1Z0-1127-25 Practice Test 3 — 25 Questions

Question 1

The job fails with "InvalidParameter: trainingDatasetUri". What should the administrator check first?

Accepted Answer

Whether the bucket exists and the file is accessible.. The error 'InvalidParameter: trainingDatasetUri' indicates that the URI provided for the training dataset is invalid or inaccessible. In OCI Generative AI service, the training dataset must be stored in an OCI Object Storage bucket, and the URI must point to an existing, readable file. Therefore, the administrator should first verify that the bucket exists and the file is accessible, as this is the most common cause of this error.

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Whether the compartment has sufficient budget.

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Whether the model ID supports fine-tuning.

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Whether the parameters JSON is correctly formatted.

Question 2

A retail company wants to generate product descriptions from attribute data. They have no prior AI experience. Which approach is most appropriate?

Accepted Answer

Use the Cohere Command model with carefully crafted prompts.. The Cohere Command model is specifically designed for text generation tasks like creating product descriptions from attribute data. With no prior AI experience, using a pre-trained model with carefully crafted prompts is the most efficient and accessible approach, requiring no custom training or complex infrastructure.

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Train a custom model from scratch.

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Fine-tune a model on a synthetic dataset.

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Use the Cohere Embed model to generate embeddings and then decode.

Question 3

A data scientist fine-tuned a model on OCI Gen AI using a dedicated AI cluster. After deployment, the model gives inaccurate results. Which troubleshooting step should they take first?

Accepted Answer

Check the training data for bias or quality issues. The most common cause of inaccurate results after fine-tuning is poor training data quality or bias. Before adjusting infrastructure or switching models, you must validate the dataset for issues like label errors, class imbalance, or sampling bias, as these directly degrade model performance regardless of compute resources.

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Switch to a different base model

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Increase the cluster size

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Use a serverless endpoint

Question 4

Which LangChain component is responsible for splitting long documents into smaller, overlapping chunks before embedding?

Accepted Answer

Text Splitter. The Text Splitter is the LangChain component whose sole job is to break long documents into smaller, often overlapping chunks before they are embedded and stored. It sits between the Document Loader and the embedding/vector-store step in a RAG pipeline. Splitting with overlap preserves context across chunk boundaries so retrieval returns coherent passages.

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Retriever

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Vector Store

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Document Loader

Question 5

A developer is using OCI Generative AI service's CLI to generate text but gets a 'Rate limit exceeded' error. What is the most likely cause?

Accepted Answer

The number of requests per minute exceeds the allowed quota.. The 'Rate limit exceeded' error indicates that the client has sent more requests than the allowed quota within a specific time window. OCI Generative AI service enforces rate limits (e.g., requests per minute) to protect backend resources and ensure fair usage. When the developer exceeds this threshold, the service returns a 429 HTTP status code with the 'Rate limit exceeded' message.

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The API key is invalid.

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The model is not available in that region.

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The input text is too long.

Question 6

A data scientist receives an error when calling the embed_text API: "InvalidRequest: input too long". What is the most likely cause and solution?

Accepted Answer

The input text exceeds the maximum token limit for the model; truncate the input.. The error 'InvalidRequest: input too long' indicates that the input text exceeds the maximum token limit for the embedding model. OCI Generative AI embedding models, like all transformer-based models, have a fixed context window (e.g., 512 or 1024 tokens). The solution is to truncate the input to fit within that limit, as the API will reject overly long inputs.

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The model specified is not supported for embeddings; use a different model.

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The API request rate exceeds the tenancy limit; reduce the request rate.

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The API key is invalid or expired; regenerate the key.

Question 7

Which OCI Generative AI capability allows you to provide example input-output pairs to guide the model's behavior without fine-tuning?

Accepted Answer

Few-shot learning. Few-shot learning is the correct answer because it allows you to provide a small number of example input-output pairs directly in the prompt to guide the model's behavior for a specific task, without updating the model's weights through fine-tuning. In OCI Generative AI, this technique leverages the model's in-context learning ability to adapt its responses based on the provided examples, making it ideal for quick task adaptation without the cost or complexity of retraining.

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Reinforcement Learning from Human Feedback (RLHF)

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Prompt engineering

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Fine-tuning

Question 8

An application uses RAG to answer customer queries, but answers are often incomplete because the retrieved chunks do not contain full context. Which adjustment should the developer make?

Accepted Answer

Increase the number of retrieved chunks. Increasing the number of retrieved chunks (C) directly addresses incomplete answers by providing more context from the vector database. With RAG, the retriever fetches the top-k chunks most similar to the query; raising k increases the breadth of information passed to the LLM, reducing the chance that critical context is missing. This is the most straightforward fix when chunks themselves are correctly sized but the retrieved set lacks full coverage.

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Use a different embedding model

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Increase chunk overlap

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Decrease chunk size

Question 9

An enterprise wants to use OCI Generative AI to generate personalized email campaigns. They have a large customer database with preferences and past purchase history. Which design is best for high relevance and scalability?

Accepted Answer

Use a pipeline: retrieve relevant customer data and inject into prompt.. It implements a retrieval-augmented generation (RAG) pattern, which is the recommended approach for personalizing outputs at scale in OCI Generative AI. By retrieving only the relevant customer data (e.g., recent purchases, preferences) and injecting it into the prompt, you keep the context window efficient, reduce token costs, and avoid exceeding model limits. This design also allows the model to generate unique, context-aware emails without the overhead of fine-tuning per customer or the rigidity of rule-based systems.

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Use a single prompt with all customer details as context.

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Fine-tune a model on each customer's history separately.

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Use a rule-based engine with AI-generated templates.

Question 10

Refer to the exhibit. The dashboard shows latency grouped by modelId, but some points are missing for certain modelIds. Which of the following is the most likely reason?

Accepted Answer

The modelIds with missing data may have been deleted or are inactive. In OCI's Generative AI service, model deployments are associated with specific modelIds. If a modelId is deleted or its deployment is deactivated, the corresponding telemetry data (e.g., latency metrics) will no longer be reported, causing gaps in the dashboard. The dashboard aggregates metrics only for active modelIds, so missing points indicate that those modelIds are no longer in service.

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The metric name is misspelled

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The aggregation interval is too short

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The compartmentId is incorrect

Question 11

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?

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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