1Z0-1127-25 OCI Generative AI Service Practice Test 6 — 15 Questions

Question 1

A data scientist is fine-tuning a model using T-Few in OCI Generative AI. They have prepared a dataset with prompt/completion pairs. Which file format is required for the training data upload?

Accepted Answer

JSONL with each line containing 'prompt' and 'completion' keys. OCI Generative AI fine-tuning expects training data in JSONL format with specific fields.

Answer

Plain text file with one prompt-completion pair per line separated by a tab

Answer

Parquet file with a 'text' column containing concatenated prompt and completion

Answer

CSV with columns 'input' and 'output'

Question 2

A company needs to generate vector embeddings for a multilingual document set to support semantic search across English and French documents. Which embedding model should they use?

Accepted Answer

Cohere embed-multilingual-v3.0. The embed-multilingual-v3.0 model supports multiple languages, including English and French.

Answer

Cohere embed-english-v3.0

Answer

Cohere Command R

Answer

Meta Llama 3 8B

Question 3

An administrator wants to grant a group of data scientists permission to use OCI Generative AI resources in a specific compartment, but prevent them from creating Dedicated AI Clusters. Which IAM policy statement achieves this?

Accepted Answer

Allow group data-scientists to use generative-ai-family in compartment genai-dev where request.operation != 'CreateDedicatedAiCluster'. It uses the 'use' verb to grant the data scientists access to OCI Generative AI resources while adding a condition with 'request.operation != 'CreateDedicatedAiCluster'' to explicitly deny the ability to create Dedicated AI Clusters. In OCI IAM, the 'use' verb includes read and update capabilities but not create or delete, and the condition further restricts the specific create operation, aligning with the requirement to prevent cluster creation.

Answer

Allow group data-scientists to read generative-ai-family in compartment genai-dev

Answer

Allow group data-scientists to manage generative-ai-family in compartment genai-dev

Answer

Allow group data-scientists to use generative-ai-models in compartment genai-dev

Question 4

A developer is using the OCI Generative AI Chat API to build a conversational assistant. They want the assistant to adopt a formal tone regardless of user input. Which parameter should they set in the API request?

Accepted Answer

Set the system prompt to 'You are a formal assistant that responds in a professional tone.'. The system prompt (or preamble) sets the assistant's behavior consistently. Temperature affects randomness but not tone directly.

Answer

Set a high temperature (e.g., 0.9)

Answer

Set the frequency penalty to a high value

Answer

Set max tokens to a low value

Question 5

A user wants to quickly test different prompts and parameters (temperature, max tokens) with various OCI Generative AI models without writing any code. Which tool should they use?

Accepted Answer

The OCI Generative AI Playground. The OCI Generative AI Playground provides an interactive web UI to test models and adjust parameters.

Answer

The InferenceClient SDK in Python

Answer

OCI CLI with generative-ai commands

Answer

OCI Cloud Shell

Question 6

A company wants to use OCI Generative AI Agents to build a RAG application over documents stored in OCI Object Storage. What must they create first?

Accepted Answer

A knowledge base linked to the Object Storage bucket. OCI Generative AI Agents require a knowledge base to index data sources before creating an agent.

Answer

A Dedicated AI Cluster

Answer

An embedding endpoint

Answer

A fine-tuning job for the base model

Question 7

During fine-tuning a model using T-Few in OCI Generative AI, the job fails with a 'dataset format error'. The training dataset is a JSONL file. Which of the following is the MOST likely cause?

Accepted Answer

Some lines have an extra 'metadata' key. Each JSONL line must contain exactly 'prompt' and 'completion' keys. Extra keys or missing keys cause format errors.

Answer

The file contains more than 1000 lines

Answer

The completion field contains more than 1024 tokens

Answer

The file uses Unix line endings

Question 8

A developer needs to generate embeddings for a set of search queries to be used in a semantic search system. Which input type should they specify when calling the Embedding API?

