1Z0-1127-25 OCI Generative AI Service Practice Test 5 — 15 Questions

Question 1

Which OCI Generative AI model family is specifically designed to convert text into vector embeddings for semantic search and clustering tasks?

Accepted Answer

Cohere Embed. Cohere Embed models (e.g., embed-english-v3.0, embed-multilingual-v3.0) are explicitly designed for generating text embeddings. Cohere Command R and R+ are for generation, Meta Llama 3 is a general-purpose LLM, and Cohere Rerank is for re-ranking search results.

Answer

Meta Llama 3

Answer

Cohere Command R

Answer

Cohere Rerank

Question 2

A data scientist is using the OCI Generative AI Playground to test a summarization model. They want to generate shorter summaries and avoid repetitive phrasing. Which parameter adjustments should they make?

Accepted Answer

Decrease temperature and increase frequency penalty. Lowering temperature reduces randomness, increasing frequency penalty penalizes repeated tokens, and setting appropriate max tokens controls output length. Stop sequences define when generation stops, and presence penalty penalizes token presence overall.

Answer

Increase temperature and increase presence penalty

Answer

Set stop sequences and disable frequency penalty

Answer

Increase temperature and decrease frequency penalty

Question 3

An organization needs to deploy a fine-tuned model for real-time inference with strict latency requirements. They have provisioned a Dedicated AI Cluster with 2 model units. Which statement about this setup is accurate?

Accepted Answer

The cluster provides low-latency, dedicated inference for the fine-tuned model, and you are billed per model unit.. Dedicated AI Clusters provide low-latency dedicated inference, and model units are used to allocate capacity for running models, including custom fine-tuned ones. The cluster is specifically designed for hosting fine-tuned models with consistent performance.

Answer

You must use the shared infrastructure for fine-tuned models; dedicated clusters are only for base models.

Answer

The cluster automatically scales model units based on request load.

Answer

The cluster can only host OCI’s built-in models, not custom fine-tuned models.

Question 4

A developer is fine-tuning a Cohere Command R model using OCI Data Science and the T-Few technique. They have prepared a dataset. What is the required format for the training data?

Accepted Answer

A JSONL file where each line contains a 'prompt' and a 'completion' field. OCI Generative AI fine-tuning expects a JSONL file where each line is a JSON object containing a prompt and completion (or response) field. This format pairs input with expected output for supervised fine-tuning.

Answer

A CSV file with columns 'input' and 'output'

Answer

A Parquet file with 'text' and 'label' columns

Answer

A plain text file with one conversation per line

Question 5

A security administrator needs to grant a group of data scientists the ability to use OCI Generative AI service resources in the compartment 'genai-dev'. They want to allow the group to create endpoints and run inference, but not to manage IAM policies. Which policy statement is correct?

Accepted Answer

Allow group DataScientists to use generative-ai-family in compartment genai-dev. The 'use' verb in OCI IAM policies grants the ability to perform operational actions on resources without allowing management or administrative changes. For the OCI Generative AI service, 'use generative-ai-family' permits data scientists to create endpoints and run inference (the core actions needed), while explicitly preventing them from managing IAM policies or altering resource configurations. This aligns with the principle of least privilege required by the security administrator.

Answer

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

Answer

Allow group DataScientists to inspect generative-ai-family in compartment genai-dev

Answer

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

Question 6

During a fine-tuning job for a text generation model, the loss curve shows that the training loss decreases steadily, but the evaluation loss increases after a few epochs. Which action is most likely to improve the model's generalization?

Accepted Answer

Implement early stopping based on evaluation loss. Increasing evaluation loss while training loss decreases indicates overfitting. Early stopping prevents further training once evaluation metrics plateau or degrade, improving generalization. Reducing dataset size or increasing epochs would worsen overfitting; increasing temperature does not affect training.

Answer

Increase the number of training epochs

Answer

Reduce the size of the training dataset

Answer

Increase the temperature parameter during generation

Question 7

Which OCI Generative AI API is used to send a message and receive a model-generated response while maintaining a conversation history?

Accepted Answer

Chat API. The Chat API is specifically designed for multi-turn conversations, supporting system prompts and history. The Generate API is for single-turn text generation. InferenceClient is a client class, not an API endpoint. The Embeddings API is for vector generation.

