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SAP C-AIG-2412 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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 (Q42-Q47):

NEW QUESTION # 42
Which of the following describes Large Language Models (LLMs)?

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

Answer: C

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 # 43
How can few-shot learning enhance LLM performance?

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

Answer: D

NEW QUESTION # 44
What are some features of Joule?
Note: There are 3 correct answers to this question.

A. Streamlining tasks with an Al assistant that knows your unique role.
B. Providing coding assistance and content generation.
C. Generating standalone applications.
D. Maintaining data privacy while offering generative Al capabilities.
E. Downloading and processing data.

Answer: A,B,D

Explanation:
B: Providing coding assistance and content generation:
* Coding:Joule can help developers write code faster and with fewer errors. Imagine you need to create a simple report in ABAP (SAP's programming language). Instead of remembering the exact syntax and functions, you could describe what you need to Joule in plain English. It could then generate the code snippet, saving you time and reducing the chance of mistakes. This applies to other coding languages too, not just those within the SAP ecosystem.
* Content generation:Joule can create different kinds of content, such as:
* Emails:Need to send a quick update to your team? Tell Joule what information to include, and it can draft the email for you.
* Reports:Joule can analyze data and generate summaries or reports based on your requirements.
* Presentations:Need to create a slide deck? Joule can help you structure it and even suggest relevant content.
* Translations:Joule can translate text between multiple languages, making it easier to collaborate with colleagues around the world.
C: Maintaining data privacy while offering generative AI capabilities:
* Data security is paramount:SAP understands that businesses deal with sensitive data. Joule is built with strong security measures to protect this information. This includes things like encryption and access controls to ensure that only authorized users can see sensitive data.
* Privacy-preserving AI:Joule uses techniques like differential privacy to ensure that AI models don't inadvertently reveal private information while still providing valuable insights. This means that even if Joule learns from your company's data, it won't be possible to reconstruct that data or identify individuals from the AI's output.
D: Streamlining tasks with an AI assistant that knows your unique role:
* Personalized experience:Joule learns about your job title, department, and the tasks you typically perform. This allows it to provide more relevant and helpful suggestions.
* Contextual awareness:Joule understands the context of your work. For example, if you're a financial analyst, Joule will prioritize providing assistance related to finance tasks and data.
* Proactive help:Joule doesn't just wait for you to ask questions. It can anticipate your needs and proactively offer help. For instance, if you're working on a sales forecast, Joule might suggest relevant data sources or provide insights from previous forecasts.
In essence, Joule aims to be a powerful AI assistant that makes your work life easier and more efficient while keeping your data safe and respecting your privacy.

NEW QUESTION # 45
What is a part of LLM context optimization?

A. Enhancing the computational speed of the model
B. Adjusting the model's output format and style
C. Reducing the model's size to improve efficiency
D. Providing the model with domain-specific knowledge needed to solve a problem

Answer: D

Explanation:
LLM context optimization involves tailoring a Large Language Model's (LLM) input context to enhance its performance on specific tasks, particularly by incorporating domain-specific knowledge.
1. Understanding LLM Context Optimization:
* Definition:Context optimization refers to the process of adjusting the input provided to an LLM to ensure it includes relevant information, thereby enabling the model to generate more accurate and contextually appropriate outputs.
* Domain-Specific Knowledge Integration:By embedding domain-specific information into the model's context, the LLM can better understand and address specialized queries, leading to improved problem- solving capabilities.
2. Importance of Domain-Specific Knowledge:
* Enhanced Relevance:Providing domain-specific context ensures that the model'sresponses are pertinent to the particular field or subject matter, increasing the utility of the generated content.
* Improved Accuracy:With access to specialized knowledge, the LLLM is less likely to produce generic or incorrect answers, thereby enhancing the overall quality of its outputs.
3. Methods of Context Optimization:
* Prompt Engineering:Crafting prompts that include necessary domain-specific information to guide the model towards generating desired responses.
* Retrieval-Augmented Generation (RAG):Incorporating external data sources into the model's context to provide up-to-date and relevant information pertinent to the domain.

NEW QUESTION # 46
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

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

Answer: A,D

Explanation:
* C. To interpret human instructions and control software systems without necessarily producing output for human consumption.This is a key area where generative AI is breaking new ground. Think of it as AI acting as a "middleman" between you and software. Here are some examples:
* Automating complex tasks:You could tell the AI to "optimize this database for performance" or
"find and fix security vulnerabilities in this code." The AI would then interact with the software systems to carry out these instructions, without needing to show you every step or result.
* Controlling robots or IoT devices:Imagine instructing an AI to "adjust the lighting in the meeting room" or "have the robot retrieve the package from the warehouse." The AI translates your instructions into actions for those systems.
* Managing cloud resources:AI could dynamically allocate cloud resources based on your needs, scaling them up or down without your direct intervention.
* D. To interpret human instructions and control software systems always producing output for human consumption.This is more in line with traditional chatbot interactions, but with a broader scope. It's about AI generating outputs that are directly useful or informative for humans. Examples include:
* Creating realistic images or videos:Based on your description, the AI could generate a photorealistic image of a new product design or a short video clip for a marketing campaign.
* Writing different kinds of creative text formats:AI can generate stories, poems,articles, summaries, and even code, all tailored to your specifications.
* Providing personalized recommendations:AI can analyze your preferences and provide recommendations for products, services, or information.
Why the other options are incorrect:
* A. To produce outputs based on software input.This is a general capability of AI, not something specific to generative AI or beyond chatbots. Many AI systems analyze software input (like sensor data or log files) to produce outputs.
* B. To follow a specific schema - human input, AI processing, and output for human consumption.
This describes the basic interaction pattern of many AI systems, including chatbots. It's not something that specifically differentiates generative AI or goes beyond typical chatbot applications.

NEW QUESTION # 47
......

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