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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q61-Q66):
NEW QUESTION # 61
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?
Answer: A
Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference
NEW QUESTION # 62
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?
Answer: B
Explanation:
Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) are two techniques used for adapting pre-trained LLMs for specific tasks.
Fine-tuning:
Modifies all model parameters, requiring significant computing power.
Can lead to catastrophic forgetting, where the model loses prior general knowledge.
Example: Training GPT on medical texts to improve healthcare-specific knowledge.
Parameter-Efficient Fine-Tuning (PEFT):
Only a subset of model parameters is updated, making it computationally cheaper.
Uses techniques like LoRA (Low-Rank Adaptation) and Adapters to modify small parts of the model.
Avoids retraining the full model, maintaining general-purpose knowledge while adding task-specific expertise.
Why Other Options Are Incorrect:
(A) is incorrect because fine-tuning does not train from scratch, but modifies an existing model.
(B) is incorrect because both techniques involve model modifications.
(D) is incorrect because PEFT does not replace the model architecture.
🔹 Oracle Generative AI Reference:
Oracle AI supports both full fine-tuning and PEFT methods, optimizing AI models for cost efficiency and scalability.
NEW QUESTION # 63
Given the following code: chain = prompt |11m
Answer: A
Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language
NEW QUESTION # 64
How are documents usually evaluated in the simplest form of keyword-based search?
Answer: B
Explanation:
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.
NEW QUESTION # 65
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
Answer: B
Explanation:
Retrieval-Augmented Generation (RAG) combines retrieval mechanisms with text generation, allowing models to pull external knowledge before generating responses.
How RAG Works:
The model retrieves relevant documents from an external database.
Uses this retrieved information to generate factually grounded responses.
Reduces hallucinations, improving accuracy and context relevance.
Why Other Options Are Incorrect:
(A) is incorrect because RAG modifies the retrieved text by integrating it into a generated response.
(B) is incorrect because RAG retrieves and uses data, not just stores it.
(C) is incorrect because RAG relies on external knowledge, whereas LLMs alone use internal pre-trained knowledge.
🔹 Oracle Generative AI Reference:
Oracle AI applies RAG techniques to improve enterprise AI applications, enhancing fact-based text generation.
NEW QUESTION # 66
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