Answer

InferenceClient

Answer

Embeddings API

Answer

Generate API

Question 8

A team is building a multilingual semantic search application. They need to index documents in English, Spanish, and French, and later search using queries in any of these languages. Which embedding model should they use?

Accepted Answer

Cohere embed-multilingual-v3.0. Cohere embed-multilingual-v3.0 supports multiple languages in a single model, enabling cross-lingual semantic search. embed-english-v3.0 is English-only. Command R and Llama 3 are not embedding models.

Answer

Meta Llama 3 70B

Answer

Cohere Command R

Answer

Cohere embed-english-v3.0

Question 9

A developer is using the OCI Generative AI Agents service to build a RAG application. They have uploaded policy PDFs to an OCI Object Storage bucket. What is the next step to make the documents searchable?

Accepted Answer

Create a knowledge base from the Object Storage bucket. In OCI Generative AI Agents, you create a knowledge base from data sources like Object Storage. The agent then uses this knowledge base to retrieve relevant information for answering questions. Creating a dedicated cluster or fine-tuning is not required for RAG with agents.

Answer

Create a dedicated AI cluster to host the documents

Answer

Use the Embeddings API to embed each PDF manually

Answer

Fine-tune a model on the PDFs

Question 10

An organization wants to use OCI Generative AI for a high-volume summarization workload. They estimate 10 million tokens per month and need consistent low latency. Which pricing model is most cost-effective?

Accepted Answer

Provision a Dedicated AI Cluster with model units. On-demand pricing can be expensive at high volumes. Dedicated clusters offer predictable cost per model unit and low-latency dedicated inference, making them more cost-effective for high-volume, latency-sensitive workloads. Pay-as-you-go (on-demand) is suitable for low or variable usage.

Answer

Use the free tier

Answer

Use OCI Data Science notebook sessions

Answer

On-demand token-based pricing

Question 11

Which statement accurately describes the T-Few fine-tuning technique used in OCI Generative AI?

Accepted Answer

It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.. The T-Few fine-tuning technique is a parameter-efficient fine-tuning (PEFT) method that updates only a small fraction of the model's parameters, typically by introducing and training adapter layers or using low-rank updates. This approach significantly reduces computational and memory requirements compared to full fine-tuning, making it suitable for adapting large language models with limited resources. In OCI Generative AI, T-Few enables efficient customization without retraining the entire model.

Answer

It automatically adjusts hyperparameters during inference.

Answer

It does not require any training data and works by prompting only.

Answer

It updates all model parameters, requiring substantial compute resources.

Question 12

A company wants to implement a retrieval-augmented generation (RAG) chatbot using OCI Generative AI Agents. Which TWO services or components are required for this solution?

Accepted Answer

Knowledge Base. A Knowledge Base is the core repository that stores the documents or data sources the RAG chatbot retrieves from. OCI Generative AI Agents use a knowledge base to perform retrieval-augmented generation, where the agent first retrieves relevant chunks from the knowledge base and then passes them to the generative model to produce a grounded, context-aware response.

Answer

Cohere Embed model

Answer

Fine-tuned model

Answer

Dedicated AI Cluster

Question 13

A developer is building a multi-turn chatbot using the OCI Generative AI Chat API. Which THREE parameters or features should they configure to maintain coherent conversation history?

Accepted Answer

System prompt. System prompt sets the assistant's behavior. Preamble override can customize the initial context. Multi-turn conversation history is managed by sending previous messages in the API call. Temperature and stop sequences affect generation but not history management.

Answer

Temperature

Answer

Stop sequences

Question 14

A data scientist is using the OCI Generative AI Embeddings API to generate vectors for a classification task. Which TWO input types are appropriate for this use case?

Accepted Answer

clustering. The Embeddings API supports input types like 'classification' and 'clustering' which optimize embeddings for those tasks. 'search_document' and 'search_query' are for search. 'text' is not a valid input type.

Answer

search_query

Answer

text

Answer

search_document

Question 15

An OCI Generative AI user wants to host a fine-tuned model with guaranteed low latency for a production application. Which option should they choose?

Accepted Answer

Provision a Dedicated AI Cluster with model units for the fine-tuned model. A Dedicated AI Cluster provides dedicated inference capacity with low latency, ideal for production fine-tuned models. Shared infrastructure may have variable latency.

Answer

Use the Chat API with a base model

Answer

Use the on-demand shared inference endpoint

Answer

Use the OCI Generative AI Playground