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Certification SAP C_AIG_2412 Test Answers & Reliable C_AIG_2412 Source

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q44-Q49):

NEW QUESTION # 44
What are some drivers for the rapid adoption of generative AI? Note: There are 2 correct answers to this question.

A. Wide availability
B. Ease of use
C. Availability of skilled developers
D. Significant hardware cost savings

Answer: A,B

Explanation:
The rapid adoption of generative AI is driven by several key factors:
1. Wide Availability:
* Access to Generative AI Tools:The proliferation of generative AI tools and platforms has made the technology widely accessible to businesses and individuals. This widespread availability facilitates experimentation and integration into various applications.
2. Ease of Use:
* User-Friendly Interfaces:Modern generative AI tools often feature intuitive interfaces that require minimal technical expertise, enabling a broader range of users to leverage AI capabilities without deep technical knowledge.

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 without necessarily producing output for human consumption.
B. To interpret human instructions and control software systems always producing output for human consumption.
C. To produce outputs based on software input.
D. To follow a specific schema - human input, Al processing, and output for human consumption.

Answer: A,B

NEW QUESTION # 46
How does SAP ensure the enterprise-readiness of its Al solutions?

A. By ensuring that Al models make bias-free decisions without human input
B. By implementing rigorous product standards for Al capabilities
C. By using generic Al models without business context complying with Al ethics standards

Answer: B

Explanation:
SAP ensures the enterprise-readiness of its AI solutions through the implementation of rigorous product standards:
1. Rigorous Product Standards for AI Capabilities:
* Development Guidelines:SAP adheres to strict guidelines during the development of AI systems, ensuring they meet high standards of quality, security, and performance.
* Ethical Framework:SAP's AI Ethics Policy governs the development, deployment, use, and sale of AI systems, defining clear ethical rules aligned with global standards.
* Compliance and Governance:SAP has established governance bodies and processes to oversee AI ethics, ensuring that AI solutions are developed and deployed responsibly.

NEW QUESTION # 47
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. Preprocess a dataset for machine learning
B. Evaluate the performance of a language model using few-shot learning
C. Generate random examples for language model training
D. Train a language model from scratch

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 # 48
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 ensure workload separation for different tenants or departments.
B. They enable simultaneous orchestration of Kubernetes clusters.
C. They provide isolation for datasets and Al artifacts.
D. They enhance pipeline execution speeds through workload distribution.

Answer: A,C

NEW QUESTION # 49
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