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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 hours?

A. 30 unit hours
B. 20 unit hours
C. 40 unit hours
D. 25 unit hours

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 × 2 = 20 unit hours-Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.

NEW QUESTION # 17
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

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

Answer: D

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 # 18
What does in-context learning in Large Language Models involve?

A. Conditioning the model with task-specific instructions or demonstrations
B. Adding more layers to the model
C. Training the model using reinforcement learning
D. Pretraining the model on a specific domain

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model's pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context.
OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt-based customization.

NEW QUESTION # 19
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

A. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
B. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
C. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
D. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.

NEW QUESTION # 20
In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To convert vectors into a non-indexed format for easier retrieval
B. To map vectors to a data structure for faster searching, enabling efficient retrieval
C. To compress vector data for minimized storage usage
D. To categorize vectors based on their originating data type (text, images, audio)

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.

NEW QUESTION # 21
......

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