20+ practice questions focused on Deploying and Managing Generative AI on OCI — one of the most tested topics on the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127-25 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Deploying and Managing Generative AI on OCI PracticeYou 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?
Explanation: 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.
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?
Explanation: The error indicates that the OCI tenancy has reached its quota for dedicated endpoints. Requesting a service limit increase is the correct action because OCI enforces service limits on dedicated endpoints to manage resource allocation, and increasing this limit allows the deployment to proceed.
A user runs the CLI command shown but receives only one model in the list, even though they know there are more models available in the compartment. What is the most likely reason?
Explanation: C is correct because the OCI CLI's `oci generative-ai model list` command, by default, returns only the first page of results (typically 25 items). To retrieve all models in the compartment, the `--all` flag must be explicitly added to the command, which internally handles pagination by making successive API calls until all results are collected. Without this flag, the CLI only shows the first page, which may contain only one model if the page size is small or if the model appears first in the list.
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?
Explanation: 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.
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?
Explanation: 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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Practice all Deploying and Managing Generative AI on OCI questions1. Baseline your knowledge
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The exact number varies per candidate. Deploying and Managing Generative AI on OCI is tested as part of the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127-25 blueprint. Practicing with targeted Deploying and Managing Generative AI on OCI questions ensures you can handle any format or difficulty that appears.
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