1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 7 — 15 Questions

Question 1

A company needs to ensure that only authorized users can invoke an endpoint for a generative AI model. Which OCI feature should be used to control access?

Accepted Answer

OCI Identity and Access Management (IAM) policies. OCI Identity and Access Management (IAM) policies are the correct choice because they define who (users, groups, or service principals) can invoke which OCI resources, including generative AI model endpoints. IAM policies use resource-type and verb-based statements (e.g., 'allow group A to manage ai-service-family in compartment X') to enforce authorization at the API level, ensuring only authorized principals can call the model's inference endpoint.

Answer

Network security groups (NSGs)

Answer

VCN flow logs

Answer

OCI Web Application Firewall (WAF)

Question 2

A team is fine-tuning a foundation model on a large dataset stored in OCI Object Storage. They want to minimize data transfer costs. What is the best practice for locating the storage?

Accepted Answer

Place the bucket in the same region as the fine-tuning job. Placing the Object Storage bucket in the same OCI region as the fine-tuning job eliminates cross-region data transfer charges. OCI charges egress fees when data moves between regions, but intra-region data transfer between services in the same region is free. This minimizes costs while keeping the data accessible for the fine-tuning workload.

Answer

Place the bucket in the same region and availability domain as the fine-tuning job

Answer

Use OCI File Storage instead of Object Storage

Answer

Use a cross-region bucket to leverage geographically distributed data

Question 3

During deployment of a generative AI model, the inference endpoint returns high latency and timeouts. The model is deployed on a dedicated AI cluster with multiple nodes. What is the most likely cause?

Accepted Answer

The cluster nodes are configured with insufficient parallelism or the model is not properly parallelized across nodes. High latency and timeouts in a distributed AI inference deployment typically indicate that the model workload is not efficiently distributed across the cluster nodes. Option C is correct because insufficient parallelism—either due to misconfigured node resources (e.g., insufficient vCPUs, GPU cores, or memory bandwidth) or improper model sharding/parallelization—causes some nodes to become bottlenecks while others remain underutilized, leading to queuing delays and eventual timeouts.

Answer

The inference request batch size is too small

Answer

The model is too large for the cluster memory

Answer

The client-side network is slow

Question 4

A team has deployed a generative AI model and needs to monitor inference performance and set up alerts for increased error rates. Which OCI service should they integrate with?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides metrics and alarms for tracking inference performance (e.g., latency, throughput) and error rates from deployed generative AI models. It allows you to set up threshold-based alerts on custom or predefined metrics, enabling proactive incident response. This directly addresses the requirement to monitor inference performance and alert on increased error rates.

Answer

OCI Cloud Guard

Answer

OCI Events

Answer

OCI Logging

Question 5

A DevOps engineer is setting up monitoring and logging for a generative AI inference endpoint. Which three resources should they enable? (Select THREE.)

Accepted Answer

OCI Logging for inference requests and responses. Option B is correct because OCI Logging captures the inference request and response payloads, which is essential for debugging model behavior, auditing prompts/completions, and troubleshooting generative AI endpoint issues. Option C is correct because OCI Monitoring provides time-series metrics such as endpoint latency and error rates, enabling alarms, dashboards, and SLA tracking for the inference service. Option D is correct because OCI Application Performance Monitoring (APM) provides distributed tracing of inference requests across the application stack, helping pinpoint latency bottlenecks and failures in the request path. Option A is not correct because VCN flow logs only capture network-level metadata (source/destination IP, ports, protocol, accept/reject) and do not provide application-level inference visibility. Option E is not correct because OCI Audit logs record control-plane API calls (e.g., create/update/delete of resources) rather than the data-plane inference requests and responses the scenario requires.

