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

Comprehensive and Detailed In-Depth Explanation:

In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.

OCI 2025 Generative AI documentation likely discusses memory under LangChain’s context management features.

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.

Comprehensive and Detailed In-Depth Explanation:

A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite—penalizing unused tokens isn’t the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.

OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

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.