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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q13-Q18):
NEW QUESTION # 13
What do prompt templates use for templating in language model applications?
Answer: A
Explanation:
Prompt templates are structured text-based input patterns that include placeholders for dynamic variable substitution. These templates help generate prompts for LLMs (Large Language Models) in a systematic and reusable way.
Prompt Template Example using str.format():
template = "What is the capital of {country}?"
formatted_prompt = template.format(country="France")
print(formatted_prompt) # Output: "What is the capital of France?"
Why str.format() is Used:
It allows dynamic insertion of variables.
It is flexible and widely supported in Python-based AI frameworks.
Used in LangChain, OpenAI API, and Oracle AI applications.
Why Other Options Are Incorrect:
(A) Lambda functions are used for anonymous function execution, not string templating.
(C) List comprehensions are used for iterating over lists, not text formatting.
(D) Class and object structures define OOP models, not LLM prompt templates.
๐น Oracle Generative AI Reference:
Oracle AI frameworks use Python's str.format() and f-strings for LLM prompt engineering and AI-driven workflow automation.
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NEW QUESTION # 14
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?
Answer: B
Explanation:
In the inference workflow of the OCI Generative AI service, a "model endpoint" is a critical component. It serves as a designated point for handling user requests and providing model responses. When users or applications send requests to the model endpoint, the endpoint processes these requests by passing them to the deployed model. The model then generates responses based on the input data, and these responses are returned to the user through the same endpoint. This setup facilitates efficient and scalable interaction with the AI model, ensuring that inference can be performed seamlessly and reliably.
Reference
Oracle Cloud Infrastructure (OCI) Generative AI service documentation
General principles of model deployment and inference in cloud services
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NEW QUESTION # 15
What is the purpose of Retrievers in LangChain?
Answer: C
Explanation:
Retrievers in LangChain serve the primary function of fetching relevant data from an external knowledge base or database to enhance the performance of Large Language Models (LLMs).
How Retrievers Work:
They retrieve documents, embeddings, or structured data that might be relevant to a given query.
Used in Retrieval-Augmented Generation (RAG) models to fetch real-time data.
Improves model responses by providing accurate and up-to-date knowledge.
Use Cases of Retrievers:
Chatbots: Enhancing responses with real-world or proprietary knowledge.
Question Answering Systems: Providing factual accuracy by referencing stored knowledge.
Enterprise AI Solutions: Connecting with databases, vector stores, and APIs to fetch data.
Why Other Options Are Incorrect:
(A) is incorrect because breaking tasks into smaller steps is handled by agents or chains.
(C) is incorrect because retrievers do not train LLMs; they enhance query responses.
(D) is incorrect because pipelines integrate components, whereas retrievers fetch external data.
๐น Oracle Generative AI Reference:
Oracle AI integrates retrieval mechanisms in enterprise AI solutions, improving data-driven AI responses.
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NEW QUESTION # 16
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?
Answer: A
Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services
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NEW QUESTION # 17
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?
Answer: A
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NEW QUESTION # 18
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