1Z0-1127 Timed Practice Test 3 — 10 Questions

Question 1

A company has fine-tuned a large language model using OCI Generative AI service. When attempting to deploy the model to a dedicated endpoint, the deployment fails with an error indicating insufficient capacity. Which action should be taken to resolve this issue?

Accepted Answer

Request a service limit increase for dedicated endpoints. Option C is correct because capacity quotas can be increased by requesting a service limit increase. Option A is wrong because deploying to a different region may not address capacity issues in the current region. Option B is wrong because using a pre-built model would not leverage the fine-tuned model. Option D is wrong because deleting other endpoints may not be necessary if quota increase is possible.

Answer

Delete existing endpoints to free capacity

Answer

Deploy the model to a different OCI region

Answer

Use a pre-built model instead of the fine-tuned model

Question 2

A startup wants to minimize costs when using OCI Generative AI service for a chatbot application that experiences sporadic usage. Which deployment strategy is most cost-effective?

Accepted Answer

Use the serverless on-demand API without dedicated endpoints. Option B is correct because serverless on-demand pricing charges only for usage, ideal for sporadic workloads. Option A is wrong because dedicated endpoints incur hourly costs regardless of usage. Option C is wrong because pre-built models may also have per-request costs but dedicated endpoints are not cost-effective. Option D is wrong because running models on OCI Compute adds management overhead and costs.

Answer

Use a pre-built model with a dedicated endpoint

Answer

Provision a dedicated endpoint for low latency

Answer

Deploy the model on OCI Compute with autoscaling

Question 3

A user wants to access the OCI Generative AI service programmatically. Which credential method is recommended for use in a production application running on OCI Compute?

Accepted Answer

Instance principal. Instance principal authentication is the recommended method for production applications running on OCI Compute because it allows the application to authenticate with OCI services without managing or embedding any credentials. The OCI Compute instance assumes a dynamic group and IAM policy that grants it permissions, and the SDK automatically handles token exchange via the instance metadata service, eliminating the need for long-lived secrets.

Answer

API signing keys

Answer

User password and OCID

Answer

Resource principal

Question 4

A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?

Accepted Answer

OCI Monitoring. A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.

Answer

OCI Events

Answer

OCI Notifications

Answer

OCI Logging

Question 5

A company has multiple teams sharing an OCI Generative AI Dedicated AI Cluster. They need to ensure that each team can only access their own fine-tuned models and cannot see or invoke models from other teams. What is the best approach?

Accepted Answer

Use OCI compartments and IAM policies with resource-level permissions for models. OCI compartments and IAM policies with resource-level permissions allow you to grant granular access to specific models within a Dedicated AI Cluster. By placing each team's fine-tuned models in separate compartments and writing policies that restrict access to those compartments, you ensure teams can only see and invoke their own models. This approach leverages OCI's native identity and access management without requiring separate clusters or network-level isolation.

Answer

Train separate models for each team

Answer

Encrypt model artifacts with different keys for each team

Answer

Use network security lists to isolate traffic

Question 6

A developer wants to invoke an OCI Generative AI model from an application running on a compute instance in OCI. The instance is in a private subnet. What is the most secure method to access the model endpoint?

Accepted Answer

Use a Service Gateway to access the endpoint privately.. A Service Gateway allows resources in a private subnet to access OCI services, including the Generative AI model endpoint, over the OCI private network without traversing the internet. This is the most secure method because traffic stays within the OCI backbone, avoiding exposure to public IPs and reducing the attack surface.

Answer

Use an Internet Gateway and public endpoint.

Answer

Use a VPN Connect to connect to the model's public IP.

Answer

Use a NAT Gateway to access the endpoint.

Question 7

A company is designing a generative AI solution on OCI that must comply with data privacy regulations. Which three best practices should they follow? (Choose three.)

Accepted Answer

Enable audit logging for all inference requests. Option A is correct because enabling audit logging for all inference requests is a fundamental data privacy best practice. It provides an immutable record of who accessed the generative AI service, what data was sent, and when, which is essential for compliance audits and detecting unauthorized access. OCI Audit service captures these events automatically when configured, ensuring traceability without storing the actual inference payloads.

Answer

Allocate a dedicated compartment for generative AI resources to apply specific IAM policies

Answer

Store all inference inputs and outputs in a public bucket for transparency

Question 8

Which OCI Generative AI service model family supports fine-tuning with custom datasets?

Accepted Answer

Cohere Command. Cohere Command is the model family within OCI Generative AI that supports fine-tuning with custom datasets, allowing users to adapt the model for domain-specific tasks like summarization or classification. In contrast, Cohere Embed is designed for generating text embeddings, Cohere Summarize is a specialized endpoint for summarization without fine-tuning support, and GPT-3 is not natively available in OCI Generative AI for fine-tuning.

Answer

Cohere Embed

Answer

Cohere Summarize

Answer

GPT-3

Question 9

A developer wants to integrate generative AI capabilities into an application using REST API calls. Which OCI Generative AI service endpoint should they use for text generation?

Accepted Answer

/completions. Option A is correct because the OCI Generative AI service exposes a REST API endpoint at `/completions` specifically for text generation tasks. This endpoint accepts a prompt and returns a generated text completion, aligning directly with the developer's requirement to integrate generative AI capabilities via REST API calls.

Answer

/models

Answer

/inference

Answer

/chat

Question 10

Refer to the exhibit. The dashboard shows latency grouped by modelId, but some points are missing for certain modelIds. Which of the following is the most likely reason?

Accepted Answer

The modelIds with missing data may have been deleted or are inactive. Option C is correct because in OCI's Generative AI service, model deployments are associated with specific modelIds. If a modelId is deleted or its deployment is deactivated, the corresponding telemetry data (e.g., latency metrics) will no longer be reported, causing gaps in the dashboard. The dashboard aggregates metrics only for active modelIds, so missing points indicate that those modelIds are no longer in service.

Answer

The metric name is misspelled

Answer

The aggregation interval is too short

Answer

The compartmentId is incorrect