1Z0-1127-25 OCI Generative AI Service Practice Test 8 — 15 Questions

Question 1

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 2

A company has fine-tuned a Cohere Command R model using T-Few and wants to deploy it for real-time inference with the lowest possible latency. They have provisioned a dedicated AI cluster with 2 model units. However, latency is still higher than expected. Which action is MOST likely to reduce latency?

Accepted Answer

Increase the number of model units on the dedicated AI cluster. On OCI Generative AI dedicated AI clusters, model units (MUs) determine the compute capacity allocated to host the model. If latency is higher than expected with 2 MUs, the model is likely under-provisioned relative to the request load, so increasing the number of model units adds serving capacity and reduces per-request latency. This is the most direct lever for a latency problem on a dedicated cluster.

Answer

Reduce the temperature parameter to 0

Answer

Switch from dedicated AI cluster to shared infrastructure

Answer

Use a larger base model like Llama 3 70B

Question 3

A company wants to use OCI Generative AI to build a multilingual customer support chatbot. They need to understand customer queries in multiple languages and generate responses in the same language. Which TWO actions should they take? (Choose two.)

Accepted Answer

Select Cohere Command R as the base model for chat. Option C is correct because Cohere Command R is a multilingual generative chat model available in OCI Generative AI that can understand queries and generate responses across many languages, directly supporting the chatbot's conversational needs. Option D is correct because embed-multilingual-v3.0 produces vector embeddings that capture semantic meaning across multiple languages, enabling multilingual semantic search and retrieval (e.g., RAG) so queries in any supported language can match relevant knowledge. Option A is wrong because embed-english-v3.0 is English-only and would fail to represent non-English customer queries accurately. Option B is not required and is not one of the marked answers, since fine-tuning Llama 3 on multilingual data is unnecessary when a natively multilingual model like Command R is available. Option E is wrong because the Summarisation API condenses text and does not provide multilingual query understanding or same-language response generation for a chatbot.

Answer

Use the embed-english-v3.0 model for embedding queries

Answer

Fine-tune Meta Llama 3 on multilingual data

Answer

Use the Summarisation API for each language

Question 4

A financial services company must deploy a fine-tuned model for transaction categorization. The model must be isolated from other tenants and provide predictable low-latency inference. The compliance team also requires that training data never leaves the OCI tenancy. Which THREE steps should the team take? (Choose three.)

Accepted Answer

Fine-tune the model using T-Few technique within OCI. Option A is correct because T-Few (few-shot fine-tuning) is performed inside OCI Generative AI, so the training data stays within the tenancy and never leaves it, satisfying the compliance requirement. Option D is correct because a dedicated AI cluster with model units provides the isolated, tenant-specific compute capacity needed for the fine-tuned model. Option C is correct because deploying the fine-tuned model on that dedicated cluster after fine-tuning delivers the isolation and predictable low-latency inference the company requires. Option B is not appropriate because on-demand inference uses shared, multi-tenant endpoints rather than the dedicated isolated deployment required here. Option E is incorrect because a shared AI cluster does not provide tenant isolation and offers less predictable latency.

Answer

Use OCI GenAI on-demand inference for the fine-tuned model

Answer

Host the model on a shared AI cluster to reduce cost

Question 5

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

Accepted Answer

It supports both Cohere and Meta Llama models. Option C is correct because the OCI Generative AI Playground exposes the service's hosted foundation models, which include both Cohere models (e.g., Command R/R+) and Meta Llama models, so users can select and experiment with either family. Option D is correct because the Playground is an interactive chat/console interface where you can switch between available models and tune inference parameters such as temperature, max tokens, top-p, and frequency/presence penalties to see how outputs change. Option A is not correct because the Playground does not provide a dedicated side-by-side multi-model comparison dashboard; you interact with one selected model at a time. Option B is not correct because uploading training datasets for fine-tuning is done through the OCI Generative AI fine-tuning workflow (dedicated fine-tuning jobs and datasets in Object Storage), not the Playground. Option E is not correct because provisioning and managing dedicated AI clusters (dedicated AI clusters for hosting custom/fine-tuned models) is handled through the OCI Generative AI dedicated AI cluster management features, not the Playground.

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 6

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 7

A data scientist needs to fine-tune a large language model on a custom dataset of 10,000 prompt-completion pairs. They want to minimize cost while still updating the model effectively. Which fine-tuning technique is used by OCI Generative AI service?

Accepted Answer

T-Few fine-tuning. T-Few fine-tuning is the technique used by OCI Generative AI service for fine-tuning large language models. It is a parameter-efficient method that updates only a small number of parameters, reducing computational cost while still effectively adapting the model to custom datasets. This aligns with the goal of minimizing cost while updating the model effectively.

Answer

Prefix tuning

Answer

Adapter fine-tuning

Answer

LoRA fine-tuning

Question 8

An organization requires low-latency inference for a custom fine-tuned model that will be used in a real-time application. They also need guaranteed availability and isolation from other tenants. Which infrastructure option should they choose?

Accepted Answer

Provision a Dedicated AI Cluster with model units for the fine-tuned model. Dedicated AI Clusters provide isolated, low-latency inference for custom models. Shared infrastructure is multi-tenant and may have higher latency. On-demand tokens and serving endpoints without a cluster do not offer dedicated resources.

Answer

Create an endpoint on OCI API Gateway pointing to shared inference

Answer

Use the OCI Generative AI Playground to host the model

Answer

On-demand token-based inference on shared infrastructure

Question 9

A developer is using the OCI Generative AI Playground to test a Cohere Command R model. They want to reduce repetitiveness in the generated responses. Which parameter should they increase?

