Comprehensive and Detailed In-Depth Explanation:

Few-shot prompting involves providing a few examples in the prompt to guide the LLM’s behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn’t expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn’t significantly affected by few-shot prompting.

OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

Comprehensive and Detailed In-Depth Explanation:

In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.

OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

Comprehensive and Detailed In-Depth Explanation:

Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn’t how greedy decoding works—it’s probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.

OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.

Comprehensive and Detailed In-Depth Explanation:

" k-shot prompting " (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM’s output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets—examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated—k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.

OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

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:

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:

Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false—text generation can benefit from complex models. Option B is incorrect—text is categorical. Option D is wrong, as diffusion models aren’t inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it’s less straightforward.

OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.

Comprehensive and Detailed In-Depth Explanation:

T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM’s weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false—T-Few updates weights, not architecture. Option D is incorrect—T-Few typically reduces training time. T-Few optimizes efficiency.

OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.

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 “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.