1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 6 — 15 Questions

Question 1

Which two actions are required when deploying a custom fine-tuned model using the OCI Generative AI service? (Choose two.)

Accepted Answer

Upload model artifacts to OCI Object Storage. Deploying a custom fine-tuned model in OCI Generative AI requires the model artifacts (e.g., weights, configuration files) to be stored in OCI Object Storage. The service pulls the artifacts from a designated bucket during deployment. Option E is correct because a dedicated AI cluster must be created to host the model, providing the necessary compute resources for inference.

Answer

Configure an API Gateway for the model endpoint

Answer

Register the model in the OCI Data Science Model Catalog

Answer

Set up a load balancer for the deployment

Question 2

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. 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 3

Which two metrics would you monitor to ensure a generative AI deployment on OCI is operating efficiently? (Choose two.)

Accepted Answer

Request throughput (requests per second). Request throughput (requests per second) is a critical metric for monitoring the operational efficiency of a generative AI deployment on OCI because it directly measures the system's capacity to handle incoming inference requests. If throughput drops below expected levels, it indicates a bottleneck in the compute resources (e.g., GPU utilization) or the serving infrastructure, which can lead to degraded user experience and potential timeouts.

Answer

Number of active users

Answer

Object Storage bucket size

Answer

Model accuracy on validation set

Question 4

Refer to the exhibit. An administrator receives the error shown when attempting to deploy a custom model. What is the most likely cause?

Accepted Answer

The user or service does not have permission to read the model artifact from Object Storage. The error indicates that the deployment process cannot access the model artifact stored in Object Storage. In OCI Generative AI, the service must have read permission on the bucket and object to download the artifact. If the user or service principal lacks the necessary IAM policy (e.g., `allow service generative-ai to read objects in compartment X where target.bucket.name='Y'`), the deployment fails with this access-denied error.

Answer

The compartment ID is incorrect

Answer

The model artifact file is corrupted

Answer

The dedicated AI cluster ID is invalid

Question 5

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. 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

Question 6

Refer to the exhibit. Users in the group cannot create a new custom model deployment on a Dedicated AI Cluster. What is the most likely missing permission?

Accepted Answer

Use of virtual-network-family for cluster networking. Creating a custom model deployment on a Dedicated AI Cluster requires the user to have the 'Use of virtual-network-family for cluster networking' permission. This permission allows the user to specify and manage the virtual network (VCN) and subnet that the cluster uses for networking. Without it, the deployment fails because the cluster cannot be attached to the required network resources.

Answer

Manage ai-document-understanding

Answer

Manage ai-agents

Answer

Manage instance-configurations

Question 7

A retail company uses OCI Generative AI to generate product descriptions. They observe the model occasionally produces biased content. Which technique should be applied to reduce bias in model outputs?

Accepted Answer

Apply prompt engineering with explicit instructions to avoid bias.. Prompt engineering allows you to explicitly instruct the model to avoid biased content, such as by including directives like 'Ensure the description is neutral and unbiased' in the system or user prompt. This technique directly influences the model's output generation without altering its underlying parameters, making it a targeted and effective approach for reducing bias in OCI Generative AI models.

Answer

Increase the max_tokens parameter.

Answer

Reduce the model's inference temperature to 0.

Answer

Use a different random seed for each request.

Question 8

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 9

A data scientist needs to fine-tune a model on OCI Generative AI. Which of the following is a required parameter in the fine-tuning request?

Accepted Answer

All of the above. In OCI Generative AI, the fine-tuning request requires all three parameters: hyperparameters (to define training behavior like learning rate and epochs), model_name (to specify the base model being fine-tuned), and dataset_type (to indicate the format of the training data, such as 'TEXT' or 'MULTI_TURN'). Therefore, 'All of the above' is correct because each listed option is a mandatory field in the fine-tuning API call.

Answer

hyperparameters

Answer

model_name

Answer

dataset_type

Question 10

A company is deploying a chatbot powered by OCI Generative AI. They want to inject the conversation history into the model prompt to maintain context. However, they notice that after a long conversation, the model starts to ignore earlier messages. What is the most likely cause?

