QUIZ 2025 C-AIG-2412: SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER HIGH HIT-RATE LATEST LEARNING MATERIALS

Quiz 2025 C-AIG-2412: SAP Certified Associate - SAP Generative AI Developer High Hit-Rate Latest Learning Materials

Quiz 2025 C-AIG-2412: SAP Certified Associate - SAP Generative AI Developer High Hit-Rate Latest Learning Materials

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
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 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 3
  • 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 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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2025 SAP C-AIG-2412: SAP Certified Associate - SAP Generative AI Developer –High Pass-Rate Latest Learning Materials

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q37-Q42):

NEW QUESTION # 37
What are some examples of generative Al technologies? Note: There are 2 correct answers to this question.

  • A. Al models that generate new content based on training data
  • B. Robotic process automation
  • C. Foundation models
  • D. Rule-based algorithms

Answer: A,C

Explanation:
Generative AI encompasses technologies that create new content by learning from existing data.
1. AI Models That Generate New Content Based on Training Data:
* Definition:These models analyze large datasets to produce original outputs, such as text, images, or music, that resemble the patterns found in the training data.
* Examples:Models like GPT-4 generate human-like text, while DALL E creates images from textual descriptions.
2. Foundation Models:
* Definition:Foundation models are large-scale AI models trained on extensive data across various domains. They serve as a base for fine-tuning on specific tasks, enabling versatility in applications.
* Examples:Models such as BERT and GPT-3 are foundation models that can be adapted for tasks like translation, summarization, or content generation.


NEW QUESTION # 38
How does SAP ensure the enterprise-readiness of its Al solutions?

  • A. By using generic Al models without business context complying with Al ethics standards
  • B. By ensuring that Al models make bias-free decisions without human input
  • C. By implementing rigorous product standards for Al capabilities

Answer: C


NEW QUESTION # 39
Which of the following executables in generative Al hub works with Anthropic models?

  • A. GCP Vertex Al
  • B. Azure OpenAl Service
  • C. AWS Bedrock
  • D. SAP AI Core

Answer: C


NEW QUESTION # 40
What contract type does SAP offer for Al ecosystem partner solutions?

  • A. All-in-one contracts, with services that are contracted through SAP
  • B. Bring Your Own License (BYOL) for embedded partner solutions
  • C. Annual subscription-only contracts
  • D. Pay-as-you-go for each partner service

Answer: A,C,D


NEW QUESTION # 41
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 produce outputs based on software input.
  • C. To follow a specific schema - human input, Al processing, and output for human consumption.
  • 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 # 42
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