1Z0-1127-25 Practice Test 6 — 10 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