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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q43-Q48):
NEW QUESTION # 43
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 always producing output for human consumption.
- B. To interpret human instructions and control software systems without necessarily producing output for human consumption.
- C. To follow a specific schema - human input, Al processing, and output for human consumption.
- D. To produce outputs based on software input.
Answer: A,B
NEW QUESTION # 44
You want to use the orchestration service through SAP's generative-Al-hub-sdk.
What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM
11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
- A. Run the Orchestration Request
- B. Create the Orchestration Configuration
- C. Define the LLM
- D. Define the Template and Default Input Values
Answer: C
NEW QUESTION # 45
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 always producing output for human consumption.
- B. To interpret human instructions and control software systems without necessarily producing output for human consumption.
- C. To follow a specific schema - human input, Al processing, and output for human consumption.
- D. To produce outputs based on software input.
Answer: A,B
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 # 46
Which technique is used to supply domain-specific knowledge to an LLM?
- A. Fine-tuning the model on general data
- B. Retrieval-Augmented Generation
- C. Prompt template expansion
- D. Domain-adaptation training
Answer: B
Explanation:
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by integrating external domain-specific knowledge, enabling more accurate and contextually relevant outputs.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Application in Supplying Domain-Specific Knowledge:
* Domain Adaptation:By leveraging RAG, LLMs can access specialized information without the need for extensive retraining or fine-tuning. This approach is particularly beneficial for domains with rapidly evolving information or where incorporating proprietary data is essential.
* Efficiency:RAG enables models to provide informed responses by referencing external data, reducing the necessity for large-scale domain-specific training datasets and thereby conserving computational resources.
3. Advantages of Using RAG:
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Enhanced Accuracy:Incorporating external knowledge allows the model to produce more precise and contextually relevant outputs, especially in specialized domains.
NEW QUESTION # 47
What are some SAP recommendations to evaluate pricing and rate information of model usage within SAP's generative Al hub?
Note: There are 2 correct answers to this question.
- A. Adopt best practice pricing strategies, such as outcome-based pricing
- B. Avoid subscription-based pricing models
- C. Weigh the cost of using advanced models against the expected return on investment
- D. Use pricing models that have fixed rates irrespective of the usage patterns
Answer: A,C
NEW QUESTION # 48
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