EXAM C_AIG_2412 CRAM - C_AIG_2412 REAL TESTING ENVIRONMENT

Exam C_AIG_2412 Cram - C_AIG_2412 Real Testing Environment

Exam C_AIG_2412 Cram - C_AIG_2412 Real Testing Environment

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SAP C_AIG_2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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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Exam C_AIG_2412 Cram - SAP SAP Certified Associate - SAP Generative AI Developer - High Pass-Rate C_AIG_2412 Real Testing Environment

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.

  • A. Create chat responses and embeddings
  • B. Configure SAP BTP credentials
  • C. Customize SAP AI Launchpad
  • D. Interact with LLMs

Answer: A,D


NEW QUESTION # 64
You want to extract useful information from customer emails to augment existing applications in your company.
How can you use generative-ai-hub-sdk in this context?

  • A. Train custom models based on the mail data.
  • B. Generate random email content and send them to customers.
  • C. Generate a new SAP application based on the mail data.
  • D. Generate JSON strings based on extracted information.

Answer: D

Explanation:
The generative-ai-hub-sdk in SAP's Generative AI Hub enables developers to interact with large language models (LLMs) for various tasks, including information extraction and data formatting.
1. Extracting Information from Customer Emails:
* Natural Language Processing (NLP):By leveraging LLMs, the SDK can process unstructured email content to identify and extract pertinent information, such as customer inquiries, sentiments, or intents.
2. Generating JSON Strings:
* Structured Data Output:After extracting the necessary information, the SDK can format the data into JSON strings. This structured format is essential for integrating the extracted information into existing applications, facilitating seamless data exchange and processing.
3. Integration into Existing Applications:
* Application Enhancement:The JSON-formatted data can be utilized to augment existing applications, such as customer relationship management (CRM) systems, by providing insights derived from customer emails, thereby improving decision-making and customerinteractions.


NEW QUESTION # 65
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?

  • A. Chain-of-thought prompting
  • B. Few-shot prompting
  • C. One-shot prompting
  • D. Meta prompting

Answer: D


NEW QUESTION # 66
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. Combine multiple complex tasks into a single prompt.
  • D. Keep prompts as short as possible to avoid confusion.

Answer: A

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 # 67
Why is generative Al gaining significant attention and investment in the current business landscape? Note: There are 2 correct answers to this question.

  • A. It lowers barriers to adoption.
  • B. It only requires natural language skills to use.
  • C. It can replicate complex technical skills without training or quality control.
  • D. It can run entire business operations without human intervention.

Answer: A,B


NEW QUESTION # 68
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