1Z0-1127-25 OCI Generative AI Service Practice Test 10 — 15 Questions

Question 1

A company wants to process a large batch of documents to generate summaries using OCI Generative AI. They need the most cost-effective option without compromising on summary quality. Which approach should they use?

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

Accepted Answer

Use the on-demand (shared) inference endpoint with the Summarisation API. On-demand pricing is pay-per-token, suitable for batch workloads without dedicated capacity; it's cost-effective for sporadic or large batch jobs.

Answer

Use OCI Generative AI Agents with a knowledge base

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Provision a dedicated AI cluster for the batch job

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Fine-tune a model on summarization and deploy it on a dedicated cluster

Question 2

Which API should a developer use to send a multi-turn conversation history to an LLM, including a system message and previous user/assistant exchanges, using OCI Generative AI?

Accepted Answer

Chat API. The OCI Generative AI Chat API is purpose-built for multi-turn conversations: it accepts a list of messages with roles (system, user, assistant) so the model retains conversational context across turns. The Generate API handles single-prompt text generation without native conversation history semantics.

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Summarisation API

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Generate API

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Embedding API

Question 3

A company wants to build a RAG application using OCI Generative AI Agents. Which TWO components are required to set up the agent?

Accepted Answer

A knowledge base. In OCI Generative AI Agents, a knowledge base (option B) is a required component because it stores the vectorized content that the agent retrieves from during RAG (retrieval-augmented generation) to ground its responses. A data source such as OCI Object Storage (option D) is also required, since the knowledge base must ingest documents from a source to build its index; without ingested data there is nothing to retrieve. An embedding model (option A) is used internally by the service to vectorize the data, but it is not a separately provisioned component the customer must set up, so it is not one of the two required setup components. A dedicated AI cluster (option C) is not needed, as OCI Generative AI Agents is a managed service that does not require provisioning dedicated infrastructure. A fine-tuned model (option E) is optional and unrelated to the basic RAG setup, since the agent can use a base model with retrieval instead of fine-tuning.

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An embedding model

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A dedicated AI cluster

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A fine-tuned model

Question 4

A data scientist is using the OCI Generative AI Playground to test a model for a text generation task. They want to control the output to be more focused and avoid repeating the same phrases. Which THREE parameter adjustments should they consider?

Accepted Answer

Increase the presence penalty. Option E (Decrease the temperature) is correct because lowering temperature makes the model's probability distribution sharper, so sampling favors high-probability tokens and produces more focused, deterministic output rather than wandering into tangential text. Option D (Increase the frequency penalty) is correct because it penalizes tokens in proportion to how many times they have already appeared in the generated text, directly discouraging the repetition of the same phrases. Option A (Increase the presence penalty) is correct because it applies a flat penalty to any token that has already appeared at least once, pushing the model toward new topics and wording and thereby reducing repeated phrases. Option B (Increase the temperature) is wrong because raising temperature flattens the distribution and makes output more random and less focused, the opposite of the goal. Option C (Increase the max tokens) is wrong because it only extends the allowed output length and does not influence focus or repetition.

Answer

Increase the temperature

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Increase the max tokens

Question 5

A solutions architect is building a customer-facing chatbot on OCI Generative AI. Legal requires that no prompt or completion content ever be stored or used to train any model, and the architect must prove this to auditors. Which OCI Generative AI capability should the architect enable and document?

Accepted Answer

The 'Do not store model data' option on the OCI Generative AI service. The service setting 'Do not store model data' is the only control that directly addresses retention and training use of prompt and completion content. Cluster isolation, private networking, and content filtering all harden the deployment but leave the data-handling policy unchanged. Because the requirement is explicitly about non-persistence and non-training, enabling and documenting that setting is the correct action.

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Guardrails configured with a personally identifiable information (PII) filter

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A private endpoint backed by an OCI private subnet and a network security group

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Dedicated AI clusters with a customer-managed encryption key stored in OCI Vault

Question 6

A solutions architect in the eu-frankfurt-1 region is building an application that calls the OCI Generative AI chat endpoint for a Cohere Command R model. The application must use IAM credentials that are automatically rotated and are never stored as long-lived secret keys on the compute instances. Which authentication mechanism should the application use?

Accepted Answer

Instance principals. Instance principals let a compute instance obtain short-lived, platform-managed certificates from the instance metadata service, so the application can call the OCI Generative AI chat endpoint without storing or rotating static credentials. This meets both requirements: automatic rotation and no long-lived secret keys on the instance.

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User API keys stored in the instance metadata

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Resource principal with a dynamic group for the model endpoint

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OCI Vault secret with a bearer token in the request header

Question 7

A developer is calling the OCI Generative AI inference endpoint for a Cohere Command model and wants the model to produce deterministic, repeatable output for a regression test suite. Which parameter should they set to 0?

Accepted Answer

temperature. Deterministic output requires removing the stochastic element from token selection, and temperature is the parameter that scales the logits before sampling. At temperature 0 the distribution collapses toward the single highest-probability token, giving greedy decoding and near-identical responses across identical requests. Length caps, candidate-pool limits, and repetition penalties all modify the generation in other ways but leave the random draw in place.

Answer

topK

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frequencyPenalty

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maxTokens

Question 8

A developer is prototyping a text-generation feature and wants to compare several prompt phrasings and parameter settings quickly, without writing any code or provisioning infrastructure. Which OCI Generative AI capability should the developer use?

Accepted Answer

The OCI Generative AI Playground, which provides an interactive console for testing prompts and parameters against available models.. The OCI Generative AI Playground is the console-based, no-code environment for interactively testing prompts and parameters against available models. It requires no infrastructure provisioning and no scripting, so a developer can rapidly compare prompt phrasings and settings. Dedicated clusters, direct API scripting, and notebook sessions all add setup or coding overhead that the scenario explicitly rules out.

