1Z0-1127-25 OCI Generative AI Service — Timed 40 Questions

Question 1

A machine learning engineer is fine-tuning a Cohere Command R model using OCI Generative AI. They want to evaluate the fine-tuned model's performance before deploying. Which TWO methods can they use?

Accepted Answer

Use the OCI Generative AI Playground to send test prompts to the fine-tuned model endpoint. The OCI Generative AI Playground provides a user-friendly interface to directly test prompts against a deployed fine-tuned model endpoint, allowing you to evaluate responses interactively without writing code. Option E is correct because the Python SDK's InferenceClient allows programmatic sending of test prompts and analyzing responses, which is ideal for automated evaluation. Option A is incorrect because the fine-tuning job's validation metrics reflect performance during training, not after deployment. Option C is incorrect because monitoring cluster latency measures infrastructure performance, not model accuracy or output quality. Option D is incorrect because while the OCI CLI can call the inference endpoint, it is not primarily used for evaluating model performance; the CLI is more for automation and management tasks, and the question asks for evaluation methods, making the Playground and SDK more appropriate.

Answer

Check the fine-tuning job's status in the OCI Console for validation metrics

Answer

Provision a dedicated AI cluster and monitor the cluster's latency metrics

Answer

Use the OCI CLI to call the model inference endpoint with test data

Question 2

A team fine-tuned a model using T-Few and validated it. They now want to deploy this fine-tuned model to a dedicated AI cluster for low-latency inference. What must they do FIRST?

Accepted Answer

Create a dedicated AI cluster with a specified number of model units. To deploy a fine-tuned model to a dedicated AI cluster for low-latency inference, you must first create the dedicated AI cluster and specify the number of model units. This cluster provides isolated compute resources that ensure consistent, low-latency performance, unlike the shared serverless endpoint. The fine-tuned model is then deployed onto this cluster, not copied to Object Storage first.

Answer

Copy the fine-tuned model to an Object Storage bucket

Answer

Increase the temperature parameter in the inference request

Answer

Delete the base model to free up capacity

Question 3

A machine learning engineer is fine-tuning a Cohere Command R model using OCI Generative AI. They want to evaluate the fine-tuned model's performance before deploying. Which TWO methods can they use?

40 minute time limit — choose an answer to begin.

Accepted Answer

The 'completion' field should contain the expected model response. The T-Few fine-tuning method for Cohere Command R models requires the dataset to include a 'completion' field that contains the expected model response. This field is used as the target output during supervised fine-tuning, where the model learns to generate the desired completion given the input prompt.

Answer

The dataset must include a 'context' field for RAG fine-tuning

Answer

The dataset can be in CSV format with 'input' and 'output' columns

Answer

Each line must include a 'system' key for the system prompt

Question 4

An organization wants to use OCI Generative AI for summarizing long legal documents. Which OCI Generative AI service component is specifically designed for this task?

Accepted Answer

Summarisation. The Summarisation API in OCI Generative AI is a dedicated endpoint optimized for condensing long texts into concise summaries. It uses specialized model configurations and prompt engineering to handle the context window and extraction requirements of legal documents, unlike general-purpose generation endpoints.

Answer

Chat API

Answer

Embedding API

Answer

Generate API

Question 5

An administrator is creating IAM policies for OCI Generative AI. They want to allow a group of developers to use (invoke) models and manage endpoints, but NOT create or delete Dedicated AI Clusters. Which TWO policy statements should be combined?

Accepted Answer

Allow group developers to use generative-ai-family in compartment dev. The 'use' verb on 'generative-ai-family' grants permission to invoke models and perform read-only operations on all Generative AI resources, including endpoints, without allowing create or delete actions on Dedicated AI Clusters. Option E is correct because 'manage' on 'generative-ai-endpoints' allows full control over endpoints (create, update, delete, use) while still not granting any permissions on Dedicated AI Clusters. Together, these two statements give developers the ability to use models and manage endpoints but explicitly exclude create/delete on Dedicated AI Clusters.

