C-AIG-2412 - Perfect Valid Dumps SAP Certified Associate - SAP Generative AI Developer Ebook

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SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 2	SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 3	SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.
Topic 4	SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.

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

NEW QUESTION # 44
What capabilities does the Exploration and Development feature of the generative Al hub provide?
Note: There are 2 correct answers to this question.

A. Al playground and chat
B. Prompt editor and management
C. Automatic model selection
D. Develop and debug ABAP code

Answer: A,B

NEW QUESTION # 45
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. Generate random examples for language model training
B. Preprocess a dataset for machine learning
C. Train a language model from scratch
D. Evaluate the performance of a language model using few-shot learning

Answer: D

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 # 46
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system?
Note: There are 2 correct answers to this question.

A. Faithfulness
B. Relevance
C. Carbon footprint
D. Speed

Answer: A,B

NEW QUESTION # 47
Which of the following is a principle of effective prompt engineering?

A. Write vague and open-ended instructions to encourage creativity.
B. Keep prompts as short as possible to avoid confusion.
C. Use precise language and providing detailed context in prompts.
D. Combine multiple complex tasks into a single prompt.

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 # 48
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

A. The LLM directly ans wers the user's question without accessing external information.
B. The embedding model stores the generated ans wers for future reference.
C. The user's questi on is used to search a knowledge base or a set of documents.
D. The retrieved content is combined with the LLM's capabilities to generate a response.

Answer: C,D

NEW QUESTION # 49
......

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C-AIG-2412 - Perfect Valid Dumps SAP Certified Associate - SAP Generative AI Developer Ebook

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

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