1Z0-1127-25 Practice Test 3 — 30 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

Question 11

A team is building a system to detect duplicate customer support tickets. They have a dataset of 10,000 resolved tickets and want to find pairs with similar intent. Which approach would be MOST efficient and effective?

Accepted Answer

Embed each ticket using an embedding model and compute cosine similarity between all pairs. Embedding each ticket into a dense vector space and computing cosine similarity between all pairs is both efficient and effective for detecting duplicate intents. This approach leverages pre-trained embedding models (e.g., sentence-transformers) that capture semantic similarity, and pairwise cosine similarity scales well for 10,000 tickets (approximately 50 million comparisons) using optimized matrix operations. It avoids the quadratic cost of LLM inference per pair while preserving high accuracy for intent matching.

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Use a long-context LLM to process all tickets in a single prompt

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Use a generation model like Cohere Command to compare tickets one pair at a time

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Fine-tune a generation model on a classification task to predict duplicates

Question 12

Which component of the transformer architecture allows the model to weigh the importance of different words in a sentence when processing input?

Accepted Answer

Self-attention mechanism. The self-attention mechanism is the core component of the transformer architecture that enables the model to dynamically assign weights to each word in a sentence relative to every other word. This allows the model to capture contextual relationships and dependencies, such as determining which words are most relevant to the current word being processed, regardless of their distance in the sequence.

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Layer normalization

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Positional encoding

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Feed-forward neural network

Question 13

A company wants to build a multi-modal RAG system that can retrieve both text and images based on a user query. Which approach is most aligned with OCI GenAI capabilities?

Accepted Answer

Utilize a multi-modal embedding model from OCI GenAI to embed both text and images into a common vector space. OCI GenAI supports multi-modal models like Cohere's multimodal embedding model, which can embed text and images into a shared vector space, enabling retrieval across modalities. Separate text and image models would not align the vectors. OCR-based text-only approach loses image semantics. Using multiple vector stores complicates retrieval.

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Use OCI Document Understanding to convert images to text, then index text

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Use separate vector stores for text and image embeddings

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Use image captioning to generate text descriptions and index those

Question 14

A data scientist is using OCI Data Science to build a RAG system for medical literature. They have a large corpus of PDFs. They used the default OCI Generative AI embedding model and chunked each PDF into 512-character segments with 10% overlap. However, queries about specific drug doses often return incorrect information, even though the correct dose is present in the corpus. Upon inspection, they find that the retrieved chunks often contain partial dose information or miss the context units (e.g., mg vs. mcg). What improvement should they prioritize?

Accepted Answer

Use a semantic chunking strategy that respects document structure (e.g., paragraphs, sections).. The core issue is that fixed-size character chunking (512 characters) breaks medical documents mid-sentence or mid-section, causing critical context like units (mg vs. mcg) to be separated from the numeric dose. Semantic chunking that respects document structure (paragraphs, sections) ensures that dose information and its units remain in the same chunk, preserving the semantic integrity needed for accurate retrieval. This directly addresses the observed problem of partial dose information and missing context units.

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Implement a secondary verification step using a rule-based pattern matcher.

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Increase the chunk overlap to 50% to ensure more context.

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Fine-tune the embedding model on medical text.

Question 15

A developer is using the OCI Generative AI service and notices that the cost per API call is higher than expected. Which factor contributes MOST to the cost of an LLM inference call?

Accepted Answer

Number of input and output tokens. Most LLM APIs charge based on the number of input and output tokens. The token count directly affects cost. Model size, context window, and temperature are related but the direct billing metric is token count.

Answer

Model size (number of parameters)

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Context window size

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Temperature setting

Question 16

A developer wants to use the OCI Generative AI service to generate text using a Cohere model. Which SDK class should be used for inference calls?

Accepted Answer

GenerativeAiInferenceClient. The correct SDK class for inference calls with Cohere models in OCI Generative AI is `GenerativeAiInferenceClient`. This class is specifically designed to handle inference operations such as text generation, summarization, and chat completions, and it supports both Cohere and Meta Llama models. It provides methods like `generate_text`, `summarize_text`, and `chat` that directly interact with the inference endpoints.

Answer

CustomModelClient

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AiInferenceClient

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GenerativeAiClient

Question 17

Which TWO are advantages of using retrieval-augmented generation (RAG) over fine-tuning for incorporating new knowledge?

Accepted Answer

Enables the model to access up-to-date information without retraining. Option B is correct because RAG decouples knowledge from model weights: at inference time the system retrieves relevant documents from an external index and injects them into the prompt, so the model can answer with current information without any retraining or weight updates. Option E is correct because RAG only requires updating the underlying document store/vector index when the corpus changes, avoiding the expensive GPU compute and repeated training runs that fine-tuning would demand for large, frequently changing corpora. Option A is not correct because domain-specific writing style is typically learned through fine-tuning on in-domain text, not through retrieval of facts. Option C is not correct because RAG actually depends on a vector database (or similar retrieval index) to embed and search documents. Option D is not correct because RAG adds a retrieval step and inserts retrieved passages into the context, which generally increases token usage and latency rather than reducing them.

Answer

Better at capturing domain-specific writing style

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Eliminates the need for a vector database

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Reduces token usage and latency compared to fine-tuning

Question 18

When invoking the OCI Generative AI service from a RAG application, the developer receives a 401 Unauthorized error. The application uses resource principal authentication from an OCI Data Science notebook session. What is the most likely fix?

Accepted Answer

Ensure the dynamic group includes the data science notebook session and has the correct policy. The 401 Unauthorized error indicates that the OCI Generative AI service cannot authenticate the request. In a RAG application using resource principal authentication from an OCI Data Science notebook session, the notebook session must be a member of a dynamic group, and that dynamic group must have an IAM policy granting it the necessary permissions (e.g., `allow dynamic-group to use generative-ai-family in compartment `). Option C correctly identifies that the dynamic group configuration or policy is missing or incorrect, which is the most likely cause of the authentication failure.

Answer

Add the Generative AI service to the subnet's security list

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Use an API key instead of resource principal

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Restart the notebook session

Question 19

A company wants to build a sentiment analysis system for customer reviews. They have a labeled dataset of 10,000 reviews. Which approach is most cost-effective and likely to yield good performance?

Accepted Answer

Fine-tune a pre-trained BERT model on the labeled dataset. Fine-tuning a pre-trained encoder-only model like BERT on the labeled dataset is a standard approach for classification tasks, offering good performance with relatively modest data and compute.

Answer

Use GPT-4 with a prompt and no fine-tuning

Answer

Use a simple bag-of-words model with logistic regression

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Train a Transformer model from scratch on the reviews

Question 20

A team has set up a RAG pipeline using OCI Data Science with OCI OpenSearch as the vector store. The embedding model is from the OCI Generative AI service. Users note that the vector search returns irrelevant documents for many queries. Which of the following is the most likely cause?

Accepted Answer

The query is not being converted to an embedding before search. In a RAG pipeline, the user's query must be converted into an embedding vector using the same embedding model that was used to embed the documents. If the query is sent as raw text to OpenSearch, it will perform a lexical (keyword) search instead of a vector similarity search, returning irrelevant results. OCI Data Science and OpenSearch require explicit embedding of the query before invoking the k-NN search endpoint.

Answer

The chunk size is too large, causing overlapping context

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The OpenSearch cluster is too small to handle the load

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The embedding model dimension does not match OpenSearch index dimension