4
- 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.
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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q37-Q42):
NEW QUESTION # 37
What are some components of the training pipeline in SAP AI Core? Note: There are 2 correct answers to this question.
- A. The SAP HANA database for model storage
- B. Input datasets stored in a hyperscaler object store
- C. Executables that define the training process
- D. Automated deployment to Kubernetes clusters
Answer: B,C
Explanation:
The training pipeline in SAP AI Core comprises several key components that facilitate the development and deployment of machine learning models.
1. Input Datasets Stored in a Hyperscaler Object Store:
* Data Storage:Input datasets are often stored in hyperscaler object stores, which provide scalable and secure storage solutions. These datasets serve as the foundational data for training machine learning models.
* Integration:SAP AI Core integrates with various hyperscaler object stores, allowing seamless access to training data during the model development process.
NEW QUESTION # 38
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 management
- B. Prompt Editor
- C. Administration
- D. Chat
Answer: A
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 # 39
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.
- A. Provide the storage secret to access the dataset.
- B. Grant access rights to the SAP BTP cockpit.
- C. Store the dataset in the SAP HANA Vector Engine.
- D. Store the dataset in a hyperscaler object store.
Answer: A,D
NEW QUESTION # 40
What is a Large Language Model (LLM)?
- A. A database system optimized for storing large volumes of textual data.
- B. An Al model that specializes in processing, understanding, and generating human language.
- C. A rule-based expert system to analyze and generate grammatically correct sentences.
- D. A gradient boosted decision tree algorithm for predicting text.
Answer: B
Explanation:
A Large Language Model (LLM) is an advanced AI model designed to handle various natural language processing tasks.
1. Definition and Purpose:
* Processing:LLMs analyze human language to understand syntax, semantics, and context.
* Understanding:They interpret the meaning behind text, enabling comprehension of nuanced language elements.
* Generating:LLMs can produce coherent and contextually appropriate text, facilitating tasks like content creation and translation.
2. Characteristics of LLMs:
* Scale:These models are trained on vast datasets, encompassing billions of words, which enhances their language capabilities.
* Architecture:LLMs typically utilize complex neural network architectures, such as transformers, to manage and process language data effectively.
3. Applications:
* Content Generation:Creating articles, summaries, and reports.
* Language Translation:Converting text from one language to another with high accuracy.
* Conversational Agents:Powering chatbots and virtual assistants to interact with users naturally.
NEW QUESTION # 41
What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.
- A. To sanitize model outputs
- B. To quickly create iterations on a new use case
- C. To customize outputs for specific types of inputs
- D. To introduce new knowledge to a model in a resource-efficient way
Answer: C,D
NEW QUESTION # 42
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