1Z0-1127 Deploying and Managing Generative AI on OCI — 15 Questions

Question 1

A company is deploying a generative AI service on OCI using the OCI Data Science service with a large language model (LLM) in a VCN. The model inference endpoint must be accessible only from a private subnet within the same VCN. Which networking component should be configured to enable this?

Accepted Answer

Service Gateway. A Service Gateway enables private subnet resources to access OCI services (including the OCI Data Science model deployment endpoint) without traversing the internet. Since the inference endpoint must be accessible only from a private subnet within the same VCN, the Service Gateway provides the necessary private connectivity by routing traffic over the OCI network fabric, not through a NAT or internet gateway.

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NAT Gateway

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Dynamic Routing Gateway (DRG)

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Internet Gateway

Question 2

A data scientist is fine-tuning a generative AI model on OCI Data Science using a custom container with GPU resources. The training job fails with an out-of-memory error despite the GPU instance having sufficient memory. The job works fine on a smaller dataset. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.

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The training script has a memory leak

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The GPU instance is not supported by OCI Data Science

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The model is not compatible with the PyTorch version

Question 3

An organization wants to deploy a generative AI chatbot using OCI Generative AI service. The chatbot must comply with data residency requirements by ensuring that all data processing occurs within a specific geographic region. What is the best practice to achieve this?

Accepted Answer

Use a dedicated AI cluster in the required region. Option A is correct because OCI Generative AI service allows you to provision a dedicated AI cluster within a specific region, ensuring all model inference and data processing remain within that geographic boundary. This dedicated cluster is isolated from other regions and complies with data residency requirements by design, as no data leaves the chosen region during processing.

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Enable cross-region replication for disaster recovery

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Configure a tenancy-wide policy to restrict region usage

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Use IAM policies to block access from other regions

Question 4

A team has deployed a generative AI model using OCI Data Science model deployment. The endpoint is behind a load balancer. Users report that after 5 minutes of inactivity, the first request takes over 30 seconds to respond, while subsequent requests are fast. What is the most likely cause and solution?

Accepted Answer

The model deployment has an idle timeout that scales down to zero; configure a minimum number of instances or use a warm-up request. The described behavior—first request after 5 minutes of inactivity taking over 30 seconds, with subsequent requests fast—is a classic symptom of an idle timeout that scales the model deployment to zero instances. OCI Data Science model deployments support auto-scaling with an idle timeout (default 5 minutes) that can reduce the number of instances to zero when no requests are received. When a new request arrives, it must wait for a new instance to spin up, causing the delay. The solution is to configure a minimum number of instances (e.g., 1) to keep the model warm, or use a warm-up request to prevent the idle timeout from triggering.

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The load balancer is scaling based on CPU utilization; increase the CPU threshold

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The VCN has a network latency issue; use a different availability domain

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The inference code has a lazy initialization; pre-load the model in the deployment script

Question 5

A company is using OCI Generative AI service with a dedicated AI cluster for text generation. They notice that the latency is higher than expected. The cluster is in the Ashburn region, and users are distributed globally. What is the most effective way to reduce latency?

Accepted Answer

Deploy dedicated AI clusters in regions closer to the users. Latency for globally distributed users is primarily driven by network distance and the speed of light. Deploying dedicated AI clusters in regions closer to the users reduces the physical distance data must travel, directly minimizing network round-trip time (RTT). This is the most effective architectural change because OCI's Generative AI service processes each request on the dedicated cluster and cannot bypass geographic latency through software optimizations alone.

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Enable the OCI Generative AI inference optimizer

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Increase the number of nodes in the dedicated AI cluster

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Use a content delivery network (CDN) to cache responses

Question 6

A machine learning engineer is deploying a fine-tuned Llama 2 model on OCI Data Science model deployment. The deployment fails with an error: 'Model artifact exceeds the maximum allowed size of 10 GB.' The model files total 12 GB. What is the best approach to resolve this?

Accepted Answer

Store the model in Object Storage and reference it in the deployment configuration. Option A is correct because OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.

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Use a different model that is smaller than 10 GB

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Increase the model deployment artifact size limit via a service request

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Compress the model artifact to under 10 GB using gzip

Question 7

A developer wants to call the OCI Generative AI service from a Python application running on an OCI Compute instance. Which method is the most secure for authenticating the API calls?

Accepted Answer

Use instance principals with a dynamic group and policy. Option C is correct because instance principals allow the Compute instance to authenticate to OCI services without storing any credentials on the instance. By assigning a dynamic group and policy, the instance obtains a temporary security token from the OCI metadata service, which is the most secure method for programmatic access from within OCI.

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Use a resource principal

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Use the OCI CLI with a config file containing credentials

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Use an API signing key stored on the instance

Question 8

Which TWO actions are recommended best practices for managing costs when using OCI Generative AI dedicated AI clusters?

Accepted Answer

Use preemptible instances for non-critical inference workloads. Option B is correct because preemptible instances in OCI are significantly cheaper than standard instances and are ideal for non-critical inference workloads that can tolerate interruptions. This aligns with cost optimization best practices by allowing you to use spare compute capacity at a reduced rate for tasks that do not require continuous availability.

