SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER UPDATED PDF MATERIAL & C-AIG-2412 EXAM TRAINING VCE & ONLINE TEST ENGINE

SAP Certified Associate - SAP Generative AI Developer updated pdf material & C-AIG-2412 exam training vce & online test engine

SAP Certified Associate - SAP Generative AI Developer updated pdf material & C-AIG-2412 exam training vce & online test engine

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q13-Q18):

NEW QUESTION # 13
What are some features of Joule?
Note: There are 3 correct answers to this question.

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

Answer: B,C,E


NEW QUESTION # 14
Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?

  • A. Create a basic prompt in SAP AI Launchpad
    *Evaluate various models for the problem using generative-ai-hub-sdk
    *Scale the solution using generative-ai-hub-sdk
    *Create a baseline evaluation method for the simple prompt
    *Enhance the prompts.
  • B. Create a basic prompt in SAP AI Launchpad
    *Enhance the prompts
    *Create a baseline evaluation method for the simple prompt
    *Evaluate various models for the problem using generative-ai-hub-sdk
    *Scale the solution using generative-ai-hub-sdk
  • C. Create a basic prompt in SAP AI Launchpad
    *Scale the solution using generative-ai-hub-sdk
    *Create a baseline evaluation method for the simple prompt
    *Enhance the prompts
    *Evaluate various models for the problem using generative-ai-hub-sdk

Answer: B

Explanation:
SAP recommends the following sequence of steps to effectively solve a business problem using the Generative AI Hub:
1. Create a Basic Prompt in SAP AI Launchpad:
* Initiation:Begin by formulating a simple prompt within SAP AI Launchpad to address the business problem. This serves as the foundation for subsequent refinements.
2. Enhance the Prompts:
* Refinement:Iteratively improve the initial prompt to better capture the nuances of the business problem, ensuring clarity and relevance.
3. Create a Baseline Evaluation Method for the Simple Prompt:
* Establish Metrics:Develop an evaluation framework to assess the performance of the prompt, setting a baseline for comparison as enhancements are made.
4. Evaluate Various Models for the Problem Using generative-ai-hub-sdk:
* Model Assessment:Utilize the generative-ai-hub-sdk to test different large language models (LLMs) against the refined prompt, identifying the model that delivers optimal results.
5. Scale the Solution Using generative-ai-hub-sdk:
* Deployment:Once the optimal model and prompt are determined, employ the generative-ai-hub-sdk to scale the solution, integrating it into the business workflow for widespread application.
Conclusion:
Following this structured approach ensures a methodical development and deployment of AI-driven solutions, enhancing their effectiveness in addressing specific business challenges.


NEW QUESTION # 15
How do resource groups in SAP AI Core improve the management of machine learning workloads? Note: There are 2 correct answers to this question.

  • A. They provide isolation for datasets and Al artifacts.
  • B. They ensure workload separation for different tenants or departments.
  • C. They enhance pipeline execution speeds through workload distribution.
  • D. They enable simultaneous orchestration of Kubernetes clusters.

Answer: A,B


NEW QUESTION # 16
Which neural network architecture is primarily used by LLMs?

  • A. Transformer architecture with self-attention mechanisms
  • B. Convolutional Neural Networks (CNNs)
  • C. Recurrent neural network architecture
  • D. Sequential encoder-decoder architecture

Answer: A


NEW QUESTION # 17
What are some advantages of using agents in training models? Note: There are 2 correct answers to this question.

  • A. To guarantee accurate decision making in complex scenarios
  • B. To improve the quality of results
  • C. To streamline LLM workflows
  • D. To eliminate the need for human oversight

Answer: B,C

Explanation:
Incorporating agents into the training and deployment of Large Language Models (LLMs) offers notable advantages:
1. Improving the Quality of Results:
* Specialized Task Handling:Agents can be designed to manage specific tasks or subtasks within a larger process, ensuring that each component is handled with expertise, thereby enhancing the overall quality of the output.
* Error Reduction:By delegating particular functions to specialized agents, the likelihood of errors decreases, leading to more accurate and reliable results.
2. Streamlining LLM Workflows:
* Process Automation:Agents can automate repetitive or time-consuming tasks within the LLM workflow, increasing efficiency and allowing human resources to focus on more complex aspects of model development and deployment.
* Workflow Management:Agents facilitate the coordination of various stages in the LLM pipeline, ensuring seamless transitions between tasks and improving overall workflow efficiency.
3. Enhancing Model Performance:
* Adaptive Learning:Agents can monitor model performance and implement adjustments in real-time, promoting continuous improvement and adaptability to new data or requirements.
* Resource Optimization:By managing specific tasks, agents help in optimizing computational resources, ensuring that the LLM operates efficiently without unnecessary expenditure of processing power.


NEW QUESTION # 18
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