1Z0-1127 Deploying and Managing Generative AI on OCI Practice Test 2 — 15 Questions

Question 1

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 They observe that the chatbot sometimes provides outdated information because the model was trained on data up to 2022.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.. Option C is correct because 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.

Answer

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

Answer

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

Question 2

A team is deploying a generative AI model using OCI Functions for serverless inference. They are experiencing cold start latency of over 10 seconds for the first invocation after idle periods. What is the best strategy to reduce cold start latency?

Accepted Answer

Use provisioned concurrency to keep a set number of function instances warm.. Option B is correct because OCI Functions supports provisioned concurrency, which keeps a specified number of instances warm. Option A (increasing memory) can reduce cold start but not as effectively. Option C (reducing timeout) might cause failures. Option D (using OCI Data Flow) is for data processing, not inference.

Answer

Migrate the inference to OCI Data Flow for better performance.

Answer

Reduce the function timeout to force faster execution.

Answer

Increase the memory allocation for the function.

Question 3

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. Option C is correct because 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

Answer

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

Answer

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

Question 4

A company is deploying a large generative AI model on OCI using GPU compute instances. They want to optimize inference cost while maintaining acceptable latency. Which TWO strategies should they implement?

Accepted Answer

Select the smallest GPU instance type that meets latency requirements.. Option B is correct because selecting the smallest GPU instance type that meets latency requirements directly reduces compute cost per inference without sacrificing user experience. This aligns with OCI's pay-as-you-go GPU pricing, where larger instances incur higher hourly costs. The key is to right-size the GPU based on model memory footprint and inference throughput, not to over-provision.

Answer

Enable provisioned concurrency on all models.

Answer

Increase the max-tokens parameter to generate longer responses.

Answer

Deploy the model on multiple large GPU instances to handle peak load.

Question 5

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 is correct because gradient accumulation allows the model to simulate the effect of a larger batch size without increasing memory usage. Instead of computing gradients over a single large batch, the optimizer accumulates gradients over several smaller batches before performing a weight update. This technique effectively decouples the batch size from memory consumption, enabling training on large models or high-resolution inputs that would otherwise cause an out-of-memory error.

Answer

Increase the learning rate to speed up training.

Answer

Increase the number of training epochs.

Question 6

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.. Option D is correct because 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 7

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.. Option C is correct because 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 8

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.. Option C is correct because 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.

Question 9

A company has fine-tuned a large language model using OCI Generative AI service. When attempting to deploy the model to a dedicated endpoint, the deployment fails with an error indicating insufficient capacity. Which action should be taken to resolve this issue?

Accepted Answer

Request a service limit increase for dedicated endpoints. Option C is correct because capacity quotas can be increased by requesting a service limit increase. Option A is wrong because deploying to a different region may not address capacity issues in the current region. Option B is wrong because using a pre-built model would not leverage the fine-tuned model. Option D is wrong because deleting other endpoints may not be necessary if quota increase is possible.

Answer

Delete existing endpoints to free capacity

Answer

Deploy the model to a different OCI region

Answer

Use a pre-built model instead of the fine-tuned model

Question 10

A startup wants to minimize costs when using OCI Generative AI service for a chatbot application that experiences sporadic usage. Which deployment strategy is most cost-effective?

Accepted Answer

Use the serverless on-demand API without dedicated endpoints. Option B is correct because serverless on-demand pricing charges only for usage, ideal for sporadic workloads. Option A is wrong because dedicated endpoints incur hourly costs regardless of usage. Option C is wrong because pre-built models may also have per-request costs but dedicated endpoints are not cost-effective. Option D is wrong because running models on OCI Compute adds management overhead and costs.

Answer

Use a pre-built model with a dedicated endpoint

Answer

Provision a dedicated endpoint for low latency

Answer

Deploy the model on OCI Compute with autoscaling

Question 11

A global enterprise is deploying a generative AI application that requires high availability across multiple OCI regions. The application must automatically fail over to a secondary region if the primary region becomes unavailable. What is the recommended architecture to achieve this?

Accepted Answer

Deploy endpoints in two regions behind an OCI Load Balancer with cross-region failover. Option A is correct because OCI Load Balancer supports cross-region failover by distributing traffic across backend sets in multiple regions, enabling automatic failover to a secondary region when the primary region becomes unavailable. This architecture ensures high availability for generative AI applications by leveraging health checks and failover policies at the load balancer level, which is the recommended approach for multi-region active-passive setups.

Answer

Deploy OCI Generative AI endpoints in two regions and use a global DNS round-robin

Answer

Use OCI Streaming to replicate requests between regions

Answer

Use DNS failover with a single endpoint in the primary region

Question 12

A data scientist is using the OCI Generative AI service to generate text completions. The API calls are returning HTTP 400 errors with the message 'Invalid model parameters'. What is the most likely cause?

Accepted Answer

One or more model parameters (e.g., temperature, top_p) are outside the accepted range. The HTTP 400 error with 'Invalid model parameters' directly indicates that one or more of the parameters sent in the API request (such as temperature, top_p, max_tokens, or stop sequences) are outside the acceptable range defined by the OCI Generative AI service. For example, temperature must be between 0 and 1, and top_p between 0 and 1, and sending a value like 2.0 for temperature would trigger this error. The other options (expired key, rate limit, incorrect endpoint) would produce different HTTP status codes or error messages.

Answer

The API key is expired

Answer

The request exceeds the rate limit

Answer

The endpoint URL is incorrect

Question 13

A developer wants to deploy a custom generative AI model that was trained using OCI Data Science. Which service should they use to expose the model as an API endpoint?

Accepted Answer

OCI Data Science Model Deployment. B is correct because OCI Data Science Model Deployment is specifically designed to host and serve machine learning models as REST API endpoints. It directly deploys models trained in OCI Data Science, managing the underlying infrastructure, scaling, and providing a secure HTTPS endpoint for inference requests.

Answer

OCI API Gateway

Answer

OCI Functions

Answer

OCI Generative AI service

Question 14

A financial services company is concerned about data privacy when using OCI Generative AI service for processing sensitive customer data. They want to ensure that their data is not used to improve the model and is encrypted at rest and in transit. Which combination of OCI features should they implement?

Accepted Answer

Provision a dedicated endpoint, configure data privacy opt-out, and use OCI Vault for encryption keys. Option B is correct because it addresses all three requirements: a dedicated endpoint ensures network isolation and encryption in transit, data privacy opt-out prevents OCI from using customer data for model improvement, and OCI Vault integration allows customers to manage their own encryption keys for data at rest, meeting the financial services company's strict data privacy and encryption needs.

Answer

Use OCI Object Storage buckets with encryption to store prompts and responses

Answer

Deploy the model in an OCI Data Science project with a private endpoint

Answer

Use the on-demand API with default encryption

Question 15

A company is using OCI Generative AI service to generate product descriptions. They notice that the model sometimes generates biased content. Which approach should they take to mitigate bias while maintaining performance?

Accepted Answer

Fine-tune the model with a balanced, curated dataset that reduces bias. Fine-tuning with a balanced, curated dataset directly addresses the root cause of bias by adjusting the model's internal weights to reduce reliance on biased patterns in the original training data. This approach preserves the model's generative performance for product descriptions because it retrains only on domain-specific, unbiased examples, unlike post-processing which merely filters outputs without correcting the underlying model behavior.

Answer

Use a larger model without fine-tuning

Answer

Post-process outputs to remove biased phrases

Answer

Switch to a different pre-built model from OCI