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Provision a fixed number of nodes to handle peak load

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Stop the dedicated AI cluster when not in use

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Use pay-as-you-go billing instead of preemptible instances

Question 9

Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?

Accepted Answer

An inference script (e.g., score.py) to handle prediction requests. Option B is correct because OCI Data Science model deployment requires an inference script (typically score.py) to define how the model processes incoming prediction requests. This script is the entry point that loads the model artifact and executes inference logic, making it an essential component for serving predictions.

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A load balancer to distribute traffic

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An API signing key for authentication

Question 10

Which TWO are valid methods to monitor the performance of a generative AI model deployed on OCI Data Science?

Accepted Answer

Use OCI Monitoring service to track custom metrics like latency and throughput. Option B is correct because OCI Monitoring service allows you to define and track custom metrics such as inference latency (e.g., p50/p99 response times) and throughput (requests per second) for your generative AI model deployed on OCI Data Science. This enables real-time performance monitoring and alerting based on thresholds you set, which is essential for production AI workloads.

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Use OCI Notifications to receive alerts on model drift

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Use OCI Events service to trigger retraining on low accuracy

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Use OCI Audit service to review API call logs

Question 11

An administrator runs the above CLI command to check the status of a dedicated AI cluster. The cluster is ACTIVE with capacity 10. However, a user reports that inference requests to this cluster are failing with a '429 Too Many Requests' error. What is the most likely cause?

Accepted Answer

The cluster is hitting the maximum inference requests per minute limit. The '429 Too Many Requests' error is an HTTP status code indicating rate limiting has been exceeded. In OCI Generative AI, dedicated AI clusters have a configurable 'maximum inference requests per minute' limit. Even if the cluster is ACTIVE and has capacity (e.g., 10 nodes), hitting this per-minute request cap will cause the API gateway to reject further requests with a 429 error. The administrator must increase the rate limit or implement client-side throttling to resolve this.

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The cluster does not have enough nodes to handle the load

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The user is not in the same compartment as the cluster

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The cluster is not in ACTIVE state

Question 12

A security administrator wrote the above IAM policy for a compartment named MyCompartment. Users in the GenerativeAIUsers group can successfully list dedicated AI clusters and models in MyCompartment, but when they try to create an inference endpoint using a model from a different compartment (SharedModels), they get an authorization error. What is the most likely missing policy statement?

Accepted Answer

ALLOW GROUP GenerativeAIUsers TO USE generative-ai-models IN COMPARTMENT SharedModels. The error occurs because the user has permission to list models in MyCompartment but not to use a model from SharedModels when creating an inference endpoint. The missing policy must grant the USE permission on generative-ai-models in the SharedModels compartment, as creating an endpoint requires the ability to reference and use the model resource from that compartment. Option D correctly provides this permission.

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ALLOW GROUP GenerativeAIUsers TO MANAGE generative-ai-models IN COMPARTMENT SharedModels

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ALLOW GROUP GenerativeAIUsers TO USE generative-ai-family IN TENANCY

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ALLOW GROUP GenerativeAIUsers TO USE generative-ai-dedicated-ai-clusters IN COMPARTMENT SharedModels

Question 13

A company has deployed a generative AI model on OCI to generate product descriptions. After a recent update, the model started producing outputs with repetitive phrases and poor coherence. The inference endpoint is configured with default parameters. Which single parameter adjustment is most likely to improve output quality?

Accepted Answer

Increase the frequency penalty parameter to 0.5. The correct answer is B because increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.

Answer

Increase the max-tokens parameter to 512

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Increase the temperature parameter to 1.5

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Decrease the top-p parameter to 0.8

Question 14

An organization wants to fine-tune a large language model on OCI using their proprietary data. They are concerned about data privacy and want to ensure that fine-tuning data does not leave the OCI region. Which OCI service should they use to securely store and manage their training data?

Accepted Answer

OCI Object Storage. C is correct because OCI Object Storage is a regional service that stores data within a specific OCI region, ensuring that fine-tuning data does not leave that region. It provides secure, durable, and scalable storage for large datasets, such as training data for LLMs, with encryption at rest and in transit, and supports direct integration with OCI Data Science and Generative AI services for fine-tuning workflows.

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OCI Block Volume

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OCI File Storage

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Oracle Autonomous Database

Question 15

A data scientist is deploying a custom generative AI model using OCI Data Science. After deploying the model to an endpoint, they notice that inference requests are failing with a timeout error when the payload size exceeds 1 MB. What is the most likely cause and solution?

Accepted Answer

The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.. The correct answer is D because OCI Data Science model deployments have a default payload size limit of approximately 1 MB. When inference requests exceed this limit, the load balancer or gateway times out the request. The solution is to increase the payload limit in the deployment configuration, which can be adjusted via the OCI console or API by modifying the `maximumRequestPayloadSize` setting.

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The load balancer is misconfigured; reconfigure the load balancer timeout settings.

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The model server lacks sufficient memory; scale out to more instances.

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The model is not optimized for large payloads; use AutoML to optimize the model.