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

Question 1

A developer is using the OCI Generative AI Generate API (not Chat API) to create a single-turn text completion. They need to include a system-level instruction that guides the model's behavior for that request. Which parameter should they use?

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

Accepted Answer

'preamble_override' parameter. The Generate API uses 'preamble_override' to set a system instruction for the completion. The 'system' parameter is for the Chat API. 'max_tokens' and 'temperature' are not system instructions.

Answer

'temperature' parameter

Answer

'system' parameter

Answer

'max_tokens' parameter

Question 2

A company wants to use OCI Generative AI Agents for a question-answering system over their internal knowledge base stored in OCI Object Storage. The data consists of PDF and Word documents. What is the first step to make this data usable by the agent?

Accepted Answer

Create a knowledge base and associate it with the Object Storage bucket. To make documents in OCI Object Storage usable by a Generative AI Agent, the first step is to create a knowledge base and associate it with the Object Storage bucket. The knowledge base ingests and indexes the documents, enabling the agent to retrieve relevant content for question answering. This is the foundational step before the agent can query the data.

Answer

Use the Embedding API to generate embeddings for all documents

Answer

Create a dedicated AI cluster for inference

Answer

Create an OCI Generative AI Agent

Question 3

A company wants to reduce costs for a high-volume, latency-tolerant text generation workload using OCI Generative AI. Which TWO strategies should they consider?

Accepted Answer

Use on-demand (pay-per-token) pricing instead of a dedicated AI cluster. Option A is correct because on-demand pay-per-token pricing in OCI Generative AI charges only for tokens consumed, which is cost-effective for high-volume but latency-tolerant workloads that don't require guaranteed dedicated capacity. Option E is correct because smaller models such as Cohere Command R consume fewer resources and cost less per token than larger models like Command R+, directly reducing cost for a workload that tolerates latency and doesn't demand maximum quality. Option B is not appropriate because a dedicated AI cluster incurs fixed provisioning costs aimed at consistent throughput, which is unnecessary and more expensive for a latency-tolerant workload. Option C is incorrect because temperature controls randomness of output, not token consumption, so raising it does not reduce cost. Option D is incorrect because using the largest model increases cost per token and is not a cost-reduction strategy.

Answer

Provision a dedicated AI cluster for consistent throughput

Answer

Increase the temperature parameter to reduce token usage

Answer

Use the largest available model to ensure best quality

Question 4

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?

Accepted Answer

Create a knowledge base that references the Object Storage bucket. Option B is correct because an OCI Generative AI Agents knowledge base is the component that ingests and indexes the approved documents, and it must be created with a data source pointing to the Object Storage bucket holding those documents. Option E is correct because the approved documents must first reside in an Object Storage bucket within the target compartment, which serves as the data source for the knowledge base. Option D is correct because the agent's service principal needs an IAM policy granting read access to the objects in that specific compartment; without it, ingestion and retrieval will fail with authorization errors. Option A is not required because a dedicated AI cluster is for hosting fine-tuned or dedicated model endpoints, not for building a RAG knowledge base. Option C is not required because OCI Generative AI Agents automatically handles embedding generation and vector storage when the knowledge base is created, so manual Embedding API calls are unnecessary.

Answer

Provision a dedicated AI cluster in the same compartment

Answer

Use the Embedding API to manually generate embeddings for each document

Question 5

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 6

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 7

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. Repetitive output in large language models is directly countered by the frequency penalty, which penalizes tokens based on how often they have already appeared in the generated text, discouraging the model from repeating the same words or phrases. Increasing the frequency penalty makes the model less likely to loop on identical tokens, directly addressing the repetition symptom. This parameter is available in the OCI Generative AI Playground and is the most targeted adjustment for repetition.

Answer

Increase max tokens

Answer

Decrease temperature

Answer

Add a stop sequence

Question 8

A developer is using the OCI Generative AI Generate API (not Chat API) to create a single-turn text completion. They need to include a system-level instruction that guides the model's behavior for that request. Which parameter should they use?

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

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 9

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 OCI Generative AI embedding model explicitly optimized for English-only text, producing high-quality vector representations for semantic search, clustering, and classification. It is the English counterpart to embed-multilingual-v3.0 and is the correct choice when the corpus is English and maximum English retrieval quality is desired.

Answer

Meta Llama 3 8B

Answer

Cohere embed-multilingual-v3.0

Answer

Cohere Command R

Question 10

A team fine-tuned a model using T-Few and validated it. They now want to deploy this fine-tuned model to a dedicated AI cluster for low-latency inference. What must they do FIRST?

Accepted Answer

Create a dedicated AI cluster with a specified number of model units. To deploy a fine-tuned model to a dedicated AI cluster for low-latency inference, you must first create the dedicated AI cluster and specify the number of model units. This cluster provides isolated compute resources that ensure consistent, low-latency performance, unlike the shared serverless endpoint. The fine-tuned model is then deployed onto this cluster, not copied to Object Storage first.

Answer

Copy the fine-tuned model to an Object Storage bucket

Answer

Increase the temperature parameter in the inference request

Answer

Delete the base model to free up capacity

Question 11

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 12

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 13

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 14

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 15

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. Option A (Throughput requirements and consistency) is correct because dedicated AI clusters provide reserved, guaranteed capacity (measured in dedicated AI cluster units) that delivers consistent throughput, whereas on-demand inference is subject to shared-capacity limits and throttling, so high or steady throughput needs favor a dedicated cluster. Option B (Latency sensitivity of the application) is correct because latency-sensitive summarization workloads benefit from the dedicated cluster's provisioned, non-shared inference endpoints, which avoid the queuing variability of on-demand endpoints. Option E (Cost predictability and budget constraints) is correct because dedicated clusters are billed on a committed hourly/period basis (predictable, fixed cost) while on-demand is pay-per-token (variable), so budget forecasting drives the choice. Option C (Model accuracy on summarization tasks) does not belong because accuracy is a function of the chosen foundation model and prompting, not the pricing/consumption model. Option D (Data residency requirements) does not belong because residency is determined by the region and endpoint you select, not by whether you use on-demand or dedicated cluster pricing.

Answer

Model accuracy on summarization tasks

Answer

Data residency requirements