Answer

Allow group developers to use generative-ai-clusters in compartment dev

Answer

Allow group developers to manage generative-ai-family in compartment dev

Answer

Allow group developers to manage generative-ai-clusters in compartment dev

Question 6

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. By indexing the documents in a vector store, the system retrieves relevant chunks at query time and passes them as context to a frozen LLM, ensuring the model always uses the most current information.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Answer

Fine-tune a base LLM on the policy documents monthly

Question 7

Which OCI Generative AI service component is specifically designed to convert text into vector representations for use in semantic search and clustering?

Accepted Answer

Embedding. Embedding is the correct answer because it is the OCI Generative AI service component that converts text into dense vector representations (embeddings). These numerical vectors capture semantic meaning, enabling similarity comparisons for tasks like semantic search and clustering, where documents with similar vectors are considered contextually related.

Answer

Chat

Answer

Summarisation

Answer

Agents

Question 8

An organization is deploying a custom fine-tuned model for a real-time fraud detection application. The model must respond within 200ms and cannot share infrastructure with other customers. Which OCI GenAI infrastructure option should they choose?

Accepted Answer

A dedicated AI cluster with provisioned model units. A dedicated AI cluster with provisioned model units ensures exclusive infrastructure, meeting the requirement to not share resources with other customers. This option also provides predictable, low-latency inference (under 200ms) by allocating dedicated compute capacity for the fine-tuned model, which is critical for real-time fraud detection.

Answer

On-demand token-based inference

Answer

Shared infrastructure with reserved capacity

Answer

OCI Functions using the GenAI SDK

Question 9

A developer is using the OCI GenAI Playground to test a summarisation model. They want the summary to be concise and less creative. Which combination of parameter adjustments would best achieve this?

Accepted Answer

Decrease temperature to 0.1 and increase frequency penalty. Decreasing temperature to 0.1 makes the model more deterministic and less creative, while increasing the frequency penalty discourages repetition, both of which help produce a concise, less creative summary. This combination directly aligns with the goal of reducing randomness and controlling output length.

Answer

Set presence penalty to high and temperature to 0.5

Answer

Increase temperature to 0.9 and decrease frequency penalty to 0

Answer

Increase max tokens and set stop sequences

Question 10

A security engineer needs to allow the DataScience group to use OCI Generative AI resources (Chat, Embedding, and Summarisation) in the compartment 'genai-compartment', but not allow them to create dedicated AI clusters. Which IAM policy statement achieves this?

Accepted Answer

Allow group DataScience to use generative-ai-family in compartment genai-compartment and allow group DataScience to inspect dedicated-ai-clusters in compartment genai-compartment. The 'use' verb on generative-ai-family grants access to Chat, Embedding, and Summarization resources, while 'inspect' on dedicated-ai-clusters provides read-only visibility without allowing creation or management of clusters. Option A is incorrect because it grants 'use' on dedicated-ai-clusters, which would allow creating clusters. Option B is incorrect because the condition only denies the 'MANAGE_DEDICATED_AI_CLUSTER' permission but still allows other permissions like 'use' on dedicated-ai-clusters, which could permit cluster creation. Option D is incorrect because it uses an explicit deny on manage, but without an allow for dedicated-ai-clusters, the group would have no access to them at all (not even inspect), and the deny statement is unnecessary and could complicate policy evaluation.

Answer

Allow group DataScience to use generative-ai-family in compartment genai-compartment and allow group DataScience to use dedicated-ai-clusters in compartment genai-compartment

Answer

Allow group DataScience to use generative-ai-family in compartment genai-compartment where request.permission != 'MANAGE_DEDICATED_AI_CLUSTER'

Answer

Allow group DataScience to use generative-ai-family in compartment genai-compartment and deny group DataScience to manage dedicated-ai-clusters in compartment genai-compartment

Question 11

Which input type should be used with the Cohere Embed API when generating embeddings for a query in a semantic search system?

Accepted Answer

search_query. The Cohere Embed API uses the `search_query` input type specifically for embedding search queries in a semantic search system. This tells the model to optimize the embedding for matching against documents that were embedded with the `search_document` type, ensuring proper alignment in the vector space for retrieval tasks.

Answer

search_document

Answer

clustering

Answer

classification

Question 12

Which TWO statements accurately describe the OCI Generative AI Playground? (Choose two.)

