1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 8 — 15 Questions

Question 1

Which TWO actions should be taken to monitor model drift in a deployed generative AI model? (Select TWO)

Accepted Answer

Compare inference statistics over time. Comparing inference statistics over time (Option A) is correct because model drift in generative AI is detected by monitoring changes in output distributions, token probabilities, or response patterns relative to baseline metrics. This allows you to identify when the model's behavior deviates from expected performance due to shifts in input data or underlying patterns.

Answer

Retrain the model weekly

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Use OCI Data Labeling for new data

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Deploy multiple model versions

Question 2

Which THREE components are essential for a production-grade generative AI deployment on OCI? (Select THREE)

Accepted Answer

OCI Logging for audit. A is correct because OCI Logging provides centralized audit logging for all API calls and resource changes in the generative AI deployment. This is essential for compliance, security monitoring, and troubleshooting in a production environment, as it captures detailed logs of model invocations, data access, and configuration changes.

Answer

OCI Vault for secrets

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OCI Data Flow for data processing

Question 3

A company has deployed a fine-tuned GPT model on OCI Generative AI using a dedicated AI cluster with 2 nodes. The endpoint is used by an internal application that generates product descriptions. Recently, the application started receiving timeouts and slow responses. The monitoring dashboard shows that the cluster's CPU utilization is consistently above 90%, and the request queue is growing. The team has verified that the model and code have not changed. The application traffic has increased by 20% over the past month. What should the team do to resolve the issue?

Accepted Answer

Increase the number of nodes in the dedicated AI cluster from 2 to 4.. The dedicated AI cluster with 2 nodes is experiencing sustained CPU utilization above 90% and a growing request queue due to a 20% increase in traffic. Scaling out the cluster by adding more nodes (from 2 to 4) increases the available compute capacity, allowing the cluster to handle the higher inference load without timeouts. This directly addresses the resource bottleneck without requiring code or model changes.

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Switch to a serverless endpoint to handle variable traffic.

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Reduce the batch size in the inference requests to lower CPU usage.

Answer

Implement a caching layer for frequently requested descriptions.

Question 4

A data science team at a healthcare company has fine-tuned a Llama 2 model using OCI Data Science and registered it in the Model Catalog. They want to deploy it as a managed endpoint using OCI Generative AI. The model requires 64 GB of GPU memory. The team has created a dedicated AI cluster with a single node shape that has 48 GB GPU memory. When they attempt to deploy the model, the deployment fails with an error indicating insufficient resources. The team has verified that the model artifact is correct and that the compartment policies allow deployment. What should the team do to successfully deploy the model?

Accepted Answer

Select a node shape with higher GPU memory, such as 80 GB.. The model requires 64 GB of GPU memory, but the dedicated AI cluster uses a node shape with only 48 GB. The only way to satisfy the memory requirement is to select a node shape with higher GPU memory, such as 80 GB, as OCI Generative AI managed endpoints require a single node to host the entire model. Increasing nodes or enabling model parallelism does not help because OCI Generative AI does not support distributed inference across nodes for managed endpoints, and reducing precision may not guarantee the model fits or may degrade accuracy.

Answer

Increase the number of nodes in the cluster to 2.

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Enable model parallelism to split the model across nodes.

Answer

Reduce the model's precision from FP16 to INT8 to lower memory usage.

Question 5

A multinational corporation uses OCI Generative AI to power a customer support chatbot. The chatbot uses a fine-tuned model deployed on a dedicated AI cluster in the us-ashburn-1 region. The application is used globally, and users in Europe are experiencing high latency (over 2 seconds) compared to users in North America (under 500 ms). The company has a requirement to keep all data within the US due to compliance, so they cannot deploy in Europe. The latency is not due to network bandwidth but due to the inference time. The monitoring shows that the cluster is at 80% utilization during peak hours. The team wants to reduce the latency for European users without violating data residency. What is the best course of action?