Accepted Answer

search_query. The Embedding API in OCI Generative AI requires specifying the input type as 'search_query' when generating embeddings for search queries in a semantic search system. This input type optimizes the embedding model to produce vectors that are specifically tuned for query-side representation, ensuring better alignment with document embeddings during similarity search.

Answer

clustering

Answer

search_document

Answer

classification

Question 9

Which OCI Generative AI model is designed to rerank and improve the relevance of documents retrieved by a search system?

Accepted Answer

Cohere Rerank. Cohere Rerank is specifically designed for reranking documents to improve relevance ranking.

Answer

Cohere Embed

Answer

Cohere Command R

Answer

Meta Llama 3

Question 10

A team fine-tuned a Cohere Command R model in OCI GenAI and validated it. They now need to deploy it for production inference with a dedicated endpoint. What is the correct sequence of steps?

Accepted Answer

Provision a Dedicated AI Cluster, deploy the model to the cluster, then create an endpoint. In OCI Generative AI, the correct sequence for deploying a fine-tuned model to a dedicated endpoint is: first provision a Dedicated AI Cluster (which provides the isolated compute infrastructure), then deploy the model to that cluster, and finally create an endpoint that exposes the deployed model for inference. This ensures the model is hosted on dedicated resources before the endpoint is created.

Answer

Create an endpoint, deploy the model, then provision a Dedicated AI Cluster

Answer

Deploy the model to a shared endpoint, then provision a Dedicated AI Cluster for scaling

Answer

Create an endpoint, then provision a Dedicated AI Cluster, then deploy the model

Question 11

A data scientist is configuring a fine-tuning job in OCI Generative AI. Which TWO of the following are required inputs for creating the job?

Accepted Answer

Training dataset (JSONL file). Fine-tuning requires a base model and a training dataset. The other options are optional or configurable later.

Answer

Temperature setting for fine-tuning

Answer

Max tokens limit for validation

Answer

Inference endpoint name

Question 12

A company wants to use OCI Generative AI Agents to create a RAG-powered customer support system. Which THREE components are essential for the agent to work?

Accepted Answer

A knowledge base. OCI Generative AI Agents require a knowledge base, a data source (like Object Storage), and a base LLM to generate responses. The other options are optional.

Answer

A Dedicated AI Cluster

Answer

A fine-tuned embedding model

Question 13

A data scientist needs to fine-tune a model using OCI Generative AI. They have prepared a dataset in JSONL format with prompt/completion pairs. The fine-tuning job is configured with the T-Few technique. What is a key characteristic of T-Few fine-tuning?

Accepted Answer

It modifies a small subset of parameters using lightweight adapter layers. T-Few is a parameter-efficient fine-tuning method that updates only a small fraction of model parameters, making it faster and more resource-efficient than full fine-tuning. It does not train all parameters, add new layers, or require unlabeled data.

Answer

It requires unlabeled data for unsupervised pre-training before fine-tuning

Answer

It updates all model parameters, requiring significant compute resources

Answer

It only trains a new classification head on top of a frozen base model

Question 14

A team wants to use OCI Generative AI Agents to build a question-answering system over documents stored in OCI Object Storage. They have created a knowledge base and are ready to test. Which API should they use to interact with the agent for multi-turn conversations?

Accepted Answer

Sessions API. The Sessions API is used to manage conversational sessions with OCI GenAI Agents, allowing multi-turn interactions. The Chat API is for direct LLM chat without agent capabilities. The Generate API is for single-turn text generation, and the Embedding API is for vector creation.

Answer

Chat API

Answer

Embedding API

Answer

Generate API

Question 15

Which of the following is NOT an available model in OCI Generative AI service?

Accepted Answer

OpenAI GPT-4. OCI GenAI offers Cohere Command R, Command R+, Meta Llama 3, Cohere Embed models, and Cohere Rerank. GPT-4 is an OpenAI model and is not available in OCI GenAI.

Answer

Cohere Embed English v3.0

Answer

Meta Llama 3

Answer

Cohere Command R+