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SAP C-AIG-2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q61-Q66):

NEW QUESTION # 61
Which neural network architecture is primarily used by LLMs?

A. Transformer architecture with self-attention mechanisms
B. Sequential encoder-decoder architecture
C. Convolutional Neural Networks (CNNs)
D. Recurrent neural network architecture

Answer: A

Explanation:
Large Language Models (LLMs) primarily utilize the Transformer architecture, which incorporates self- attention mechanisms.
1. Transformer Architecture:
* Overview:Introduced in 2017, the Transformer architecture revolutionized natural language processing by enabling models to handle long-range dependencies in text more effectively than previous architectures.
GeeksforGeeks
* Components:The Transformer consists of an encoder-decoder structure, where the encoder processes input sequences, and the decoder generates output sequences.
2. Self-Attention Mechanisms:
* Functionality:Self-attention allows the model to weigh the importance of different words in a sequence relative to each other, enabling it to capture contextual relationships regardless of their position.
* Benefits:This mechanism facilitates parallel processing of input data, improving computational efficiency and performance in understanding complex language patterns.
3. Application in LLMs:
* Model Examples:LLMs such as GPT-3 and BERT are built upon the Transformer architecture, leveraging self-attention to process and generate human-like text.
* Advantages:The Transformer architecture's ability to manage extensive context and dependencies makes it well-suited for tasks like language translation, summarization, and question-answering.

NEW QUESTION # 62
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. Train a language model from scratch
B. Generate random examples for language model training
C. Preprocess a dataset for machine learning
D. Evaluate the performance of a language model using few-shot learning

Answer: D

NEW QUESTION # 63
Which of the following is unique about SAP's approach to Al?

A. Offering Al capabilities in their future products as of 2025.
B. Utilizing Al mainly for marketing purposes.
C. Focusing Al solely on customer support services.
D. SAP's deep integration of Al with business processes and analytics.

Answer: D

NEW QUESTION # 64
How can few-shot learning enhance LLM performance?

A. By offering input-output pairs that exemplify the desired behavior
B. By enhancing the model's computational efficiency
C. By reducing overfitting through regularization techniques
D. By providing a large training set to improve generalization

Answer: A

NEW QUESTION # 65
Which of the following is a benefit of using Retrieval Augmented Generation?

A. It allows LLMs to access and utilize information beyond their initial training data.
B. It enables LLMs to learn new languages without additional training.
C. It eliminates the need for fine-tuning LLMs for specific tasks.
D. It reduces the computational resources required for language modeling.

Answer: A

Explanation:
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data.
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. Benefits of RAG:
* Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses.
* 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.
* Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.

NEW QUESTION # 66
......

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ACCURATE CERTIFICATION C-AIG-2412 DUMP & LEADING OFFER IN QUALIFICATION EXAMS & COMPLETE SAP SAP CERTIFIED ASSOCIATE - SAP GENERATIVE AI DEVELOPER

Accurate Certification C-AIG-2412 Dump & Leading Offer in Qualification Exams & Complete SAP SAP Certified Associate - SAP Generative AI Developer