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

Question 1

A company is deploying a fine-tuned model on an OCI Generative AI dedicated AI cluster. They need to ensure that the deployment meets high availability and scalability requirements. Which two actions should they take? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create the dedicated AI cluster with multiple units to enable internal load balancing and redundancy.. To achieve high availability and scalability, you should create the dedicated AI cluster with multiple units, which provides internal redundancy and load balancing. Additionally, the application should implement retry logic with exponential backoff to handle transient failures. These two actions together ensure that the deployment can handle load and recover from temporary issues. The other options are either not supported or not applicable to OCI Generative AI dedicated AI clusters.

Answer

Use OCI Load Balancer to distribute traffic across multiple endpoints of the same dedicated AI cluster.

Answer

Deploy the model to multiple dedicated AI clusters in different availability domains and use a load balancer.

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Enable automatic scaling on the dedicated AI cluster to add units during peak load.

Question 2

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 3

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.

Answer

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 4

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. 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.

Answer

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 5

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 deployments require an inference script such as score.py that defines the predict function and handles incoming prediction requests through the model artifact's runtime. Option C is correct because the model artifact (a ZIP or folder containing the serialized model files, dependencies, and the inference script) is the packaged unit that OCI Data Science deploys and serves. Option E is correct because a deployment configuration is required to specify the shape/resources (for example, VM.Standard.E4.Flex with a given OCPU and memory), the environment (conda environment or container image), and other runtime settings for the deployment. Option A is not required because OCI Data Science model deployment provisions its own managed endpoint and load balancing internally, so the user does not supply a load balancer. Option D is not required because authentication to the deployment endpoint is handled through OCI IAM policies, resource principals, or auth tokens, not through an API signing key configured as a deployment component.

Answer

A load balancer to distribute traffic

Answer

An API signing key for authentication

Question 6

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 supports custom metrics, so you can publish model-specific metrics such as inference latency and throughput via the Monitoring API/CLI and build alarms on them. Option C is correct because OCI Logging can collect and store inference logs (e.g., via the Logging service and agents or the Logging API), which lets you inspect request/response data and analyze model behavior over time. Option A is not a monitoring method per se: OCI Notifications only delivers messages/alerts that must first be triggered by an alarm or event, and it does not itself detect model drift. Option D is not a valid monitoring method: OCI Events reacts to state changes in OCI resources, not to model accuracy metrics, so it cannot trigger retraining on low accuracy by itself. Option E is not a monitoring method for model performance: OCI Audit records control-plane/API activity for compliance and security, not inference latency, throughput, or model quality.

Answer

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 7

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.

Answer

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 8

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.

Answer

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 9

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. 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 10

A company is using OCI Generative AI service to power a customer support chatbot. They observe that the chatbot sometimes provides outdated information because the model was trained on data up to 2022. They want to incorporate real-time knowledge without retraining the model. Which approach should they use?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pattern using OCI OpenSearch.. Retrieval-Augmented Generation (RAG) allows the model to access real-time information from an external knowledge base, such as OCI OpenSearch, without retraining. This pattern retrieves relevant documents or data at inference time and injects them into the prompt, enabling the model to answer with up-to-date context. It directly addresses the need for real-time knowledge while keeping the base model static.

Answer

Increase the max-tokens parameter to allow longer responses.

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Use prompt engineering to instruct the model to ignore old information.

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Fine-tune the model with recent data from 2023 onwards.

Question 11

An administrator needs to ensure that only specific users in the finance department can invoke a generative AI model deployed on OCI. Which IAM policy should be used?

Accepted Answer

allow group finance_group to use generative-ai-model in compartment finance. The 'use' verb in an OCI IAM policy grants the minimum required permissions to invoke a generative AI model without allowing management actions like creating or deleting models. The policy scopes access to the 'finance_group' group and the 'finance' compartment, ensuring only specific users in the finance department can invoke the model.

Answer

allow group admins to use generative-ai-model in compartment finance

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allow group finance_group to manage generative-ai-model in compartment finance

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allow any-user to use generative-ai-model in compartment finance

Question 12

A team is fine-tuning a generative AI model on OCI using a custom dataset. The training job fails with an out-of-memory error. Which THREE actions should they take to resolve this issue?

Accepted Answer

Use gradient accumulation to simulate larger batch sizes.. Option A (gradient accumulation) is correct because it lets the model process small micro-batches that fit in GPU memory while accumulating gradients across several steps to emulate a larger effective batch size, directly reducing peak memory usage. Option C (a larger GPU shape with more memory) is correct because OCI GPU shapes such as those with more VRAM (for example, A100 or H100-based instances) provide additional memory capacity for model parameters, activations, and optimizer states, eliminating the out-of-memory condition. Option D (reduce the batch size) is correct because lowering the number of samples processed per forward/backward pass proportionally reduces activation memory, which is the most common cause of OOM errors during fine-tuning. Option B is not correct because increasing the learning rate affects convergence speed and stability, not memory consumption, and could even worsen training. Option E is not correct because increasing the number of epochs only extends training duration and does not reduce per-step memory requirements.

