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Realistic C_AIG_2412 Reliable Exam Tips | Easy To Study and Pass Exam at first attempt & Authoritative SAP SAP Certified Associate - SAP Generative AI Developer

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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 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 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'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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which technique is used to supply domain-specific knowledge to an LLM?

A. Prompt template expansion
B. Retrieval-Augmented Generation
C. Fine-tuning the model on general data
D. Domain-adaptation training

Answer: B

Explanation:
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by integrating external domain-specific knowledge, enabling more accurate and contextually relevant outputs.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Application in Supplying Domain-Specific Knowledge:
* Domain Adaptation:By leveraging RAG, LLMs can access specialized information without the need for extensive retraining or fine-tuning. This approach is particularly beneficial for domains with rapidly evolving information or where incorporating proprietary data is essential.
* Efficiency:RAG enables models to provide informed responses by referencing external data, reducing the necessity for large-scale domain-specific training datasets and thereby conserving computational resources.
3. Advantages of Using RAG:
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Enhanced Accuracy:Incorporating external knowledge allows the model to produce more precise and contextually relevant outputs, especially in specialized domains.

NEW QUESTION # 56
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.

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

Answer: B,D

Explanation:
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.

NEW QUESTION # 57
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. Train a language model from scratch
C. Evaluate the performance of a language model using few-shot learning
D. Generate random examples for language model training

Answer: C

NEW QUESTION # 58
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

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

Answer: A,D,E

NEW QUESTION # 59
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.

A. It provides instant access to a wide range of large language models (LLMs).
B. It only supports traditional machine learning models.
C. It ensures relevant, reliable, and responsible business Al.
D. It operates independently of SAP's partners and ecosystem.

Answer: A,C

Explanation:
The SAP Generative AI Hub is designed to integrate generative AI into business processes, offering several key features:
1. Ensuring Relevant, Reliable, and Responsible Business AI:
* Trusted AI Integration:The Generative AI Hub consolidates access to large language models (LLMs) and foundation models, grounding them in business and context data. This integration ensures that AI solutions are pertinent, dependable, and adhere to responsible AI practices.
2. Providing Instant Access to a Wide Range of Large Language Models (LLMs):
* Diverse Model Access:The hub offers immediate access to a broad spectrum of LLMs fromvarious providers, such as GPT-4 by Azure OpenAI and open-source models like Falcon-40b. This variety enables developers to select models that best fit their specific use cases.
3. Integration with SAP AI Core and AI Launchpad:
* Seamless Orchestration:The Generative AI Hub is part of SAP AI Core and AI Launchpad, facilitating the incorporation of generative AI into AI tasks. It streamlines innovation and ensures compliance, benefiting both SAP's internal needs and its broader ecosystem of partners and customers.

NEW QUESTION # 60
......

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