Accepted Answer

Optimize the model using techniques like quantization or pruning to reduce inference time.. The latency issue is explicitly due to inference time, not network bandwidth or cluster utilization. Model optimization techniques like quantization (reducing precision of weights from FP32 to INT8) and pruning (removing redundant neurons) directly reduce the computational cost per inference, thereby lowering the response time without moving data or changing the deployment region. This approach satisfies the data residency constraint while addressing the root cause of high latency for European users.

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Implement an edge caching layer in Europe to serve common queries.

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Increase the number of nodes in the cluster to distribute the load.

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Deploy an additional endpoint in a European region and use a global load balancer.

Question 6

Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?

Accepted Answer

Missing GPU acceleration for inference. GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.

Answer

High network latency between the client and the model endpoint

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Model is too large for the VM.Standard.E3.Flex shape

Answer

Insufficient CPU resources for the model size

Question 7

A data scientist deployed a fine-tuned Llama 2 7B model on OCI Model Deployment with a single VM.GPU.A10.1 shape. Users report average latency of 3 seconds per request, which is too high for the intended real-time application. The model is used for short text generation (max 128 tokens). The data scientist wants to reduce per-request latency without significant accuracy loss. Which action would be most effective?

Accepted Answer

Apply 4-bit quantization using AWQ. 4-bit quantization using AWQ reduces the model's memory footprint and computational requirements by compressing weights to 4-bit integers, which directly decreases inference latency on the VM.GPU.A10.1 shape. This technique preserves most of the model's accuracy while enabling faster token generation, making it the most effective single action for reducing per-request latency in a real-time short text generation scenario.

Answer

Increase the number of workers per replica

Answer

Increase the max_tokens parameter for the model

Answer

Enable response streaming for the model endpoint

Question 8

A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?

Accepted Answer

OCI Data Science Model Deployment with OCI Monitoring and OCI Logging. OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.

Answer

OCI Compute with custom Docker container and Prometheus monitoring

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OCI Functions with API Gateway for authentication

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OCI Data Flow with OCI Data Catalog for model registry

Question 9

A company is deploying a generative AI model for a real-time inference API. To ensure high availability and cost efficiency under variable load, which two configurations should they implement? (Choose two.)

Accepted Answer

Set the number of model deployment replicas to at least 2. Deploying at least two replicas ensures high availability by eliminating a single point of failure; if one replica fails, the other can still serve inference requests. This is a standard best practice for production workloads on OCI, where model deployment replicas are distributed across fault domains to maintain service continuity.

Answer

Use a single replica with a larger GPU to handle all traffic

Answer

Deploy the model in a single availability domain to simplify management

Answer

Disable connection draining on the load balancer

Question 10

A company is deploying a generative AI model on OCI for an internal application that must comply with strict security policies. The model will be accessed by a limited group of users. Which three actions should the administrator take to ensure security? (Choose three.)

Accepted Answer

Deploy the model in a private VCN subnet. Deploying the model in a private VCN subnet ensures that the model endpoint is not exposed to the internet, which is a fundamental security requirement for compliance with strict security policies. By placing the model in a private subnet, all traffic must traverse through a bastion host, VPN, or FastConnect, providing network isolation and reducing the attack surface. This aligns with OCI's shared responsibility model where the customer controls network security.

Answer

Expose the model endpoint to the internet for ease of access

Answer

Disable audit logging to minimize storage costs

Question 11

Your organization uses OCI Data Science to train a generative AI model for code generation. After training, you want to deploy it as a REST API. You create a model deployment using the OCI console, but after 30 minutes the deployment status is still 'Creating'. You check the logs and see the message: 'Insufficient capacity for shape VM.GPU.A10.1 in availability domain AD-1'. The deployment is configured with a single replica. You have verified your tenancy has sufficient service limits for GPU instances. What should you do to resolve this issue quickly?

Accepted Answer

Change the deployment to use a different GPU shape, such as VM.GPU.A10.2. The error indicates that the specific GPU shape VM.GPU.A10.1 lacks capacity in the current availability domain. Switching to a different GPU shape, such as VM.GPU.A10.2, which uses a different instance configuration, can bypass the capacity constraint without requiring a region change or service limit increase. This is the fastest resolution because it directly addresses the availability domain capacity issue while keeping the deployment in the same region and AD.