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An OCI Data Science notebook session with the OCI Python SDK installed, used to call the model.

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A dedicated AI cluster hosting a fine-tuned model, which the developer can query from the console.

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The Generative AI inference REST API called from a local script using a config file with the user's API key.

Question 9

A developer is preparing a custom fine-tuning job in OCI Generative AI. They want the resulting model to be served from a dedicated, private endpoint inside their tenancy, and they want to control the endpoint's shape and scaling behavior. Which OCI Generative AI capability should they use to host the fine-tuned model?

Accepted Answer

Create a dedicated AI cluster in OCI Generative AI and host the fine-tuned model on a dedicated endpoint. In OCI Generative AI, dedicated AI clusters provide tenancy-scoped compute for hosting models on dedicated endpoints, including fine-tuned models. Because the developer needs a private endpoint with control over hosting shape and scaling, hosting the fine-tuned model on a dedicated endpoint within a dedicated AI cluster is the appropriate capability. Other options describe storage or experimentation tools that do not deliver dedicated model serving.

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Publish the fine-tuned model to OCI Object Storage and call it through a pre-authenticated request

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Use the OCI Generative AI Playground to interact with the fine-tuned model

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Import the fine-tuned model into OCI Data Science and deploy it as a model deployment

Question 10

A developer is using the OCI Generative AI Playground to compare how a model behaves when the prompt is phrased in different ways. They want each request to be evaluated independently so that earlier prompts do not influence later ones. Which Playground behavior should they rely on?

Accepted Answer

Each submission starts a new conversation with no retained context. The Playground treats each prompt submission as a new, independent request unless the user deliberately continues a chat session. That default behavior gives the developer clean, unbiased comparisons across different phrasings without needing any special configuration.

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The Playground automatically summarizes previous prompts before each request

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The model caches the first response and reuses it for similar prompts

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A 'Reset context' toggle must be disabled to prevent history from being sent

Question 11

An enterprise wants its OCI Generative AI endpoint to remain inside the Oracle Cloud network and never traverse the public internet, while still being reachable from applications running in the same VCN. Which endpoint type should they configure?

Accepted Answer

A private endpoint associated with the VCN. Keeping generative AI traffic off the public internet means the endpoint itself must be private and bound to the VCN. A private endpoint gives the service a private IP inside the customer's network, where security lists, network security groups, and IAM control access. Public endpoints, dedicated capacity clusters, and service gateways address other concerns such as reachability, performance, and routing to Oracle services.

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A public inference endpoint with an API key

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A service gateway attached to the on-premises network

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A dedicated AI cluster endpoint

Question 12

A developer is calling the OCI Generative AI chat endpoint with a Cohere Command R model and wants the model to produce more deterministic, repeatable answers for a structured data extraction task. They set temperature to 0. Which additional parameter should they also tune to reduce randomness in token selection?

Accepted Answer

Top-p (nucleus sampling). Temperature and top-p are the two primary sampling controls. With temperature at 0, lowering top-p narrows the candidate token set to the most probable tokens, which reduces randomness and produces more consistent, repeatable extraction results.

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Max tokens

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Presence penalty

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Frequency penalty

Question 13

A solutions architect needs to programmatically invoke a Cohere model hosted in OCI Generative AI from an application running on an OCI Compute instance. The application must authenticate without storing long-lived user credentials on the instance. Which authentication approach should the architect configure?

Accepted Answer

Use an instance principal with a dynamic group and a policy granting access to the generative AI resource. Instance principals allow an OCI Compute instance to obtain short-lived credentials automatically through the instance metadata service. Combined with a dynamic group and an IAM policy granting access to OCI Generative AI, the application can call the model without embedding user API keys. This is the recommended pattern for workloads running on OCI compute resources that need service access without long-lived secrets.

Answer

Embed a pre-authenticated request (PAR) URL for the generative AI endpoint in the application code

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Use a customer secret key tied to the instance's compartment to sign requests to the model

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Create a local OCI user, generate an API key, and store the private key on the instance

Question 14

A team is designing a RAG pipeline on OCI Generative AI Agents and needs the retriever to return the most semantically relevant chunks from a large corpus of policy PDFs. Which component should they configure to convert the corpus and the user query into comparable vector representations?

Accepted Answer

The embedding model assigned to the knowledge base. Semantic retrieval works by mapping both the query and the stored text into the same vector space and then ranking by similarity. The embedding model is the component that performs this mapping, so it must be configured for the knowledge base and must match the language and domain of the policy documents. Chat models, index types, and prompt templates affect generation or search mechanics but do not create the embeddings.

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The vector store index type

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The agent's system prompt template

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The chat model selected for the agent

Question 15

A developer wants to embed OCI Generative AI chat completions directly into a Python application that already uses the OCI SDK for other services, without adding a new SDK family. Which approach fits this requirement?

Accepted Answer

Use the OCI Python SDK's Generative AI client with the chat API and the configured OCI config profile. Reusing the OCI Python SDK's Generative AI client keeps authentication, retries, and configuration consistent with the rest of the application and exposes chat completions without adding another SDK. Alternatives either target a different platform's API, add process overhead, or use an authentication model OCI does not accept for these endpoints.

Answer

Call the model's REST endpoint with a bearer token issued by an external identity provider

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Install the Cohere Python SDK and call the Cohere endpoint with an OCI API key

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Use the OCI CLI to invoke chat completions and parse the JSON output inside the Python process