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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Support for tokenizing longer sentences
B. Emphasis on syntactic clustering of word embeddings
C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
D. Capacity to translate text in over 100 languages

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.

NEW QUESTION # 61
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
B. A pre-trained LLM model from Cohere or OpenAI.
C. An LLM dedicated to generating text responses without external data integration.
D. A keyword search-based AI that responds based on specific keywords identified in customer queries.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

NEW QUESTION # 62
When does a chain typically interact with memory in a run within the LangChain framework?

A. After user input but before chain execution, and again after core logic but before output.
B. Only after the output has been generated.
C. Before user input and after chain execution.
D. Continuously throughout the entire chain execution process.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.

NEW QUESTION # 63
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Ranker
B. Retriever
C. Generator
D. Encoder-Decoder

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 64
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

A. 744 unit hours
B. 480 unit hours
C. 240 unit hours
D. 20 unit hours

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

NEW QUESTION # 65
......

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Oracle - Useful 1Z0-1127-25 - New Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Duration

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q60-Q65):

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