Accepted Answer

The model has a limited context window size.. The model's context window size limits the total number of tokens (input + output) it can process at once. When the conversation history grows beyond this limit, older messages are truncated or dropped, causing the model to lose context from earlier parts of the conversation. This is a fundamental constraint of transformer-based models like those used in OCI Generative AI.

Answer

The model's max_tokens limit is too low, truncating the prompt.

Answer

The top_p parameter is set to 1, causing deterministic output.

Answer

The temperature setting is too high, causing randomness.

Question 11

An organization needs to ensure that all inference requests to OCI Generative AI are logged for compliance. Which OCI feature should be enabled?

Accepted Answer

OCI Logging for the AI service. OCI Logging for the AI service captures detailed request and response data for inference calls to OCI Generative AI, including payloads, timestamps, and user identities. This feature must be explicitly enabled per service endpoint to meet compliance requirements for logging all inference requests. Unlike Audit logs, which record control-plane operations, OCI Logging provides data-plane logging for the AI service itself.

Answer

OCI Cloud Guard

Answer

OCI Vault

Answer

OCI Audit logs

Question 12

A team wants to use OCI Generative AI to generate synthetic data for training a model. They are concerned about the cost of API calls. Which pricing model would be most cost-effective for high-volume batch processing?

Accepted Answer

Reserved capacity with a fixed monthly fee. Reserved capacity with a fixed monthly fee provides the lowest per-request cost for high-volume batch processing. OCI Generative AI offers dedicated capacity pricing, which is ideal for predictable, large-scale workloads where you commit to a certain throughput, avoiding per-request charges that would accumulate significantly with high volume.

Answer

OCI Universal Credits with per-request charges

Answer

Monthly subscription with limited requests

Answer

Pay-as-you-go per request

Question 13

A company deploys a fine-tuned model on an OCI Generative AI dedicated AI cluster. After deployment, they observe high latency during peak hours. The cluster has only one replica. Which action would most effectively reduce latency without increasing cost unnecessarily?

Accepted Answer

Enable auto-scaling with a maximum of 3 replicas.. Enabling auto-scaling with a maximum of 3 replicas (Option B) is the most effective action because it dynamically adds replicas during peak hours to handle increased load, reducing latency, while limiting the maximum to 3 prevents unnecessary cost overruns. This balances performance and cost, unlike a fixed large replica count or switching models, which either wastes resources or fails to address the root cause of insufficient compute capacity.

Answer

Increase the number of replicas to 10.

Answer

Switch to a larger base model.

Answer

Move to a serverless deployment model.

Question 14

An enterprise with strict data residency requirements wants to use OCI Generative AI. They must ensure that no training data or inference data leaves a specific OCI region. Which configuration option should they choose?

Accepted Answer

Use a dedicated AI cluster in the desired region and disable cross-region access.. A dedicated AI cluster in the desired region, with cross-region access disabled, ensures that all compute, training data, and inference data remain physically within that OCI region. This satisfies strict data residency requirements because the cluster is isolated from other regions at the network and infrastructure level, preventing any data egress.

Answer

Configure a service gateway with a private endpoint.

Answer

Implement a policy restricting data transfer via OCI Identity and Access Management.

Answer

Use OCI Data Transfer Service to keep data within the region.

Question 15

A machine learning engineer evaluates OCI Generative AI for a real-time content generation application. They need to meet a SLAs of 99.9% availability. Which deployment architecture satisfies the requirement with the lowest cost?

Accepted Answer

Two dedicated AI clusters in different availability domains.. Deploying two dedicated AI clusters in different availability domains within a single region provides high availability (HA) to meet the 99.9% SLA while minimizing cost. OCI's dedicated AI clusters are regional resources, and placing replicas across availability domains protects against domain-level failures without the cross-region data transfer and egress costs incurred by multi-region deployments.

Answer

Two dedicated AI clusters in different regions.

Answer

Two dedicated AI clusters in the same availability domain.

Answer

Single dedicated AI cluster with a single replica.