最新C-AIG-2412合格保証試験問題集には正確で最新な問題があります
C-AIG-2412試験ブレーン問題集で学習注釈と理論
質問 # 25
You want to extract useful information from customer emails to augment existing applications in your company.
How can you use generative-ai-hub-sdk in this context?
- A. Generate random email content and send them to customers.
- B. Train custom models based on the mail data.
- C. Generate a new SAP application based on the mail data.
- D. Generate JSON strings based on extracted information.
正解：D
解説：
The generative-ai-hub-sdk in SAP's Generative AI Hub enables developers to interact with large language models (LLMs) for various tasks, including information extraction and data formatting.
1. Extracting Information from Customer Emails:
* Natural Language Processing (NLP):By leveraging LLMs, the SDK can process unstructured email content to identify and extract pertinent information, such as customer inquiries, sentiments, or intents.
2. Generating JSON Strings:
* Structured Data Output:After extracting the necessary information, the SDK can format the data into JSON strings. This structured format is essential for integrating the extracted information into existing applications, facilitating seamless data exchange and processing.
3. Integration into Existing Applications:
* Application Enhancement:The JSON-formatted data can be utilized to augment existing applications, such as customer relationship management (CRM) systems, by providing insights derived from customer emails, thereby improving decision-making and customerinteractions.
質問 # 26
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
正解：A
解説：
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.
質問 # 27
You want to use the orchestration service through SAP's generative-Al-hub-sdk. What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM 11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
- A. Define the Template and Default Input Values
- B. Define the LLM
- C. Run the Orchestration Request
- D. Create the Orchestration Configuration
正解：B
解説：
The provided code snippet defines a Large Language Model (LLM) within the SAP Generative AI Hub SDK's orchestration service:
from gen_ai_hub.orchestration.models.llm import LLM
llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
1. Importing the LLM Class:
* Code:from gen_ai_hub.orchestration.models.llm import LLM
* Purpose:Imports the LLM class from the SDK, enabling the creation of an LLM instance.
2. Defining the LLM Instance:
* Code:llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature":
0.2})
* Parameters:
* name:Specifies the model's name, in this case, "gpt-40".
* version:Indicates the model version, set to "latest" to use the most recent version.
* parameters:A dictionary defining model-specific parameters:
* max_tokens:Sets the maximum number of tokens (words or word pieces) the model can generate, here limited to 256 tokens.
* temperature:Controls the randomness of the output; a lower value like 0.2 results in more deterministic responses.
3. Role in Orchestration Pipeline:
* Function:This definition is a crucial step in the orchestration pipeline, specifying which LLM to use and configuring its behavior for subsequent tasks.
Conclusion:
The code snippet defines an LLM named "gpt-40" with specific parameters, preparing it for integration into an AI-driven workflow within SAP's Generative AI Hub.
質問 # 28
Which of the following is a benefit of using Retrieval Augmented Generation?
- A. It eliminates the need for fine-tuning LLMs for specific tasks.
- B. It allows LLMs to access and utilize information beyond their initial training data.
- C. It enables LLMs to learn new languages without additional training.
- D. It reduces the computational resources required for language modeling.
正解：B
解説：
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.
質問 # 29
Which of the following are functionalities provided by the generative-Al-hub-SDK ? Note: There are 2 correct answers to this question.
- A. Interact with LLMs
- B. Customize SAP AI Launchpad
- C. Create chat responses and embeddings
- D. Configure SAP BTP credentials
正解：A、C
解説：
The Generative AI Hub SDK offers functionalities that empower developers to:
1. Interact with Large Language Models (LLMs):
* Model Access:The SDK provides a developer-friendly way to consume foundational models available in the SAP Generative AI Hub, facilitating seamless interactions with these models.
2. Create Chat Responses and Embeddings:
* Natural Language Processing:With this SDK, developers can interact with models to create natural language completions, chat responses, and embeddings, enabling the development of sophisticated AI- driven applications.
Conclusion:
The Generative AI Hub SDK enables developers to interact with LLMs and create chat responses and embeddings, supporting the development of advanced AI functionalities within applications.
質問 # 30
Which of the following sequence of steps does SAP recommend you use to solve a business problem using generative Al hub?
