1Z0-1127-25 OCI Generative AI Service Practice Test 3 — 15 Questions

Question 1

A developer is building a RAG application using OCI Generative AI Agents. They want to ensure the agent only retrieves information from approved documents in a specific compartment. Which THREE steps are required?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create a knowledge base that references the Object Storage bucket. IAM policies restrict access to data, placing documents in the correct compartment, and creating a knowledge base pointing to that compartment are required. Creating a dedicated cluster and using the Embedding API directly are not necessary steps for the agent.

Answer

Provision a dedicated AI cluster in the same compartment

Answer

Use the Embedding API to manually generate embeddings for each document

Question 2

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

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

Answer

Fine-tune a base LLM on the policy documents monthly

Question 3

A data scientist needs to create vector embeddings for a multilingual customer feedback dataset to perform clustering analysis. Which OCI Generative AI embedding model should they choose?

Accepted Answer

Cohere embed-multilingual-v3.0. The embed-multilingual-v3.0 model is designed for multilingual content, making it the best choice for a multilingual dataset. The other options are either English-only or not embedding models.

Answer

Cohere Command R+

Answer

Cohere embed-english-v3.0

Answer

Meta Llama 3 70B

Question 4

What is the primary benefit of using a Dedicated AI Cluster for inference in OCI Generative AI?

Accepted Answer

Higher throughput and lower latency due to dedicated compute resources. A Dedicated AI Cluster provides exclusive, low-latency inference with reserved capacity, unlike shared infrastructure where resources are contended.

Answer

Ability to use any LLM model for free

Answer

Automatic model fine-tuning on the cluster

Answer

No need to create endpoints

Question 5

A team fine-tuned a Cohere Command R model using the T-Few technique on a dataset of JSONL prompt/completion pairs. After deployment, they observe that the model's responses are too repetitive. Which parameter adjustment in the OCI Generative AI Playground would BEST address this issue?

Accepted Answer

Increase frequency penalty. Increasing the frequency penalty discourages the model from repeating tokens that have already appeared, reducing repetitive outputs. Temperature and max tokens address randomness and length, not repetition specifically. Stop sequences are for terminating generation early.

Answer

Increase max tokens

Answer

Decrease temperature

Answer

Add a stop sequence

Question 6

An organization wants to allow its data science group to use OCI Generative AI services but restrict access to a specific compartment. Which IAM policy statement correctly achieves this?

Accepted Answer

allow group data-scientists to use generative-ai-family in compartment GenAI-Prod. The 'allow group to use generative-ai-family in compartment ' policy grants access to all GenAI resources within that compartment. The other options either miss the compartment scope or use incorrect verbs.

Answer

allow group data-scientists to manage generative-ai-family in tenancy

Answer

allow group data-scientists to read generative-ai-family in compartment GenAI-Prod

Answer

allow group data-scientists to use generative-ai-family where target.compartment.id = 'GenAI-Prod'

Question 7

A developer is using the OCI Generative AI Chat API to build a multi-turn conversational assistant. They want the assistant to adopt a formal tone throughout the conversation. Which parameter should they set in the API request to achieve this?

Accepted Answer

preamble_override. The 'preamble_override' parameter sets the system-level instruction (e.g., 'You are a formal assistant...') that persists across turns. The other options control generation statistics, not system behavior.

Answer

temperature

Answer

frequency_penalty

Answer

max_tokens

Question 8

Which OCI Generative AI model is specifically designed to generate embeddings for English text?

Accepted Answer

Cohere embed-english-v3.0. Cohere embed-english-v3.0 is the embedding model for English text. The other options are either generative models or multilingual embedding models.

Answer

Meta Llama 3 8B

Answer

Cohere embed-multilingual-v3.0

Answer

Cohere Command R

Question 9

A user wants to test different prompt variations with a generative model interactively without writing code. Which OCI Generative AI tool should they use?

Accepted Answer

OCI Generative AI Playground. The OCI Generative AI Playground provides a no-code interface to test models, adjust parameters, and experiment with prompts interactively.

Answer

OCI Generative AI API with InferenceClient

Answer

OCI Data Science Notebook

Answer

OCI Generative AI Agents

Question 10

An organization needs to fine-tune a Cohere Command R model for a custom domain. They have prepared a dataset in JSONL format. Which component of the fine-tuning job specifies the base model and the training dataset location?

Accepted Answer

The fine-tuning job creation request body. The fine-tuning job creation request includes parameters such as 'baseModelId' and 'trainingDataset' (pointing to the dataset in Object Storage). The model deployment endpoint and cluster are separate steps.

Answer

The dedicated AI cluster settings

Answer

The T-Few configuration file

Answer

The model deployment endpoint configuration

Question 11

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

Accepted Answer

Embedding API. The Embedding API generates vector embeddings from text, which can be used for semantic search, clustering, etc.

Answer

Chat API

Answer

Summarisation API

Answer

Generate API

Question 12

A company wants to build a RAG-based assistant that answers queries using documents stored in OCI Object Storage. Which OCI Generative AI service should they use?

Accepted Answer

OCI Generative AI Agents. OCI Generative AI Agents provides a managed RAG service that can ingest documents from Object Storage and answer questions based on the ingested knowledge base.

Answer

OCI Generative AI Playground

Answer

OCI Generative AI Embedding API

Answer

OCI Generative AI Chat API

Question 13

A data scientist needs to generate embeddings for a collection of documents to be used for both clustering and semantic search. They want to use appropriate input types for each task. Which TWO input types should they use from the Cohere Embed API? (Choose two.)

Accepted Answer

search_document. For clustering, the 'clustering' input type is appropriate. For semantic search, 'search_document' (for documents to be searched) and 'search_query' (for queries) are used. The question asks for two options that cover both tasks; 'clustering' and 'search_document' are correct. 'search_query' is for queries, not documents, and 'classification' is for classification tasks.

Answer

search_query

Answer

embedding

Answer

classification

Question 14

A team is planning to use OCI Generative AI to summarize large documents. They need to choose between on-demand (pay-as-you-go) and dedicated cluster pricing. Which THREE factors should they consider when deciding? (Choose three.)

Accepted Answer

Throughput requirements and consistency. Latency sensitivity, throughput consistency, and cost predictability are key factors. On-demand is variable cost with potential contention; dedicated offers fixed cost with reserved capacity. Model accuracy does not differ based on pricing model, and data residency is a compliance factor but not directly tied to pricing model choice.

Answer

Model accuracy on summarization tasks

Answer

Data residency requirements

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

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

Answer

Fine-tune a base LLM on the policy documents monthly