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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which of the following executables in generative Al hub works with Anthropic models?

A. GCP Vertex Al
B. AWS Bedrock
C. Azure OpenAl Service
D. SAP AI Core

Answer: B

Explanation:
In SAP's Generative AI Hub, the integration with Anthropic models is facilitated through specific executables:
1. AWS Bedrock:
* Integration with Anthropic Models:AWS Bedrock provides access to Anthropic's Claude models, enabling developers to utilize these models within their applications.
* Execution via Generative AI Hub:Through the Generative AI Hub, developers can select AWS Bedrock as the executable to work with Anthropic models, integrating them into their AI solutions.
Conclusion:
To work with Anthropic models within SAP's Generative AI Hub, developers should utilize the AWS Bedrock executable, which provides access to these models for integration into their applications.

NEW QUESTION # 29
What is one primary benefit of using LLMs in business applications?

A. They require no maintenance or updates once implemented
B. They enhance automation and scalability of processes
C. They eliminate all data privacy concerns in business operations
D. They replace the need for human decision-making entirely

Answer: B

Explanation:
The primary benefit of using LLMs in business applications is their ability to enhance automation and scalability, making processes more efficient and adaptable to large-scale needs. Option A is incorrect because LLMs augment, rather than fully replace, human decision-making-human oversight remains critical. Option B is false as LLMs do not inherently eliminate privacy concerns; data privacy must still be managed (e.g., through SAP's privacy-preserving techniques like differential privacy). Option C is inaccurate since LLMs require ongoing maintenance, updates, and monitoring to remain effective. Option D is correct because LLMs automate tasks like document processing, content generation, and customer interaction, while their scalability allows businesses to handle increasing data volumes and user demands efficiently, as seen in SAP's integration with tools like Joule and SAP Business AI.

NEW QUESTION # 30
Match the components of a Retrieval Augmented Generation architecture to the diagram.

Answer:

Explanation:

Explanation:
A screenshot of a computer Description automatically generated

NEW QUESTION # 31
You want to assign urgency and sentiment categories to a large number of customer emails. You want to get a valid json string output for creating custom applications. You decide to develop a prompt for the same using generative Al hub.
What is the main purpose of the following code in this context?
prompt_test = """Your task is to extract and categorize messages. Here are some examples:
{{?technique_examples}}
Use the examples when extract and categorize the following message:
{{?input}}
Extract and return a json with the following keys and values:
-"urgency" as one of {{?urgency}}
-"sentiment" as one of {{?sentiment}}
"categories" list of the best matching support category tags from: {{?categories}} Your complete message should be a valid json string that can be read directly and only contains the keys mentioned in t import random random.seed(42) k = 3 examples random. sample (dev_set, k) example_template = """<example> {example_input} examples
' --- '.join([example_template.format(example_input=example ["message"], example_output=json.dumps (example[ f_test = partial (send_request, prompt=prompt_test, technique_examples examples, **option_lists) response = f_test(input=mail["message"])

A. Train a language model from scratch
B. Evaluate the performance of a language model using few-shot learning
C. Generate random examples for language model training
D. Preprocess a dataset for machine learning

Answer: B

Explanation:
The provided code is designed to evaluate the performance of a language model in assigning urgency and sentiment categories to customer emails by utilizing few-shot learning within SAP's Generative AI Hub.
1. Few-Shot Learning in Prompt Engineering:
* Definition:Few-shot learning involves providing a language model with a limited number of examples to enable it to perform a specific task effectively. In this context, the model isgiven a few examples of categorized messages to learn how to assign urgency and sentiment to new, unseen emails.
2. Code Functionality:
* Prompt Template Creation:The prompt_test variable defines a template that instructs the model to extract and categorize messages, specifying the desired output format as a JSON string.
* Example Selection:The code randomly selects a subset of examples from a development set (dev_set) to include in the prompt, demonstrating the expected input-output pairs to the model.
* Model Interaction:The function f_test sends the constructed prompt, along with the input message, to the language model for processing.
* Response Handling:The model's response is expected to be a JSON string containing the assigned urgency, sentiment, and categories for the input message.
3. Purpose of the Code:
* Performance Evaluation:By using few-shot learning, the code evaluates how well the language model can generalize from the provided examples to accurately categorize new customer emails. This approach assesses the model's ability to understand and apply the categorization criteria based on minimal training data.

NEW QUESTION # 32
What are the benefits of SAP's generative Al hub? Note: There are 2 correct answers to this question.

A. Accelerate Al development with flexible access to a broad range of models
B. Build custom Al solutions and extend SAP applications
C. Provide libraries for no-code development
D. Send your data to various LLM providers for training feedback

Answer: A,B

Explanation:
SAP's Generative AI Hub offers several benefits that enhance AI development and integration within business processes:
1. Accelerate AI Development with Flexible Access to a Broad Range of Models:
* Diverse Model Access:The Generative AI Hub provides instant access to a wide array of large language models (LLMs) from various providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b.
* Flexible Integration:This access allows developers to select and utilize the most suitable models for their specific use cases, thereby accelerating AI development and deployment.
2. Build Custom AI Solutions and Extend SAP Applications:
* Custom AI Solutions:The hub offers a comprehensive toolset for building custom AI solutions, including prompt engineering tools, SDKs, and fine-tuning services.
* Extending SAP Applications:Developers can leverage these tools to create AI-powered extensions for SAP applications like SAP S/4HANA and SAP SuccessFactors, enhancing their functionality and adaptability.

NEW QUESTION # 33
......

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