Answer

Increase the learning rate to speed up training.

Answer

Increase the number of training epochs.

Question 13

A large enterprise is deploying a generative AI model for internal document summarization. The model is deployed on OCI Data Science using a custom container. The inference endpoint is behind a public load balancer. The security team requires that all traffic between the client and the endpoint be encrypted in transit and that the endpoint not be accessible from the public internet. The current setup uses a public load balancer with an SSL certificate. The VCN has a public subnet for the load balancer and a private subnet for the model deployment. The security team is concerned that the load balancer is publicly accessible. The enterprise wants to maintain high availability and low latency. What should the architect do to meet the security requirements?

Accepted Answer

Replace the public load balancer with a private load balancer in a private subnet, and attach an SSL certificate for encryption.. Replacing the public load balancer with a private load balancer in a private subnet ensures the endpoint is not accessible from the public internet, while attaching an SSL certificate maintains encryption in transit. This satisfies both security requirements without sacrificing high availability or low latency, as the private load balancer still provides load balancing and TLS termination within the VCN.

Answer

Use a site-to-site VPN to connect clients to the VCN and access the endpoint via private IP.

Answer

Remove the load balancer and use a service gateway to access the model deployment directly from the VCN.

Answer

Keep the public load balancer but add a Web Application Firewall (WAF) to block unauthorized IPs.

Question 14

A healthcare company is using OCI Generative AI to analyze patient records and generate clinical summaries. The company must comply with HIPAA regulations, which require that all protected health information (PHI) be encrypted at rest and in transit, and that access be logged and audited. The current architecture uses an OCI Data Science model deployment with a public endpoint. The model is stored in an OCI Object Storage bucket that is publicly accessible for testing. The company is now moving to production. The compliance officer has flagged the following issues: (1) The model endpoint is publicly accessible. (2) The bucket containing the model is public. (3) No audit logs are enabled. The company wants to remediate these issues while maintaining the ability to invoke the model from on-premises applications via a secure connection. Which set of actions should the architect take?

Accepted Answer

Switch the model endpoint to a private subnet with a service gateway, update the bucket policy to block all public access, enable OCI Audit service, and set up a VPN or FastConnect for on-premises access.. It addresses all three compliance issues: moving the model endpoint to a private subnet with a service gateway removes public exposure, making the bucket private with a policy that blocks all public access secures the model artifacts, and enabling OCI Audit provides the required logging. Additionally, setting up a VPN or FastConnect allows secure on-premises access without exposing the endpoint to the public internet, fully satisfying HIPAA encryption and audit requirements.

Answer

Switch the model endpoint to a private subnet with a service gateway, change the bucket to be accessible only via pre-authenticated requests, and enable OCI Logging for the model deployment.

Answer

Keep the public endpoint but restrict access using IAM policies and source IP addresses, make the bucket private, and enable OCI Audit.

Answer

Use a public load balancer with SSL termination, restrict bucket access to the load balancer's OCID, and enable OCI Audit.

Question 15

You are deploying a generative AI solution on OCI for a healthcare client that requires strict data residency (data must remain in the EU) and low-latency inference. The solution uses a fine-tuned LLM model (7B parameters) stored in Object Storage in the Frankfurt region. You have set up an OCI Data Science model deployment endpoint with GPU shape VM.GPU.A10.1, using a single replica. During load testing with 50 concurrent users, you observe high latency (average 8 seconds per request) and occasional 504 gateway timeouts. The model deployment logs show no errors, and the model loads successfully. You have confirmed that the Object Storage bucket is in the same region and that the network latency between the client and the endpoint is minimal (under 5 ms). Which action should you take to reduce latency and eliminate timeouts?

Accepted Answer

Increase the number of replicas to 3 and enable autoscaling based on CPU utilization.. The high latency and 504 timeouts with 50 concurrent users indicate that a single GPU replica is overwhelmed by the request queue. Increasing replicas to 3 distributes the load across multiple endpoints, while enabling autoscaling based on CPU utilization ensures dynamic scaling to handle traffic spikes. This directly reduces per-request latency and eliminates timeouts without violating data residency requirements.

Answer

Increase the model deployment endpoint timeout setting from 60 seconds to 300 seconds in the OCI console.

Answer

Upgrade the model deployment shape to VM.GPU.A100.4 and keep a single replica.

Answer

Move the model deployment to the US East (Ashburn) region to leverage lower-cost GPU capacity and reduce latency.