UPDATED C_AIG_2412 CBT - EXAM C_AIG_2412 PREPARATION

Updated C_AIG_2412 CBT - Exam C_AIG_2412 Preparation

Updated C_AIG_2412 CBT - Exam C_AIG_2412 Preparation

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Tags: Updated C_AIG_2412 CBT, Exam C_AIG_2412 Preparation, Valid C_AIG_2412 Test Materials, C_AIG_2412 Real Exam Questions, C_AIG_2412 Latest Mock Exam

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Actual SAP C_AIG_2412 Exam Questions

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
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
  • 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 4
  • 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q36-Q41):

NEW QUESTION # 36
What are the applications of generative Al that go beyond traditional chatbot applications? Note: There are 2 correct answers to this question.

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

Answer: B,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 # 37
What defines SAP's approach to LLMs?

  • A. Limiting LLM usage to non-business applications only
  • B. Focusing solely on reducing the computational cost of training LLMs
  • C. Prioritizing the development of proprietary LLMs with no integration to existing systems
  • D. Ensuring ethical AI practices and seamless business integration

Answer: D

Explanation:
SAP's approach to Large Language Models (LLMs) is centered on integrating these powerful AI tools into its enterprise ecosystem while adhering to ethical standards. Unlike option A, SAP does not focus solely on proprietary LLMs without integration; instead, it leverages both proprietary and third-party models (e.g., via partnerships with providers like Azure OpenAI) to enhance business applications. Option B is incorrect because reducing computational cost is not the sole focus-SAP prioritizes value delivery through integration with business processes. Option D is also inaccurate, as SAP explicitly targets business applications rather than limiting LLMs to non-business use. Option C is correct because SAP emphasizes ethical AI practices (e.
g., through its AI Ethics Policy) and seamless integration with tools like SAP S/4HANA and SAP SuccessFactors, ensuring LLMs enhance enterprise workflows responsibly and effectively.


NEW QUESTION # 38
What is the primary function of the embedding model in a RAG system?

  • A. To store vector representations of documents and search for relevant passages
  • B. To generate responses based on retrieved documents and user queries
  • C. To evaluate the faithfulness and relevance of generated answers
  • D. To encode queries and documents into vector representations for comparison

Answer: D


NEW QUESTION # 39
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.

  • A. It only supports traditional machine learning models.
  • B. It ensures relevant, reliable, and responsible business Al.
  • C. It provides instant access to a wide range of large language models (LLMs).
  • D. It operates independently of SAP's partners and ecosystem.

Answer: B,C

Explanation:
The SAP Generative AI Hub is designed to integrate generative AI into business processes, offering several key features:
1. Ensuring Relevant, Reliable, and Responsible Business AI:
* Trusted AI Integration:The Generative AI Hub consolidates access to large language models (LLMs) and foundation models, grounding them in business and context data. This integration ensures that AI solutions are pertinent, dependable, and adhere to responsible AI practices.
2. Providing Instant Access to a Wide Range of Large Language Models (LLMs):
* Diverse Model Access:The hub offers immediate access to a broad spectrum of LLMs fromvarious providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b. This variety enables developers to select models that best fit their specific use cases.
3. Integration with SAP AI Core and AI Launchpad:
* Seamless Orchestration:The Generative AI Hub is part of SAP AI Core and AI Launchpad, facilitating the incorporation of generative AI into AI tasks. It streamlines innovation and ensures compliance, benefiting both SAP's internal needs and its broader ecosystem of partners and customers.


NEW QUESTION # 40
Which of the following is a principle of effective prompt engineering?

  • A. Combine multiple complex tasks into a single prompt.
  • B. Keep prompts as short as possible to avoid confusion.
  • C. Use precise language and providing detailed context in prompts.
  • D. Write vague and open-ended instructions to encourage creativity.

Answer: C

Explanation:
Effective prompt engineering is crucial for guiding AI models to produce accurate and relevant outputs.
1. Importance of Precision and Context:
* Clarity:Using precise language in prompts minimizes ambiguity, ensuring the AI model comprehends the exact requirements.
* Detailed Context:Providing comprehensive context helps the model understand the background and nuances of the task, leading to more accurate and tailored responses.
2. Best Practices in Prompt Engineering:
* Specificity:Clearly define the desired outcome, including any constraints or specific formats required.
* Instruction Inclusion:Incorporate explicit instructions within the prompt to guide the model's behavior effectively.
* Avoiding Ambiguity:Steer clear of vague or open-ended language that could lead to varied interpretations.
3. Benefits of Effective Prompt Engineering:
* Enhanced Output Quality:Well-crafted prompts lead to responses that closely align with user expectations.
* Efficiency:Reduces the need for iterative refinements, saving time and computational resources.


NEW QUESTION # 41
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