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NEW QUESTION # 42
Which statement best describes the Chain-of-Thought (COT) prompting technique?
Answer: D
Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.
NEW QUESTION # 43
Where can you configure language models in generative Al hub?
Answer: B
Explanation:
In SAP's Generative AI Hub, configuring language models is a crucial step for integrating AI capabilities into applications.
1. Accessing the Configuration Interface:
* ML Operations Section:Within SAP AI Launchpad, navigate to theML Operationssection to manage machine learning models and operations.
* Configuration Tab:In theConfigurationtab, you can set up and manage various aspects of language models, including selecting model providers, specifying model versions, and configuring deployment settings.
2. Steps to Configure Language Models:
* Model Selection:Choose the desired language model from the available options, such as GPT-4 or other large language models (LLMs) provided through the Generative AI Hub.
* Parameter Configuration:Set parameters like model version, input constraints, and performance settings to tailor the model's behavior to your application's requirements.
* Deployment Setup:Configure deployment details to ensure the model is accessible andintegrated appropriately within your application's infrastructure.
NEW QUESTION # 44
How can Joule improve workforce productivity? Note: There are 2 correct answers to this question.
Answer: A,B
NEW QUESTION # 45
Which of the following is a principle of effective prompt engineering?
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 # 46
What is Machine Learning (ML)?
Answer: B
Explanation:
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that empowers computer systems to learn from data and experiences, enhancing their performance over time without explicit programming for each task.
1. Definition and Core Concept:
* Learning from Data:ML algorithms process and analyze large datasets to identify patterns and make informed decisions or predictions based on new, unseen data.
* Improvement Over Time:Through iterative processes, ML models refine their accuracy and efficiency as they are exposed to more data, leading to continuous performance enhancement.
2. Types of Machine Learning:
* Supervised Learning:Models are trained on labeled datasets, where the desired output is known, to make predictions or classifications.
* Unsupervised Learning:Models work with unlabeled data to identify inherent structures or patterns without predefined outcomes.
* Reinforcement Learning:Systems learn by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting actions accordingly.
3. Applications in SAP's AI Solutions:
* SAP AI Core and AI Launchpad:SAP provides a unified framework for managing and deploying ML models, facilitating seamless integration into business processes.
* Generative AI Hub:This platform offers access to a variety of large language models (LLMs) and supports the orchestration of AI tasks, enabling the development of AI-driven applications.
NEW QUESTION # 47
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