1Z0-1127-25 OCI Generative AI Service Practice Test 4 — 15 Questions

Question 1

Which OCI Generative AI model is best suited for generating embeddings from text that can be used for semantic search across multiple languages?

Accepted Answer

Cohere embed-multilingual-v3.0. Embed-multilingual-v3.0 is designed for multilingual embedding tasks. The other models are primarily for text generation or English-only embeddings.

Answer

Cohere Command R+

Answer

Meta Llama 3 70B

Answer

Cohere Rerank

Question 2

A data scientist wants to fine-tune a Cohere Command R model using the T-Few technique. They have prepared a dataset in JSONL format with prompt/completion pairs. Which step is REQUIRED before creating the fine-tuning job?

Accepted Answer

Upload the dataset to an OCI Object Storage bucket. The dataset must be uploaded to an OCI Object Storage bucket so the fine-tuning job can access it. The other options are either optional or not required.

Answer

Register the dataset in OCI Data Labeling

Answer

Deploy a dedicated AI cluster to host the base model

Answer

Create an OCI Functions endpoint for dataset preprocessing

Question 3

An organization requires low-latency inference for a custom fine-tuned model deployed on OCI Generative AI. The model must be isolated from other tenants. Which infrastructure choice meets these requirements?

Accepted Answer

Provision a dedicated AI cluster with the required model units. A dedicated AI cluster provides isolated, low-latency inference for custom models. Shared infrastructure does not guarantee isolation and may have higher latency due to contention.

Answer

Use the shared infrastructure endpoint for the fine-tuned model

Answer

Deploy the model on OCI Data Science model deployment

Answer

Use an on-premises GPU server and call OCI GenAI via API gateway

Question 4

In the OCI Generative AI Playground, a developer wants to control how creative the model responses are. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature controls randomness; higher values produce more creative outputs. The other parameters control other aspects of generation.

Answer

Presence penalty

Answer

Max tokens

Answer

Stop sequences

Question 5

A financial services firm needs to ensure that only members of the 'DataScientists' group can use OCI Generative AI resources in the 'prod' compartment. Which IAM policy statement should be applied?

Accepted Answer

Allow group DataScientists to manage genai-family in compartment prod. The 'manage' verb on the 'genai-family' resource type grants full access (including use) to OCI Generative AI resources, and scoping the policy to 'compartment prod' restricts the permission to only that compartment. The requirement is to allow the 'DataScientists' group to use (which implies manage-level access for creating and running inference jobs) Generative AI resources specifically in the 'prod' compartment, not the entire tenancy.

Answer

Allow group DataScientists to manage genai-family in tenancy

Answer

Allow group DataScientists to use ai-services in compartment prod

Answer

Allow group DataScientists to read genai-family in compartment prod

Question 6

A developer uses the OCI Generative AI Chat API to build a multi-turn conversational agent. They notice the model starts to lose context after several exchanges. What is the MOST likely cause?

Accepted Answer

The developer is not sending the conversation history with each request. The Chat API does not automatically manage conversation history; the developer must provide previous messages. The model itself retains state only within the provided context.

Answer

The temperature parameter is set too high, causing the model to forget

Answer

The model's context window is too small for the conversation length

Answer

The fine-tuning dataset did not include multi-turn examples

Question 7

A company is building a document summarization pipeline using OCI Generative AI. They need to summarize thousands of legal documents efficiently. Which approach minimizes cost while maintaining quality?

Accepted Answer

Use the OCI Generative AI Summarisation API with a prebuilt model. The Summarisation API is designed for efficient, cost-effective summarization. Dedicated clusters are expensive for batch workloads, and embedding-based approaches add complexity and cost.

Answer

Fine-tune a model on legal documents and deploy on a dedicated cluster

Answer

Use the Chat API with a system prompt instructing summarization

Answer

Use the Embedding API to vectorize documents and then use a custom summarization algorithm

Question 8

Which OCI Generative AI model would you use to reorder search results to improve relevance ranking?

Accepted Answer

Cohere Rerank. Cohere Rerank is specifically designed to reorder documents based on relevance to a query. The other models are for generation or embedding.

Answer

Cohere Command R+

Answer

Cohere embed-english-v3.0

Answer

Meta Llama 3 70B

Question 9

A team wants to use OCI Generative AI Agents to build a RAG system that answers questions from documents stored in OCI Object Storage. What must they create first?

Accepted Answer

A knowledge base that indexes the documents from Object Storage. The agent requires a knowledge base that is connected to the data source. Directly creating a session without a knowledge base and agent will fail.

Answer

A fine-tuned model on the documents

Answer

A dedicated AI cluster for the agent

Answer

An OCI Functions endpoint to process documents

Question 10

During fine-tuning, a user notices the loss does not decrease after several epochs. The dataset is a JSONL file with 500 prompt/completion pairs. What is the MOST likely cause?

Accepted Answer

The dataset is too small; T-Few fine-tuning generally needs at least 1000 examples. Fine-tuning with T-Few typically requires at least 1000 examples for meaningful learning. The dataset size is likely insufficient.

Answer

The JSONL format is incorrect because it lacks system prompts

Answer

The base model is not compatible with the T-Few technique

Answer

The learning rate is too high, causing the model to diverge

Question 11

An application needs to generate embeddings for customer reviews to cluster them by sentiment. Which input type should be specified in the Embedding API call?

Accepted Answer

clustering. For clustering tasks, the 'clustering' input type optimizes the embeddings for that purpose. The other types are for different tasks.

Answer

search_document

Answer

search_query

Answer

classification

Question 12

A developer is using the OCI Generative AI Chat API to create a customer support bot. They want the bot to maintain a consistent personality and follow specific guidelines. Which TWO settings should they use?

Accepted Answer

System prompt. A system prompt sets the bot's behavior and guidelines, and preamble override allows customizing the model's initial instructions. Temperature and max tokens do not define personality or guidelines.

Answer

Temperature

Answer

Frequency penalty

Answer

Max tokens

Question 13

A company wants to deploy a fine-tuned model for real-time inference with consistent low latency. They are evaluating dedicated AI clusters. Which THREE factors should they consider when provisioning the cluster?

Accepted Answer

Number of clusters (for high availability). Model units determine compute capacity and cost, cluster size affects availability and fault tolerance, and the region impacts data residency and latency. The base model is already chosen, and fine-tuning timeout is irrelevant for inference.

Answer

Fine-tuning job timeout settings

Answer

The base model used for fine-tuning

Question 14

Which TWO OCI Generative AI features are available in the Playground for testing models?

Accepted Answer

Adjusting parameters like temperature and max tokens. The Playground allows interactive testing with parameter adjustment and system prompts. It does not allow fine-tuning or creating dedicated clusters.

Answer

Provisioning a dedicated AI cluster

Answer

Viewing model training metrics

Answer

Submitting a fine-tuning job

Question 15

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

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