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Quiz SAP - The Best Latest C_AIG_2412 Exam Discount

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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 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 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'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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which of the following steps must be performed to deploy LLMs in the generative Al hub?

A. Provision SAP AI
B. Provision SAP AI Core
C. Run the booster
D. Check for foundation model scenario

Answer: B

NEW QUESTION # 20
Which statement best describes the Chain-of-Thought (COT) prompting technique?

A. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.
B. Writing a series of connected prompts creating a chain of related information.
C. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.
D. Connecting related concepts by having the LLM generate chains of ideas.

Answer: C

NEW QUESTION # 21
Which of the following describes Large Language Models (LLMs)?

A. They can only process numerical data and are not capable of understanding text
B. They rely on traditional rule-based algorithms to generate responses
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 # 22
Which of the following is a principle of effective prompt engineering?

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

Answer: A

NEW QUESTION # 23
What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note: There are 3 correct answers to this question.

A. Providing metrics to quantitatively assess response quality
B. Supporting low code evaluations using graphical user interface
C. Creating custom evaluators that meet specific business needs
D. Automating prompt testing across various scenarios
E. Maintaining data privacy by using data masking techniques

Answer: A,C,D

NEW QUESTION # 24
......

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Sam Reed Sam Reed

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