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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q66-Q71):
NEW QUESTION # 66
What is the function of the Generator in a text generation system?
- A. To generate human-like text using the information retrieved and ranked, along with the user's original query
- B. To rank the information based on its relevance to the user's query
- C. To collect user queries and convert them into database search terms
- D. To store the generated responses for future use
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.
NEW QUESTION # 67
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?
- A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
- B. PEFT involves only a few or new parameters and uses labeled, task-specific data.
- C. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
- D. PEFT modifies all parameters and is typically used when no training data exists.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.
NEW QUESTION # 68
What is LangChain?
- A. A JavaScript library for natural language processing
- B. A Python library for building applications with Large Language Models
- C. A Java library for text summarization
- D. A Ruby library for text generation
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.
NEW QUESTION # 69
Which statement is true about string prompt templates and their capability regarding variables?
- A. They require a minimum of two variables to function properly.
- B. They can only support a single variable at a time.
- C. They are unable to use any variables.
- D. They support any number of variables, including the possibility of having none.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.
NEW QUESTION # 70
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?
- A. They use vector databases exclusively to produce answers.
- B. They rely on internal knowledge learned during pretraining on a large text corpus.
- C. They always use an external database for generating responses.
- D. They cannot generate responses without fine-tuning.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.
NEW QUESTION # 71
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