C-AIG-2412 PRACTICE EXAM | C-AIG-2412 RELIABLE EXAM GUIDE

C-AIG-2412 Practice Exam | C-AIG-2412 Reliable Exam Guide

C-AIG-2412 Practice Exam | C-AIG-2412 Reliable Exam Guide

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SAP C-AIG-2412 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.

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C-AIG-2412 Reliable Exam Guide, C-AIG-2412 Latest Test Questions

You will need to pass the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) exam to achieve the SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) certification. Due to extremely high competition, passing the SAP C-AIG-2412 exam is not easy; however, possible. You can use RealExamFree products to pass the C-AIG-2412 Exam on the first attempt. The SAP Certified Associate - SAP Generative AI Developer (C-AIG-2412) practice exam gives you confidence and helps you understand the criteria of the testing authority and pass the SAP C-AIG-2412 exam on the first attempt.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q20-Q25):

NEW QUESTION # 20
What are some use cases for fine-tuning of a model? Note: There are 2 correct answers to this question.

  • A. To quickly create iterations on a new use case
  • B. To customize outputs for specific types of inputs
  • C. To introduce new knowledge to a model in a resource-efficient way
  • D. To sanitize model outputs

Answer: B,C


NEW QUESTION # 21
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

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

Answer: B,C,D

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 # 22
What is the primary function of the embedding model in a RAG system?

  • A. To evaluate the faithfulness and relevance of generated Answers
  • B. To store vector representations of documents and search for relevant passages
  • C. To generate responses based on retrieved documents and user queries
  • D. To encode queries and documents into vector representations for comparison

Answer: D

Explanation:
In a Retrieval-Augmented Generation (RAG) system, the embedding model plays a crucial role in encoding textual data into vector representations, facilitating efficient retrieval and comparison.
1. Function of the Embedding Model:
* Vector Encoding:The embedding model transforms both user queries and documents into high- dimensional vector representations. This numerical encoding captures the semantic meaning of the text, enabling the system to assess similarities between different pieces of text effectively.
* Facilitating Retrieval:By encoding text into vectors, the system can perform efficient similarity searches within a vector database, identifying documents or passages that are most relevant to the user's query.
2. Importance in RAG Systems:
* Semantic Matching:The vector representations allow the system to match user queries with relevant documents based on semantic content rather than mere keyword overlap, enhancing the relevance of retrieved information.
* Efficiency:Vector-based retrieval is computationally efficient, enabling rapid identificationof pertinent information from large datasets, which is essential for real-time applications.
3. Application in SAP's Generative AI Hub:
* Integration with HANA Vector Search:SAP's Generative AI Hub integrates embedding models with HANA's vector search capabilities, allowing for efficient storage and retrieval of vector embeddings.
This integration supports the development of RAG systems that can effectively utilize SAP's data assets.
* Generative AI Hub SDK:SAP provides an SDK that facilitates the implementation of embedding models within RAG systems, enabling developers to encode queries and documents into vector representations seamlessly.


NEW QUESTION # 23
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. Preprocess a dataset for machine learning
  • B. Generate random examples for language model training
  • C. Train a language model from scratch
  • D. Evaluate the performance of a language model using few-shot learning

Answer: D


NEW QUESTION # 24
What are some benefits of using an SDK for evaluating prompts within the context of generative Al? Note:
There are 3 correct answers to this question.

  • A. Creating custom evaluators that meet specific business needs
  • B. Maintaining data privacy by using data masking techniques
  • C. Supporting low code evaluations using graphical user interface
  • D. Automating prompt testing across various scenarios
  • E. Providing metrics to quantitatively assess response quality

Answer: A,D,E

Explanation:
Utilizing an SDK for evaluating prompts within the context of generative AI offers several benefits:
1. Creating Custom Evaluators That Meet Specific Business Needs:
* Tailored Evaluation Metrics:An SDK allows developers to design and implement custom evaluation metrics that align with specific business objectives, ensuring that prompt assessments are relevant and meaningful.
* Flexibility in Evaluation Criteria:Developers can define criteria that reflect the unique requirements of their applications, leading to more accurate and business-aligned evaluations.
2. Automating Prompt Testing Across Various Scenarios:
* Scalability:An SDK enables the automation of prompt testing across multiple scenarios, facilitating large-scale evaluations without manual intervention.
* Consistency:Automated testing ensures consistent application of evaluation criteria, reducing the potential for human error and increasing reliability.
3. Providing Metrics to Quantitatively Assess Response Quality:
* Objective Assessment:The SDK can generate quantitative metrics, such as accuracy, relevance, and coherence scores, providing an objective basis for evaluating prompt performance.
* Performance Monitoring:These metrics enable continuous monitoring and improvement of prompt quality, ensuring that AI models deliver optimal results.


NEW QUESTION # 25
......

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