Accepted Answer

It supports both Cohere and Meta Llama models. The OCI Generative AI Playground provides access to both Cohere and Meta Llama models, allowing users to experiment with these supported model families interactively. This is a core feature of the Playground, enabling users to test and compare outputs from these models without needing to write code.

Answer

It provides a dashboard to compare outputs from multiple models side-by-side

Answer

It allows you to upload training datasets for fine-tuning

Answer

It can be used to provision and manage dedicated AI clusters

Question 13

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions by retrieving relevant chunks from the policy documents stored in a vector store at inference time, without requiring any model retraining. When documents are updated monthly, you only need to re-index the new content into the vector store, and the LLM can use the retrieved context to generate accurate answers. This decouples knowledge updates from model training, making it cost-effective and agile for frequently changing internal documents.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Question 14

Which OCI Generative AI API should be used to convert a user's query into a vector representation for semantic search?

Accepted Answer

Embedding API. The OCI Generative AI Embedding API is specifically designed to convert text inputs, such as user queries, into dense vector representations (embeddings). These vectors capture semantic meaning and are essential for similarity search in vector databases or retrieval-augmented generation (RAG) pipelines. The other APIs serve different purposes: Chat handles multi-turn conversations, Generate produces text completions, and Rerank reorders documents based on relevance.

Answer

Chat API

Answer

Generate API

Answer

Rerank API

Question 15

During fine-tuning a model in OCI Generative AI, the training loss does not decrease after several epochs. The dataset has 5,000 prompt-completion pairs. What is the MOST likely cause?

Accepted Answer

The dataset format is incorrect (e.g., missing 'prompt' or 'completion' fields). In OCI Generative AI fine-tuning, the dataset must be a JSON file with exactly 'prompt' and 'completion' fields. If these fields are missing or incorrectly named, the service cannot parse the training data, so the model never learns from the actual content, causing the training loss to remain flat across epochs. This is the most direct cause of a non-decreasing loss when the dataset structure is invalid.

Answer

The dataset is too large, causing underfitting

Answer

The dataset contains many duplicate prompt-completion pairs

Answer

The learning rate is too high, causing the loss to oscillate

Question 16

Which OCI Generative AI API should be used to generate a summary of a long legal document?

Accepted Answer

Generate API. The OCI Generative AI Generate API (option D) is specifically designed for text generation tasks, including summarization, by taking a prompt and returning a coherent response. For a long legal document, you would provide the document text as part of the prompt and instruct the model to generate a summary, making the Generate API the correct choice for this use case.

Answer

Embedding API

Answer

Rerank API

Answer

Chat API

Question 17

Which OCI Generative AI service component is specifically designed to convert text into numerical vectors (embeddings) that can be used for semantic search and clustering?

Accepted Answer

Embedding. The Embedding component in OCI Generative AI service is specifically designed to convert text into numerical vectors (embeddings). These embeddings capture semantic meaning, enabling use cases like semantic search and clustering by measuring vector similarity (e.g., cosine similarity).

Answer

Summarisation

Answer

Chat

Answer

Rerank

Question 18

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Train a custom model from scratch on the policy documents each month

Question 19

You need to convert a set of customer support tickets into vector embeddings for a similarity search application. Which OCI Generative AI model should you use?

Accepted Answer

Cohere Embed (e.g., embed-english-v3.0). The Cohere Embed models are designed for text-to-vector embedding. The other options are for text generation or reranking.

Answer

Cohere Rerank

Answer

Meta Llama 3

Answer

Cohere Command R

Question 20

A data scientist has fine-tuned a Cohere Command R model using the T-Few technique. They now need to deploy this custom model for real-time inference with low latency. What is the recommended deployment option in OCI Generative AI?

Accepted Answer

Provision a dedicated AI cluster and host the fine-tuned model on it. Dedicated AI clusters provide isolated, low-latency inference for custom fine-tuned models. Shared infrastructure is multi-tenant and may have variable latency; on-demand inference does not support custom models directly.

Answer

Use the OCI Generative AI Playground to test the model

Answer

Use the shared infrastructure endpoint with an API call

Answer

Create an InferenceClient pointing to the fine-tuned model directly without a cluster