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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q19-Q24):

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. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
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. 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.

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
What is the primary purpose of LangSmith Tracing?

A. To generate test cases for language models
B. To monitor the performance of language models
C. To debug issues in language model outputs
D. To analyze the reasoning process of language

Answer: D

NEW QUESTION # 21
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Shared among multiple customers for efficiency
B. Stored in Object Storage encrypted by default
C. Stored in an unencrypted form in Object Storage
D. Stored in Key Management service

Answer: B

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

NEW QUESTION # 22
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
B. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
C. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Answer: C

Explanation:
In natural language processing (NLP), encoder and decoder models play distinct but complementary roles:
Encoder Models: These models convert a sequence of words into a vector representation. They capture the semantic meaning of the input text and encode it into a fixed-size vector.
Decoder Models: These models take the vector representation generated by the encoder and convert it back into a sequence of words. This process allows for generating new text based on the encoded information, such as in translation or text generation tasks.
Reference
Research articles on encoder-decoder architectures in NLP
Technical guides on the use of encoder and decoder models in machine translation and text generation

NEW QUESTION # 23
What is the purpose of Retrievers in LangChain?

A. To retrieve relevant information from knowledge bases
B. To train Large Language Models
C. To combine multiple components into a single pipeline
D. To break down complex tasks into smaller steps

Answer: A

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.
🔹 Oracle Generative AI Reference:
Oracle AI integrates retrieval mechanisms in enterprise AI solutions, improving data-driven AI responses.

NEW QUESTION # 24
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