Free Practice Questions for Oracle 1z0-1127-25 Exam

Comprehensive and Detailed In-Depth Explanation:

OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn’t a core OCI generative focus based on standard offerings.

OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

Comprehensive and Detailed In-Depth Explanation:

Prompt injection (jailbreaking) attempts to bypass an LLM’s restrictions by crafting prompts that trick it into revealing restricted information or behavior. Option A asks the model to creatively circumvent its protocols, a classic jailbreaking tactic—making it correct. Option B is a hypothetical persuasion task, not a bypass. Option C tests privacy handling, not injection. Option D is a creative writing prompt, not an attempt to break rules. A seeks to exploit protocol gaps.

OCI 2025 Generative AI documentation likely addresses prompt injection under security or ethics sections.

Comprehensive and Detailed In-Depth Explanation:

T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful—Option C is correct. Option A (semantic understanding) is too vague—dataset size matters more. Option B (dedicated cluster) isn’t a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.

OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.

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 a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user’s query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever’s role. Option B pertains to a Ranker. Option D is unrelated, as storage isn’t the Generator’s function but a separate system task. The Generator’s role is critical in transforming inputs into natural language responses.

OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.

Comprehensive and Detailed In-Depth Explanation:

StreamlitChatMessageHistory integrates with Streamlit’s session state to store chat history, tied to a specific key (Option A, true). It’s not persisted beyond the session (Option B, true) and isn’t shared across users (Option C, true), as Streamlit sessions are user-specific. However, it’s designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.

OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.

Comprehensive and Detailed In-Depth Explanation:

In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model’s reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn’t a performance-enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.

OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.

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.

Comprehensive and Detailed In-Depth Explanation:

In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model’s pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context.

OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt-based customization.