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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
What does a cosine distance of 0 indicate about the relationship between two embeddings?

A. They are unrelated
B. They have the same magnitude
C. They are completely dissimilar
D. They are similar in direction

Answer: D

NEW QUESTION # 19
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. 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.
C. 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.
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 # 20
Which is NOT a built-in memory type in LangChain?

A. Conversation Summary Memory
B. Conversation Buffer Memory
C. Conversation ImgeMemory
D. Conversation Token Buffer Memory

Answer: C

NEW QUESTION # 21
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Improved retrievals for Retrieval Augmented Generation (RAG) systems
B. Capacity to translate text in over u languages
C. Support for tokenizing longer sentences
D. Emphasis on syntactic clustering of word embedding's

Answer: A

Explanation:
The Cohere Embed v3 model distinguishes itself from its predecessor in the OCI Generative AI service primarily through improved retrievals for Retrieval Augmented Generation (RAG) systems. This enhancement means that the new version of the model is better at retrieving relevant documents or passages that can be used to augment the generation of responses. The improvements likely include better embedding quality, which allows the model to find more relevant and contextually appropriate information during the retrieval phase.
Reference
Cohere model documentation and release notes
Technical discussions on improvements in RAG systems

NEW QUESTION # 22
In LangChain, which retriever search type is used to balance between relevancy and diversity?

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

Answer: B

NEW QUESTION # 23
......

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