Master Oracle 1Z0-1127-25 Exam with Reliable Practice Questions
Upgrade to PremiumWhat does a cosine distance of 0 indicate about the relationship between two embeddings?
Correct : C
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content---Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague---directional similarity matters. Option D (magnitude) isn't relevant---cosine ignores magnitude. This is key for semantic comparison.
: OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.
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What does accuracy measure in the context of fine-tuning results for a generative model?
Correct : C
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.
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What does the Loss metric indicate about a model's predictions?
Correct : B
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.
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In the simplified workflow for managing and querying vector data, what is the role of indexing?
Correct : B
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.
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How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
Correct : A
Comprehensive and Detailed In-Depth Explanation=
In RAG, 'Groundedness' assesses whether the response is factually correct and supported by retrieved data, while 'Answer Relevance' evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off---groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns---groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
: OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.
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