1Z0-1127-25 Flashcards — Free Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127-25 Study Cards

Adjust the chunk size and chunk overlap to better capture coherent passages.

Adjusting chunk size and overlap helps create coherent chunks that align with query intent, improving retrieval relevance. Option A is wrong because the embedding model type (dense vs. sparse) affects retrieval method but does not directly fix chunk coherence issues. Option C is wrong because increasing chunk size may introduce noise and irrelevant context. Option D is wrong because reducing the number of retrieved chunks (k) only limits results, not improves relevance of individual chunks.

Create an ANN index on the embedding vector column.

Approximate Nearest Neighbor (ANN) indexes are specifically designed for high-dimensional vector spaces, enabling efficient similarity search on embedding vectors. In Oracle Autonomous Database, ANN indexes (e.g., using IVF or HNSW algorithms) drastically reduce search latency compared to brute-force scans, which is critical for real-time RAG chatbot responses.

Fine-tune a small open-source LLM on domain-specific data

Fine-tuning a small open-source LLM on domain-specific data is the best approach because it adapts the model to understand industry-specific jargon while keeping the model small enough to maintain low latency. Unlike larger models, a fine-tuned small model can run efficiently on local hardware, reducing inference time and avoiding the overhead of external API calls or large model sizes.

The temperature is set too low; increase temperature during inference

The model's repetitive and dull responses indicate that the temperature parameter is too low, causing the model to always select the most probable tokens, leading to deterministic and monotonous outputs. Increasing temperature during inference introduces randomness into token sampling, allowing for more diverse and creative responses. This is a common issue in production LLMs where low temperature settings optimized for training metrics fail to produce engaging real-world outputs.

"Provide the output in JSON format with keys: 'summary', 'findings', and 'recommendations'."

Specifying JSON with explicit keys ('summary', 'findings', 'recommendations') gives the model an unambiguous, machine-parseable schema that enforces the exact three-section structure. This is the most effective format specification because it constrains the output to a deterministic structure that downstream code can consume directly, eliminating ambiguity about section names or ordering.

Self-consistency

Self-consistency is a technique specifically designed to address inconsistency in model outputs by sampling multiple reasoning paths (e.g., via chain-of-thought) and then selecting the most consistent answer through majority voting or similar aggregation. This directly targets the problem of contradictory statements within a single response by leveraging the model's own diverse reasoning trajectories to converge on a reliable answer.

Service Gateway

A Service Gateway enables private subnet resources to access OCI services (including the OCI Data Science model deployment endpoint) without traversing the internet. Since the inference endpoint must be accessible only from a private subnet within the same VCN, the Service Gateway provides the necessary private connectivity by routing traffic over the OCI network fabric, not through a NAT or internet gateway.

Deploy dedicated AI clusters in regions closer to the users

Latency for globally distributed users is primarily driven by network distance and the speed of light. Deploying dedicated AI clusters in regions closer to the users reduces the physical distance data must travel, directly minimizing network round-trip time (RTT). This is the most effective architectural change because OCI's Generative AI service processes each request on the dedicated cluster and cannot bypass geographic latency through software optimizations alone.

Cohere Embed (e.g., embed-english-v3.0)

Cohere Embed models (such as embed-english-v3.0) are specifically designed to generate vector embeddings from text for semantic search, clustering, and classification. Converting support tickets into embeddings for similarity search is exactly the use case for an embedding model. OCI Generative AI offers Cohere Embed as a managed embedding service.

Use the 'system' parameter (or preamble_override) to provide a system message like 'You are a formal assistant'

A system message or preamble override sets the overall behavior and tone of the assistant for the entire conversation. Temperature controls randomness; max tokens limits length; stop sequences end generation — none are suitable for defining a persistent tone.

To compute a weighted sum of all token representations based on pairwise relevance

Self-attention allows each token to attend to every other token in the sequence, capturing contextual relationships regardless of distance.

Encoder-only uses bidirectional attention and is suited for classification or NER; decoder-only uses causal attention and is suited for text generation

Encoder-only models like BERT employ bidirectional attention, allowing each token to attend to all other tokens in both directions, which is ideal for tasks requiring full context understanding such as classification or named entity recognition (NER). In contrast, decoder-only models like GPT use causal (masked) attention, where each token can only attend to previous tokens, making them suitable for autoregressive text generation.

RetrievalQA chain

RetrievalQA is the LangChain chain purpose-built to wire together a retriever, a prompt template, and an LLM so the model answers questions grounded in retrieved documents. It handles the retrieve-then-generate flow automatically: it fetches relevant chunks from the vector store, injects them into the prompt, and passes the augmented prompt to the LLM. This is the classic RAG pattern in LangChain.

as_retriever()

The as_retriever() method on a LangChain vector store object (including OracleVS) returns a VectorStoreRetriever that integrates with the LangChain chain interface. It accepts a search_kwargs dictionary where you can specify 'k' to control the number of results returned by similarity search. This is the standard LangChain pattern for converting a vector store into a retriever component for RAG pipelines.

Increase the frequency penalty

The frequency penalty directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. In OCI Generative AI Service, this parameter subtracts a fixed value from the log-probability of each token each time it is generated, making repeated tokens less likely to be chosen again. This is the most direct mechanism to address repetitive output.

Create a dynamic group and a policy granting access to the Generative AI Service

Resource principal authentication in OCI requires the application to be represented by a dynamic group, which matches instances or resources based on defined rules. A policy must then grant that dynamic group access to the Generative AI Service. This avoids storing API keys by using OCI IAM's built-in resource principal token exchange.