- A. Create a basic prompt in SAP AI Launchpad
- B. Create a basic prompt in SAP AI Launchpad
- C. Create a basic prompt in SAP AI Launchpad
正解：C
質問 # 31
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. Maintaining data privacy by using data masking techniques
- B. Supporting low code evaluations using graphical user interface
- C. Creating custom evaluators that meet specific business needs
- D. Providing metrics to quantitatively assess response quality
- E. Automating prompt testing across various scenarios
正解：C、D、E
質問 # 32
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.
- A. Store the dataset in a hyperscaler object store.
- B. Grant access rights to the SAP BTP cockpit.
- C. Store the dataset in the SAP HANA Vector Engine.
- D. Provide the storage secret to access the dataset.
正解：A、D
質問 # 33
What is the primary function of the generative Al hub in SAP's Al Foundation?
- A. To store embeddings of unstructured data for semantic data retrieval
- B. To provide ready-to-use Al services for document processing
- C. To manage the Al lifecycle efforts end-to-end
- D. To serve as an abstraction layer to access a range of foundation Al models
正解：D
質問 # 34
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.
- A. It only supports traditional machine learning models.
- B. It ensures relevant, reliable, and responsible business Al.
- C. It provides instant access to a wide range of large language models (LLMs).
- D. It operates independently of SAP's partners and ecosystem.
正解：B、C
質問 # 35
Which of the following steps is NOT a requirement to use the Orchestration service?
- A. Get an auth token for orchestration
- B. Modify the underlying Al models
- C. Create an instance of an Al model
- D. Create a deployment for orchestration
正解：B
質問 # 36
What can be done once the training of a machine learning model has been completed in SAP AI Core? Note: There are 2 correct answers to this question.
- A. The model can be deployed for inferencing.
- B. The model's accuracy can be optimized directly in SAP HANA.
- C. The model can be deployed in SAP HAN
- D. The model can be registered in the hyperscaler object store.
正解：A、D
質問 # 37
Which of the following executables in generative Al hub works with Anthropic models?
- A. AWS Bedrock
- B. SAP AI Core
- C. Azure OpenAl Service
- D. GCP Vertex Al
正解：A
質問 # 38
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.
- A. It only supports traditional machine learning models.
- B. It ensures relevant, reliable, and responsible business Al.
- C. It provides instant access to a wide range of large language models (LLMs).
- D. It operates independently of SAP's partners and ecosystem.
正解：B、C
解説：
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.
質問 # 39
How does SAP ensure the enterprise-readiness of its Al solutions?
- A. By using generic Al models without business context complying with Al ethics standards
- B. By implementing rigorous product standards for Al capabilities
- C. By ensuring that Al models make bias-free decisions without human input
正解：B
質問 # 40
How does the Al API support SAP AI scenarios? Note: There are 2 correct answers to this question.
- A. By managing Kubernetes clusters automatically
- B. By providing a unified framework for operating Al services
- C. By integrating Al services into business applications
- D. By integrating Al models into third-party platforms like AWS
正解：B、C
質問 # 41
What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.
- A. Direct deployment of Al models to SAP HAN
- B. Integration with non-SAP platforms like Azure and AWS.
- C. Simplified model retraining and performance improvement.
- D. Centralized Al lifecycle management for all Al scenarios.
正解：C、D
質問 # 42
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?
- A. One-shot prompting
- B. Few-shot prompting
- C. Chain-of-thought prompting
- D. Meta prompting
正解：D
解説：
Meta prompting is a technique in prompt engineering where a prompt is designed to generate or refine subsequent prompts.
1. Definition and Purpose:
* Streamlining Prompt Development:Meta prompting automates the creation of effective prompts by utilizing AI to generate or enhance them, thereby streamlining the prompt development process.
* Guiding AI Model Responses:By generating refined prompts, meta prompting effectively guides AI models to produce more accurate and contextually relevant responses.
2. Application in SAP's Generative AI Hub:
* Prompt Engineering Tools:SAP's Generative AI Hub provides tools that support advanced prompt engineering techniques, including meta prompting, to enhance AI model interactions.
質問 # 43
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?
