1Z0-1127-25 Practice Test 7 — 10 Questions

Question 1

A developer uses OCI Generative AI with a custom OCI OpenSearch vector store. The text generation model sometimes hallucinates facts not in the retrieved documents. What is the most effective mitigation?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use prompt engineering to instruct the model to stick to the provided context. Prompt engineering that instructs the model to strictly adhere to the provided context is the most effective mitigation because it directly addresses the root cause of hallucination: the model's tendency to generate information beyond the retrieved documents. By explicitly constraining the model's behavior through the system or user prompt, you reduce the likelihood of fabricated facts without altering the retrieval or generation parameters.

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Use a larger retrieval chunk size

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Increase the temperature

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Decrease the maximum token length

Question 2

A real-time customer support chatbot uses RAG with OCI Generative AI. The average response time is 5 seconds, which is too slow. The team identifies the vector search as the bottleneck. Which optimization would most reduce latency?

Accepted Answer

Use approximate nearest neighbor (ANN) search with a lower recall setting. The vector search bottleneck causing high latency is best addressed by reducing the computational cost of the nearest neighbor search. Approximate Nearest Neighbor (ANN) search with a lower recall setting trades a small amount of accuracy for a significant reduction in search time, directly targeting the root cause of the 5-second response time. This optimization is specifically designed for latency-sensitive applications like real-time chatbots.

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Increase the number of retrieved documents to 10

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Move the vector store to a different region

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Switch to a larger embedding model for better accuracy

Question 3

A company is building a RAG application on OCI and needs a managed vector database with native support for AI Vector Search, which offers high performance and integration with OCI GenAI. Which OCI service should they use?

Accepted Answer

Autonomous Database with AI Vector Search. Autonomous Database with AI Vector Search is the correct choice because it is a fully managed OCI service that natively supports vector similarity search, enabling high-performance RAG workflows. It integrates directly with OCI GenAI for embedding generation and LLM orchestration, eliminating the need for separate vector database infrastructure.

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OCI OpenSearch

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MySQL HeatWave

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OCI Object Storage

Question 4

A data scientist needs to fine-tune a large language model on a custom dataset of 10,000 prompt-completion pairs. They want to minimize cost while still updating the model effectively. Which fine-tuning technique is used by OCI Generative AI service?

Accepted Answer

T-Few fine-tuning. T-Few fine-tuning is the technique used by OCI Generative AI service for fine-tuning large language models. It is a parameter-efficient method that updates only a small number of parameters, reducing computational cost while still effectively adapting the model to custom datasets. This aligns with the goal of minimizing cost while updating the model effectively.

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Prefix tuning

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Adapter fine-tuning

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LoRA fine-tuning

Question 5

A developer notices that a Cohere Command model occasionally generates contradictory statements in the same response when asked to reason step-by-step. Which technique is designed to address inconsistency by generating multiple reasoning paths and selecting the most consistent answer?

Accepted Answer

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.

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Few-shot prompting

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Temperature tuning

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Tree-of-thought prompting

Question 6

A prompt engineer wants the model to adopt a strict, professional tone for a financial report generation task. Which prompt component should be used to set this tone and persona?

Accepted Answer

System prompt. The system prompt is the correct component because it is specifically designed to set the model's behavior, tone, and persona at the beginning of a conversation. In the context of a financial report generation task, a system prompt like 'You are a professional financial analyst. Use a strict, formal tone.' instructs the model to adopt that persona for all subsequent interactions, ensuring consistency across the entire session.

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Few-shot examples

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User message

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Stop sequences

Question 7

A prompt library manager wants to implement version control for prompt templates used across multiple applications. Which THREE practices should they adopt?

Accepted Answer

Use semantic versioning (e.g., v1.2.3) for prompt templates. Option C is correct because semantic versioning (v1.2.3) gives each prompt template a clear, comparable version identifier so applications can pin or upgrade to specific releases and understand the impact of changes (major/minor/patch). Option D is correct because a changelog records what changed and why, providing an audit trail that helps teams review prompt evolution, roll back problematic changes, and communicate updates across applications. Option E is correct because storing prompt templates in a version control system such as Git provides branching, commit history, diffs, code review, and rollback capabilities, which are the foundation of version control for text artifacts. Option A is not among the correct answers because automated testing, while valuable for quality assurance, is a testing practice rather than a version control practice and does not itself track or manage versions. Option B is not correct because storing prompts only in an application database without history eliminates the ability to track changes, compare revisions, or revert to prior versions, which directly contradicts the goal of implementing version control.

Answer

Automatically test prompts on a fixed set of inputs after each change

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Store prompts only in the application's database without history

Question 8

A small business is building an internal Q&A bot using OCI Generative AI with RAG. They have indexed their product manuals into OCI OpenSearch using a precomputed embedding model. When they test queries, the bot often returns answers that are only partially relevant, and sometimes it cannot find answers for questions that are clearly present in the manuals. The developers suspect the chunking strategy is suboptimal. Currently, they use a fixed chunk size of 512 tokens with no overlap. What should they do to improve retrieval relevance?

Accepted Answer

Add a 20% token overlap between consecutive chunks.. Adding a 20% token overlap between chunks ensures that semantically related content spanning chunk boundaries is not lost, which directly addresses the problem of missing answers for questions present in the manuals. Overlap preserves context continuity, improving retrieval recall and relevance without sacrificing the precision that a fixed-size chunk provides.

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Increase the chunk size to 1024 tokens to include more context.

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Reduce the chunk size to 256 tokens to increase precision.

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Switch to a sentence-based chunking strategy with no overlap.

Question 9

Which of the following best describes the difference between an encoder-only model (e.g., BERT) and a decoder-only model (e.g., GPT)?

Accepted Answer

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.

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Encoder-only is trained for text generation; decoder-only is trained for classification

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Both use the same attention pattern but differ in number of layers

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Encoder-only uses causal attention; decoder-only uses bidirectional attention

Question 10

In the context of LLMs, what is the primary function of tokenization?

Accepted Answer

To split text into manageable pieces (tokens) that the model can understand. Tokenization is the first step in processing text for LLMs, where raw input is split into smaller units called tokens (words, subwords, or characters). This is essential because models like GPT or BERT operate on discrete tokens, not raw strings, and tokenization defines the model's vocabulary and input structure.

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To assign positional encodings to each word

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To convert tokens into dense vector representations

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To remove stop words and punctuation from the input