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

Comprehensive and Detailed In-Depth Explanation:

The “temperature” parameter adjusts the randomness of an LLM’s output by scaling the softmax distribution—low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity—Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.

OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

Comprehensive and Detailed In-Depth Explanation:

RAG enhances text generation by combining an LLM’s internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG’s purpose—data is used, not just stored. Option D is incorrect—RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.

OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

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:

In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design—Option C is correct. The example shows two, but it’s not a requirement. Option A (minimum two) is false—no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose—variables are optional but supported. This adaptability aids prompt engineering.

OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

Comprehensive and Detailed In-Depth Explanation:

Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don’t deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow—embeddings capture semantics beyond just grammar.

OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.

Comprehensive and Detailed In-Depth Explanation:

Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings’ primary purpose. Option D is incorrect, as embeddings aren’t primarily for compression but for representation.

OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.

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:

For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can’t dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG’s capabilities. RAG meets both requirements effectively.

OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

Comprehensive and Detailed In-Depth Explanation:

String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false—variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.

OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.

Comprehensive and Detailed In-Depth Explanation:

The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.

OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.