- A. To simplify the process of training the embedding model
- B. To enable the matching of different relevant passages to user queries
- C. To improve the efficiency of encoding queries into vector representations
- D. To reduce the storage space required for the vector database
正解：B
解説：
In Retrieval-Augmented Generation (RAG) systems, splitting documents into smaller overlapping chunks is a crucial preprocessing step that enhances the system's ability to match relevant passages to user queries.
1. Purpose of Splitting Documents into Smaller Overlapping Chunks:
* Improved Retrieval Accuracy:Dividing documents into smaller, manageable segments allows the system to retrieve the most relevant chunks in response to a user query, thereby improving the precision of the information provided.
* Context Preservation:Overlapping chunks ensure that contextual information is maintained across segments, which is essential for understanding the meaning and relevance of each chunk in relation to the query.
2. Benefits of This Approach:
* Enhanced Matching:By having multiple overlapping chunks, the system increases the likelihood that at least one chunk will closely match the user's query, leading to more accurate and relevant responses.
* Efficient Processing:Smaller chunks are easier to process and analyze, enabling the system to handle large documents more effectively and respond to queries promptly.
質問 # 44
Where can you configure language models in generative Al hub?
- A. The Orchestration tab in SAP AI Launchpad
- B. The Models tab in Prompt Editor
- C. The Configuration tab within ML Operations in SAP AI Launchpad
- D. The Configuration tab of the SAP BTP cockpit
正解：C
質問 # 45
You want to use the orchestration service through SAP's generative-Al-hub-sdk.
What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM
11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
- A. Define the Template and Default Input Values
- B. Define the LLM
- C. Run the Orchestration Request
- D. Create the Orchestration Configuration
正解：B
質問 # 46
What are some use cases for fine-tuning of a model?
Note: There are 2 correct answers to this question.
- A. To sanitize model outputs
- B. To quickly create iterations on a new use case
- C. To customize outputs for specific types of inputs
- D. To introduce new knowledge to a model in a resource-efficient way
正解：C、D
質問 # 47
What is Machine Learning (ML)?
- A. A technology that equips machines with human-like capabilities such as problem-solving, visual perception, and decision-making.
- B. A form of Al that only focuses on creating new content, including text, images, sound, and videos.
- C. A statistical method for data processing that does not involve any Al techniques.
- D. A subset of Al that focuses on enabling computer systems to learn and improve from experience or data.
正解：D
解説：
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that empowers computer systems to learn from data and experiences, enhancing their performance over time without explicit programming for each task.
1. Definition and Core Concept:
* Learning from Data:ML algorithms process and analyze large datasets to identify patterns and make informed decisions or predictions based on new, unseen data.
* Improvement Over Time:Through iterative processes, ML models refine their accuracy and efficiency as they are exposed to more data, leading to continuous performance enhancement.
2. Types of Machine Learning:
* Supervised Learning:Models are trained on labeled datasets, where the desired output is known, to make predictions or classifications.
* Unsupervised Learning:Models work with unlabeled data to identify inherent structures or patterns without predefined outcomes.
* Reinforcement Learning:Systems learn by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting actions accordingly.
3. Applications in SAP's AI Solutions:
* SAP AI Core and AI Launchpad:SAP provides a unified framework for managing and deploying ML models, facilitating seamless integration into business processes.
* Generative AI Hub:This platform offers access to a variety of large language models (LLMs) and supports the orchestration of AI tasks, enabling the development of AI-driven applications.
質問 # 48
What are the benefits of SAP's generative Al hub? Note: There are 2 correct answers to this question.
- A. Accelerate Al development with flexible access to a broad range of models
- B. Send your data to various LLM providers for training feedback
- C. Build custom Al solutions and extend SAP applications
- D. Provide libraries for no-code development
正解：A、C
質問 # 49
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SAP C-AIG-2412 認定試験の出題範囲：
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合格させるSAP C-AIG-2412テスト練習問題 試験問題集：https://www.passtest.jp/SAP/C-AIG-2412-shiken.html
ベストSAP Certified Associate学習ガイドにはC-AIG-2412試験問題集：https://drive.google.com/open?id=1nuF-xbmuXY_VZsbDKIGnzCSp08UUa5Va