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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Enhanced generalization to unseen data
B. Reduced model complexity
C. Faster training time and lower cost
D. Increased model interpretability

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs-Option D is correct. Option A (complexity) isn't directly affected-structure remains. Option B (generalization) may occur but isn't the primary advantage. Option C (interpretability) isn't a focus. Efficiency is T-Few's hallmark.
OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.

NEW QUESTION # 86
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

A. By excluding transformer layers from the fine-tuning process entirely
B. By allowing updates across all layers of the model
C. By incorporating additional layers to the base model
D. By restricting updates to only a specific group of transformer layers

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

NEW QUESTION # 87
What does the Loss metric indicate about a model's predictions?

A. Loss is a measure that indicates how wrong the model's predictions are.
B. Loss indicates how good a prediction is, and it should increase as the model improves.
C. Loss describes the accuracy of the right predictions rather than the incorrect ones.
D. Loss measures the total number of predictions made by a model.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.

NEW QUESTION # 88
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 # 89
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

A. Choosing the word with the highest probability at each step of decoding
B. Using a weighted random selection based on a modulated distribution
C. Selecting a random word from the entire vocabulary at each step
D. Picking a word based on its position in a sentence structure

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn't how greedy decoding works-it's probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.
OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.

NEW QUESTION # 90
......

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