Answer

Delete the deployment and create it in a different region with more GPU capacity

Answer

Request a service limit increase for GPU shapes

Answer

Wait for 1 hour and check again; capacity may become available

Question 12

You manage a generative AI model deployed on OCI Model Deployment that serves a chatbot application. The model is a 13B parameter LLM on a VM.GPU.A100.1 shape. Recently, you rolled out a new version of the model that is supposed to improve response quality. However, after the update, the application starts returning HTTP 500 errors and memory usage spikes. You need to update to the new version without causing downtime. The current deployment has 2 replicas with autoscaling enabled. Which strategy should you use to safely deploy the new model version?

Accepted Answer

Create a second deployment with the new model, test it, then shift traffic using a load balancer. It implements a blue/green deployment strategy: you create a second deployment with the new model, test it in isolation, and then shift traffic using a load balancer. This avoids downtime and allows you to validate the new model before exposing it to production traffic, which is critical given the observed HTTP 500 errors and memory spikes.

Answer

Directly update the existing model deployment with the new model artifact

Answer

Stop the existing deployment, update the model artifact, then start the deployment

Answer

Increase the number of replicas to 4, then update the model

Question 13

Your team is deploying a generative AI model for a clinical decision support system. The model must meet HIPAA compliance requirements. You have trained a model using OCI Data Science and now need to deploy it so that patient data is protected. The application requires real-time inference. Which set of actions should you take to ensure compliance while maintaining low latency?

Accepted Answer

Deploy in a private VCN subnet, use a service gateway, store keys in OCI Vault, and enable OCI Logging and OCI Audit. Deploying the model in a private VCN subnet ensures the inference endpoint is not exposed to the internet, meeting HIPAA's requirement for network isolation. Using a service gateway allows private connectivity to OCI services without traversing the internet, while storing encryption keys in OCI Vault enables customer-managed key control for data at rest. Enabling OCI Logging and OCI Audit provides the necessary audit trail for compliance, and the private subnet with service gateway keeps latency low by avoiding internet hops.

Answer

Use OCI Functions with API Gateway and allow anonymous access

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Deploy in a public subnet with HTTPS and enable OCI Audit

Answer

Use OCI Data Flow for batch inference and store results in Object Storage with SSE

Question 14

You deployed a generative AI model on OCI Model Deployment with autoscaling configured based on average CPU utilization. The model is a large language model that heavily utilizes the GPU. During peak hours, the scaling is too slow to keep up with demand, resulting in high latency for users. You want to improve the responsiveness of autoscaling. Which change should you make?

Accepted Answer

Use GPU utilization as the scaling metric instead of CPU utilization. The model heavily utilizes GPU, not CPU. Autoscaling based on CPU utilization is irrelevant for GPU-bound workloads, leading to delayed scale-out. Using GPU utilization as the scaling metric directly reflects the actual resource bottleneck, enabling faster and more accurate scaling decisions.

Answer

Decrease the target CPU utilization threshold for scale-out

Answer

Increase the maximum number of replicas in the autoscaling configuration

Answer

Increase the cooldown period between scale-out events

Question 15

Your company uses OCI Data Science for model development and deployment. You have a generative AI model that requires dynamic batching for efficient inference. You deployed the model using the OCI Model Deployment service with a custom inference script in a Docker container. However, you notice that the batch size is fixed at 1, leading to low throughput. The model can process multiple requests together efficiently. You want to implement dynamic batching to increase throughput without significantly increasing latency for individual requests. What is the best approach?

Accepted Answer

Implement a queuing mechanism in the inference script that collects incoming requests and processes them in batches. Dynamic batching must be implemented at the application level within the custom inference script when using OCI Model Deployment. The service does not provide built-in request batching; instead, you need to collect incoming requests in a queue and process them together in a single forward pass, which maximizes GPU utilization while controlling latency via a timeout or max batch size.

Answer

Modify the model deployment to use a larger GPU shape to handle larger batches

Answer

Enable the model deployment's built-in request batching feature

Answer

Use OCI Streaming service to buffer requests and then invoke the model in batches from a consumer