Free Oracle 1z0-1127-25 Practice Exam with Questions & Answers | Set: 2

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:

A “model endpoint” in OCI’s inference workflow is an API or interface where users send requests and receive responses from a deployed model—Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.

OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

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.

Comprehensive and Detailed In-Depth Explanation:

Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague—both measure similarity, not distinct content vs. topicality. Option B is false—both address semantics, not syntax. Option D is incorrect—neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.

OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

Comprehensive and Detailed In-Depth Explanation:

LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)—Options B, C, and D are valid. ConversationImageMemory (A) isn’t a standard type—image handling typically requires custom or multimodal extensions, not a built-in memory class—making A correct as NOT included.

OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.

Comprehensive and Detailed In-Depth Explanation:

T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load—Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false—T-Few updates, not excludes. This selective approach optimizes resource use.

OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

Comprehensive and Detailed In-Depth Explanation:

Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.

OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

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.

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.

Comprehensive and Detailed In-Depth Explanation:

In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn’t considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn’t the primary metric. Keyword search prioritizes exact matches.

OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.