QUIZ 2025 SAP C-AIG-2412: USEFUL LATEST SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER TEST CAMP

Quiz 2025 SAP C-AIG-2412: Useful Latest SAP Certified Associate - SAP Generative AI Developer Test Camp

Quiz 2025 SAP C-AIG-2412: Useful Latest SAP Certified Associate - SAP Generative AI Developer Test Camp

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Tags: Latest C-AIG-2412 Test Camp, Latest C-AIG-2412 Learning Material, C-AIG-2412 Valid Exam Notes, C-AIG-2412 Latest Exam Format, Valid Exam C-AIG-2412 Registration

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q35-Q40):

NEW QUESTION # 35
What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

  • A. Executables that define the training process
  • B. The SAP HANA database for model storage
  • C. Input datasets stored in a hyperscaler object store
  • D. Automated deployment to Kubernetes clusters

Answer: A,C


NEW QUESTION # 36
Which of the following are features of the SAP AI Foundation? Note: There are 2 correct answers to this question.

  • A. Ready-to-use Al services
  • B. Joule integration in SAP SuccessFactors
  • C. Open source Al model repository
  • D. Al runtimes and lifecycle management

Answer: A,D

Explanation:
SAP AI Foundation is an all-in-one AI toolkit that provides developers with the necessary tools to build AI- powered extensions and applications on SAP Business Technology Platform (SAP BTP).
1. Ready-to-Use AI Services:
* Pre-Built AI Capabilities:AI Foundation offers a suite of ready-to-use AI services, enabling developers to integrate AI functionalities into their applications without the need to build models from scratch. These services include capabilities such as document information extraction, translation, and personalized recommendations.
2. AI Runtimes and Lifecycle Management:
* Comprehensive AI Management:AI Foundation provides tools for managing AI runtimes and the entire AI lifecycle, including model deployment, monitoring, and maintenance. This ensures that AI models operate efficiently and remain up-to-date, facilitating seamless integration into business processes.
3. Integration with SAP BTP:
* Unified Platform:By integrating with SAP BTP, AI Foundation allows for the development of AI solutions that are grounded in business data and context, ensuring relevance and reliability in AI-driven applications.


NEW QUESTION # 37
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

  • A. Intelligent business document processing
  • B. Personalized recommendations based on Al algorithms
  • C. Al-powered forecasting and predictions
  • D. Face detection and face recognition
  • E. Automatic human emotion recognition

Answer: A,B,C

Explanation:
SAP Business AI offers a suite of capabilities designed to enhance various business processes through intelligent automation and data-driven insights.
1. Intelligent Business Document Processing:
* Document Information Extraction:SAP Business AI includes services that automate the extraction of relevant information from business documents, such as invoices and purchase orders. This automation reduces manual data entry, minimizes errors, and accelerates processing times.
2. AI-Powered Forecasting and Predictions:
* Predictive Analytics:SAP Business AI leverages machine learning models to analyze historical data and predict future trends. This capability assists businesses in demand forecasting, financial planning, and inventory management, enabling proactive decision-making.
3. Personalized Recommendations Based on AI Algorithms:
* Personalized Recommendation Services:By analyzing user behavior and preferences, SAP Business AI provides personalized product or service recommendations. This personalization enhances customer experience and can lead to increased sales and customer satisfaction.


NEW QUESTION # 38
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
'n---n'.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. Evaluate the performance of a language model using few-shot learning
  • B. Train a language model from scratch
  • C. Preprocess a dataset for machine learning
  • D. Generate random examples for language model training

Answer: A

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 # 39
Which of the following are features of the SAP AI Foundation? Note: There are 2 correct answers to this question.

  • A. Ready-to-use Al services
  • B. Joule integration in SAP SuccessFactors
  • C. Open source Al model repository
  • D. Al runtimes and lifecycle management

Answer: A,D


NEW QUESTION # 40
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