Answer

OCI VCN flow logs for network traffic

Answer

OCI Audit logs for all API calls

Question 6

A data scientist wants to deploy a fine-tuned LLM on OCI for inference with low latency. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service (Dedicated AI Cluster). B is correct because OCI Generative AI Service with a Dedicated AI Cluster provides a managed, high-throughput, low-latency inference endpoint for fine-tuned LLMs. It leverages GPU-accelerated infrastructure and optimized serving stacks (e.g., vLLM, TensorRT-LLM) to minimize response times, making it ideal for production inference workloads.

Answer

OCI Data Science Notebook Session

Answer

OCI Data Flow

Answer

OCI Functions

Question 7

A company notices that some inference requests to their deployed model on OCI Generative AI take longer than acceptable. They want to reduce per-request latency. What should they do?

Accepted Answer

Reduce the maximum number of tokens generated. Reducing the maximum number of tokens generated directly decreases the amount of computation required per inference request because the model stops generating output earlier. Since latency is proportional to the number of output tokens produced, this is the most effective single change to reduce per-request response time in OCI Generative AI deployments.

Answer

Enable request batching

Answer

Use a larger model to improve accuracy

Answer

Increase the number of replicas in the deployment

Question 8

A company has deployed a model on a Dedicated AI Cluster and needs to monitor inference performance metrics such as request latency, throughput, and error rates. Which OCI service provides built-in monitoring dashboards for these metrics?

Accepted Answer

OCI Monitoring. OCI Monitoring is the correct service because it provides built-in dashboards and metrics for inference performance, including request latency, throughput, and error rates, specifically for Dedicated AI Cluster deployments. These metrics are automatically collected and visualized in the OCI Monitoring console, allowing real-time tracking of model inference health without additional configuration.

Answer

OCI Logging

Answer

OCI Notifications

Answer

OCI Events

Question 9

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 10

What is the primary benefit of using a Dedicated AI Cluster over On-Demand serving for deploying generative AI models on OCI?

Accepted Answer

Higher throughput and lower latency due to reserved capacity. A Dedicated AI Cluster provides reserved compute capacity on OCI, ensuring consistent high throughput and low latency for generative AI inference workloads. Unlike On-Demand serving, which shares resources and can suffer from contention or cold starts, a Dedicated AI Cluster guarantees that GPU resources are always available for your model, eliminating variability in response times.

Answer

No need to manage model versions

Answer

Automatic scaling to zero when not in use

Answer

Lower cost for variable workloads

Question 11

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. Option D is correct because deploying a custom fine-tuned model in OCI Generative AI requires the model artifacts (weights and configuration) to be stored in an OCI Object Storage bucket, from which the service imports them during creation of the custom model. Option E is correct because a dedicated AI cluster (a hosting cluster sized with the required compute units) must be created to host the custom model, since fine-tuned models are served on dedicated infrastructure rather than the shared on-demand endpoints. Options A and C are incorrect because API Gateway and load balancer configuration are not part of the OCI Generative AI custom model deployment workflow; the service exposes its own endpoint. Option B is incorrect because registering the model in the OCI Data Science Model Catalog is a Data Science workflow, not a requirement for deploying a custom model through the OCI Generative AI service.

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 12

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) (C) is correct because it measures how many inference requests the generative AI endpoint is actually serving per unit time, directly reflecting utilization and capacity of the deployment; low or saturated throughput signals inefficiency or a bottleneck. Average inference latency (D) is correct because it captures the time taken to return a model response, and monitoring it (e.g., via OCI Monitoring metrics on the model deployment endpoint) reveals whether the deployment meets performance expectations or is degraded by resource contention. Together these two operational metrics indicate whether the generative AI service is processing workload efficiently. Number of active users (A) is a usage/adoption metric, not a direct efficiency measure of the deployment. Object Storage bucket size (B) reflects storage consumption, which is unrelated to inference efficiency. Model accuracy on validation set (E) is a model-quality metric measured during training/evaluation, not a runtime efficiency metric.

Answer

Number of active users

Answer

Object Storage bucket size

Answer

Model accuracy on validation set

Question 13

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 14

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 15

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.