1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 5 — 15 Questions

Question 1

A data scientist is preparing to fine-tune a foundation model on OCI. Which two actions should they take to optimize costs? (Select TWO.)

Accepted Answer

Use the smallest model that meets accuracy requirements. Using the smallest model that meets accuracy requirements directly reduces the number of parameters and computational operations required during fine-tuning. On OCI, larger models consume significantly more GPU memory and compute hours, so selecting the minimal viable model minimizes both training time and associated costs. This aligns with cost optimization best practices for generative AI workloads.

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Use a single OCPU shape to minimize per-hour cost

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Use spot preemptible instances to save on compute

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Store training data in Archive Storage to reduce storage costs

Question 2

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. OCI Logging captures detailed logs of inference requests and responses, which is essential for auditing, debugging, and analyzing the behavior of a generative AI endpoint. This service provides a centralized repository for log data, enabling DevOps engineers to track input prompts and model outputs for compliance and troubleshooting purposes.

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OCI VCN flow logs for network traffic

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OCI Audit logs for all API calls

Question 3

An enterprise is deploying a generative AI model that must comply with data residency regulations. Which two configurations should they implement? (Select TWO.)

Accepted Answer

Set up OCI IAM policies to prevent data egress from the region for the model's resources. OCI IAM policies can explicitly deny data egress from a specific region, ensuring that the generative AI model's resources (such as training data, model artifacts, and inference endpoints) remain within the region that satisfies data residency regulations. This is achieved by writing policy statements that restrict the movement of data across regional boundaries, which is a direct control for compliance.

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Enable OCI Logging for all API calls

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Use OCI Object Storage with cross-region replication for redundancy

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Store encryption keys in an OCI Vault in a different region

Question 4

Refer to the exhibit. A user receives this error when using the OCI CLI to chat with a model. What is the most likely cause?

Accepted Answer

The user does not have the required IAM policy to invoke the model.. The error occurs because the user lacks the necessary IAM policy to invoke the model. In OCI, even if the model is deployed and the CLI is correctly configured, the IAM policy must grant the user or group the 'inference' permission on the specific model or model family. Without this policy, the OCI CLI returns an authorization error when attempting to chat with the model.

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The model is not deployed.

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The model ID is incorrect.

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The OCI CLI is not configured with the correct region.

Question 5

Refer to the exhibit. A team created this dedicated AI cluster. However, when they try to create a model deployment, the deployment fails with an error indicating insufficient public IPs. What change to the cluster configuration should they make?

Accepted Answer

Change assignPublicIp to true.. The error indicates insufficient public IPs because the cluster's subnet does not have enough available public IP addresses. Setting `assignPublicIp` to `true` in the cluster configuration allows the cluster to automatically allocate public IPs from the subnet's pool, resolving the shortage. This is required for model deployments that need public endpoints.

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Increase the nodeCount to 8.

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Attach a different subnet that has more available public IPs.

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Change the AI cluster shape to VM.GPU.A10.2.

Question 6

Refer to the exhibit. A data scientist received this output after submitting a fine-tuning job. What is the most effective change to resolve the out-of-memory error?

Accepted Answer

Increase the number of nodes in the cluster.. The out-of-memory error during fine-tuning indicates that the model's memory requirements exceed the available resources on the current node. Increasing the number of nodes in the cluster distributes the model parameters, gradients, and optimizer states across multiple GPUs or nodes, effectively increasing the total memory capacity and resolving the OOM error. This is a standard approach in distributed training frameworks like PyTorch DDP or FSDP, which OCI Data Science supports.

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Increase the sequence length.

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Reduce the learning rate.

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Decrease the number of fine-tuning epochs.

Question 7

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.

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OCI Data Science Notebook Session

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OCI Data Flow

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OCI Functions

Question 8

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.

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Enable request batching

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Use a larger model to improve accuracy

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Increase the number of replicas in the deployment

Question 9

A financial institution needs to deploy a fine-tuned model on OCI with strict data residency requirements. They must ensure that data used for inference never leaves a specific OCI region. The model is stored in Object Storage in the same region. What additional configuration is needed?

Accepted Answer

Configure the dedicated AI cluster to use a private endpoint and restrict access to the region. Configuring the dedicated AI cluster to use a private endpoint ensures that inference traffic stays within the OCI region and never traverses the public internet. This satisfies the strict data residency requirement by keeping all data and model inference within the designated region, while the model stored in Object Storage in the same region is accessed via the private endpoint without leaving the region.

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Use OCI Data Transfer service to move data

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Set up a VPN connection to on-premises

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Enable cross-region replication on the bucket

Question 10

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. 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.

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/models

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/inference

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/chat

Question 11

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.

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OCI Logging

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OCI Notifications

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OCI Events

Question 12

An AI team is fine-tuning a large language model using OCI Data Science and plans to deploy the fine-tuned model using the Generative AI service's custom model deployment. What is the required format for the model artifacts?

Accepted Answer

A .zip archive containing model weights and configuration files. The OCI Generative AI service requires custom model artifacts to be packaged as a .zip archive containing the model weights, configuration files (e.g., config.json, tokenizer files), and any necessary inference code. This format ensures the service can extract and load the model correctly into its managed inference infrastructure, aligning with the standard Hugging Face model repository structure.

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A Git repository URL

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A single .pth file

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A Docker image with the model and inference code

Question 13

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.

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Train separate models for each team

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Encrypt model artifacts with different keys for each team

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Use network security lists to isolate traffic

Question 14

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.

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No need to manage model versions

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Automatic scaling to zero when not in use

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Lower cost for variable workloads

Question 15

A developer is getting a 401 Unauthorized error when calling the OCI Generative AI inference API. What is the most likely cause?

Accepted Answer

The request is missing or has an invalid authentication signature. A 401 Unauthorized error specifically indicates a failure in authentication, not authorization or resource availability. The OCI Generative AI inference API requires every request to include a valid signature based on the OCI Signature Version 1 algorithm (RFC 2104 HMAC-SHA256). If the request is missing the Authorization header or the signature is malformed (e.g., incorrect key ID, mismatched signing string, or expired timestamp), the API gateway rejects it with a 401 response.

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The API endpoint has reached its rate limit

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The model does not support the requested parameters

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The model is not deployed