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NEW QUESTION # 71
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.
NEW QUESTION # 72
Which LangChain component is responsible for generating the linguistic output in a chatbot system?
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, LLMs (Large Language Models) generate the linguistic output (text responses) in a chatbot system, leveraging their pre-trained capabilities. This makes Option D correct. Option A (Document Loaders) ingests data, not generates text. Option B (Vector Stores) manages embeddings for retrieval, not generation. Option C (LangChain Application) is too vague-it's the system, not a specific component. LLMs are the core text-producing engine.
OCI 2025 Generative AI documentation likely identifies LLMs as the generation component in LangChain.
NEW QUESTION # 73
What differentiates Semantic search from traditional keyword search?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren't present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn't the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.
OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.
NEW QUESTION # 74
How does a presence penalty function in language model generation when using OCI Generative AI service?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.
NEW QUESTION # 75
In the simplified workflow for managing and querying vector data, what is the role of indexing?
Answer: D
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 # 76
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