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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
B. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
C. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
D. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

Answer: A

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 # 21
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

A. Explicitly providing k examples of the intended task in the prompt to guide the models output
B. Limiting the model to only k possible outcomes or answers for a given task
C. Providing the exact k words in the prompt to guide the model's response
D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

NEW QUESTION # 22
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship.
What is the nature of these relationships, and why are they crucial for language models?

A. Semantic relationships; crucial for understanding context and generating precise language
B. Linear relationships; they simplify the modeling process
C. Temporal relationships; necessary for predicting future linguistic trends
D. Hierarchical relationships; important for structuring database queries

Answer: A

Explanation:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
🔹 Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.

NEW QUESTION # 23
In LangChain, which retriever search type is used to balance between relevancy and diversity?

A. mmr
B. similarity
C. similarity_score_threshold
D. top k

Answer: A

Explanation:
In LangChain, the "mmr" (Maximal Marginal Relevance) search type is used to balance between relevancy and diversity when retrieving documents. This technique aims to select documents that are not only relevant to the query but also diverse from each other. This helps in avoiding redundancy and ensures that the retrieved set of documents covers a broader aspect of the topic.
Maximal Marginal Relevance (MMR) works by iteratively selecting documents that have high relevance to the query but low similarity to the documents already selected. This ensures that each new document adds new information and perspectives, rather than repeating what is already included.
Reference
LangChain documentation on retrievers and search types
Research papers and articles on Maximal Marginal Relevance (MMR)

NEW QUESTION # 24
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By loading the entire model into G PU memory for efficient processing
B. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
C. By allocating separate GPUS for each model instance
D. By optimizing GPIJ memory utilization for each model's unique para

Answer: B

NEW QUESTION # 25
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