Accepted Answer

Frequency penalty. The frequency penalty parameter in Cohere Command R penalizes tokens that have already appeared in the generated text, reducing the likelihood of repetition. Increasing it makes the model less likely to reuse the same tokens, directly addressing repetitiveness. Temperature and Top P affect randomness and diversity but do not specifically target repeated tokens.

Answer

Max tokens

Answer

Top P

Answer

Temperature

Question 10

Which OCI Generative AI model family is specifically designed for reranking search results to improve relevance?

Accepted Answer

Cohere Rerank. Cohere Rerank is the OCI Generative AI model family specifically designed for reranking search results to improve relevance. Unlike generation-focused models, Rerank takes a query and a list of candidate documents, scoring each for relevance to the query, thereby enhancing the quality of retrieved results in RAG pipelines.

Answer

Cohere Command R

Answer

Cohere Command R+

Answer

Meta Llama 3

Question 11

A machine learning engineer is preparing a dataset for fine-tuning a model in OCI Generative AI. The dataset consists of customer support conversations with questions and desired answers. What is the required format for the training data?

Accepted Answer

JSONL file with 'prompt' and 'completion' fields per line. OCI Generative AI expects a JSONL file where each line is a JSON object with 'prompt' and 'completion' fields. CSV, Parquet, and text files are not supported for fine-tuning.

Answer

CSV file with columns 'input' and 'output'

Answer

Parquet file with 'text' and 'label' columns

Answer

Plain text file with examples separated by blank lines

Question 12

A company wants to use OCI Generative AI Agents to build a question-answering system over documents stored in OCI Object Storage. Which component acts as the knowledge source for the agent?

Accepted Answer

A knowledge base created from the Object Storage bucket. In OCI Generative AI Agents, a knowledge base is created from a data source such as an Object Storage bucket, and it serves as the repository of documents that the agent uses to answer questions. The knowledge base is the core component that enables retrieval-augmented generation (RAG) by indexing and retrieving relevant information.

Answer

A Dedicated AI Cluster

Answer

The OCI Generative AI Playground

Answer

An endpoint created via the InferenceClient

Question 13

A developer is building a multilingual search application and needs to generate embeddings for user queries in multiple languages. Which two options are correct? (Select TWO)

Accepted Answer

Use the embed-multilingual-v3.0 model. Option C is correct because embed-multilingual-v3.0 is Cohere's multilingual embedding model, explicitly trained to produce embeddings across many languages, which is required for a multilingual search application handling user queries in multiple languages. Option D is correct because when generating embeddings for a user's search query, the input type must be set to 'search_query' so the embedding is optimized for the query side of retrieval and matches the 'search_document' embeddings used for the indexed content. Option A is wrong because embed-english-v3.0 only supports English and cannot properly embed queries in other languages. Option B is wrong because 'search_document' is the input type for indexing documents, not for embedding user queries. Option E is wrong because 'classification' is an input type intended for text classification tasks, not for search query embedding.

Answer

Use the embed-english-v3.0 model

Answer

Set the input type to 'search_document'

Answer

Set the input type to 'classification'

Question 14

An enterprise needs to deploy a custom fine-tuned model for real-time inference with strict latency requirements. They also need to manage costs by paying only for usage. Which three steps are required to achieve this? (Select THREE)

Accepted Answer

Create an endpoint using InferenceClient to point to the dedicated cluster. Option B is correct because creating an endpoint with InferenceClient (or the equivalent endpoint-creation API) is the required step to expose the fine-tuned model on the dedicated cluster so applications can send real-time inference requests to it. Option D is correct because a Dedicated AI Cluster with sufficient model units must be provisioned to provide the dedicated, predictable compute capacity needed to meet strict latency requirements. Option E is correct because the fine-tuned model must actually be hosted/deployed on that Dedicated AI Cluster; provisioning the cluster alone does not serve the model. Option A is not correct because on-demand token-based inference on shared infrastructure does not provide the dedicated capacity guarantees needed for strict latency SLAs. Option C is not correct because deleting the fine-tuning job after deployment is not a required step and does not affect inference costs in the way described; the deployed model and cluster are what incur usage costs.

Answer

Use the on-demand token-based inference on shared infrastructure

Answer

Delete the fine-tuning job after deployment to save costs

Question 15

A company is using OCI Generative AI Agents to build a customer support assistant. They have uploaded product manuals to OCI Object Storage. Which two components are required to create the agent? (Select TWO)

Accepted Answer

Create a Generative AI Agent using the knowledge base. Option C is correct because OCI Generative AI Agents requires a knowledge base that ingests and indexes the source documents; here the product manuals reside in an Object Storage bucket, so creating a knowledge base from that bucket is a mandatory step. Option A is correct because, once the knowledge base exists, you must create the Generative AI Agent and associate it with that knowledge base so the agent can perform retrieval-augmented generation over the manuals. Option B is not required: InferenceClient is a lower-level SDK construct for calling models directly, not a component needed to build an agent. Option D is wrong because data must be supplied through a knowledge base rather than uploaded directly to the agent. Option E is unnecessary because Dedicated AI Clusters are for dedicated hosting capacity, whereas agents can be created on on-demand infrastructure.

Answer

Create an endpoint via InferenceClient

Answer

Upload data directly to the agent (not via knowledge base)

Answer

Provision a Dedicated AI Cluster