Oracle Updated 1z0-1127-25 Exam Questions and Answers by jade

Comprehensive and Detailed In-Depth Explanation:

Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance—Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse—error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.

OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

Comprehensive and Detailed In-Depth Explanation:

Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn’t feasible due to large, task-specific data that can’t be efficiently included in prompts. This adjusts the model’s weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague—fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.

OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.

Comprehensive and Detailed In-Depth Explanation:

Vector databases store data as high-dimensional vectors (embeddings) and are optimized for similarity searches using metrics like cosine distance, unlike relational databases, which use tabular rows and columns for structured data. This makes Option D correct. Options A and C describerelational databases, not vector ones. Option B is false, as vector databases are specifically designed for high-dimensional spaces. Vector databases excel in semantic search and LLM integration.

OCI 2025 Generative AI documentation likely contrasts vector and relational databases under data storage.

Comprehensive and Detailed In-Depth Explanation:

A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence’s purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.

OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.