1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 6 — 15 Questions

Question 1

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?

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

Accepted Answer

Request a service limit increase for dedicated endpoints. 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.

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Delete existing endpoints to free capacity

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Deploy the model to a different OCI region

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

Question 2

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. 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.

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Use a pre-built model with a dedicated endpoint

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Provision a dedicated endpoint for low latency

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Deploy the model on OCI Compute with autoscaling

Question 3

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

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The request exceeds the rate limit

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The endpoint URL is incorrect

Question 4

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.

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OCI API Gateway

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OCI Functions

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OCI Generative AI service

Question 5

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.

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Use a larger model without fine-tuning

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Post-process outputs to remove biased phrases

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Switch to a different pre-built model from OCI

Question 6

A user wants to access the OCI Generative AI service programmatically. Which credential method is recommended for use in a production application running on OCI Compute?

Accepted Answer

Instance principal. Instance principal authentication is the recommended method for production applications running on OCI Compute because it allows the application to authenticate with OCI services without managing or embedding any credentials. The OCI Compute instance assumes a dynamic group and IAM policy that grants it permissions, and the SDK automatically handles token exchange via the instance metadata service, eliminating the need for long-lived secrets.

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API signing keys

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User password and OCID

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Resource principal

Question 7

An organization is deploying a generative AI model that requires GPU acceleration for inference. They are using OCI Data Science Model Deployment. The model is expected to handle variable traffic, with occasional spikes. Which scaling option should they configure to ensure cost-efficiency and responsiveness?

Accepted Answer

Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances. Autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.

Answer

Use OCI Generative AI on-demand API with a serverless endpoint

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Use CPU-only instances and rely on batching

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Deploy with a fixed number of 1 GPU instance

Question 8

Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?

Accepted Answer

Create a model artifact (e.g., pickle, ONNX) with inference code. Option B is correct because deploying a custom generative AI model in OCI Data Science Model Deployment requires a model artifact containing the serialized model (e.g., pickle, ONNX) along with the inference code (such as a score.py defining the predict function) that the deployment's model server will invoke. Option D is correct because the model artifact must be stored in an OCI Object Storage bucket, and the deployment references it via the artifact URI (for example, oci://bucket@namespace/path/to/artifact) so the service can download and load it. Option E is correct because the actual deployment is created with the OCI Data Science Model Deployment service, which provisions the managed HTTP endpoint and model server that hosts the artifact. Options A and C are not required: fine-tuning via the OCI Generative AI service and registering the model in the OCI Generative AI service are separate workflows for the managed Generative AI offering, not steps for deploying a custom model through OCI Data Science Model Deployment.

Answer

Fine-tune the model using OCI Generative AI service

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Register the model in OCI Generative AI service

Question 9

An administrator created the above IAM policies. A member of the GenerativeAIAdmins group reports they cannot invoke the model endpoint. Which permission is missing?

Accepted Answer

Permission to use or manage generative-ai-endpoint. The error occurs because the IAM policy grants permissions for 'generative-ai-model' but not for 'generative-ai-endpoint'. Invoking a model endpoint requires the 'use' or 'manage' permission on the 'generative-ai-endpoint' resource type, as the endpoint is the runtime interface that handles inference requests. Without this permission, the API call to the endpoint is denied, even if the user has access to the underlying model.

Answer

Permission to access the compartment

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Permission to manage generative-ai-model

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Permission to read the model's training data

Question 10

A company is deploying a fine-tuned Cohere model on OCI Generative AI service for real-time inference. They need to ensure low latency even during demand spikes. Which configuration should they prioritize?

Accepted Answer

Use a dedicated AI cluster for the endpoint.. A dedicated AI cluster provides isolated compute resources (GPUs) that are not shared with other tenants or workloads, ensuring consistent low latency even under demand spikes. This is critical for real-time inference because shared endpoints can experience resource contention and throttling during high traffic, while a dedicated cluster guarantees predictable performance.

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Enable model caching on the endpoint.

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Use streaming responses.

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Increase the max tokens parameter.

Question 11

A data scientist needs to generate vector embeddings for a large corpus of text documents to use in a semantic search application. Which OCI service is best suited for this task?

Accepted Answer

OCI Generative AI. OCI Generative AI is the correct choice because it provides a managed service for generating vector embeddings from text using large language models (LLMs) like Cohere. This service is specifically designed for tasks such as semantic search, where embeddings capture the meaning of text to enable similarity comparisons. OCI Vision, Speech, and Language focus on other modalities (images, audio, and NLP tasks like sentiment analysis) and do not offer embedding generation for semantic search.

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OCI Vision

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OCI Speech

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OCI Language

Question 12

A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?

Accepted Answer

OCI Monitoring. A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.

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OCI Events

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OCI Notifications

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OCI Logging

Question 13

An administrator needs to grant a data science team access to create and manage generative AI model endpoints in a specific compartment. Which policy should they create?

Accepted Answer

Allow group DataScientists to manage generative-ai-model-family in compartment Production. The verb 'manage' grants full CRUD (Create, Read, Update, Delete) permissions on the 'generative-ai-model-family' resource type, which is the specific resource family for generative AI model endpoints in OCI. This allows the DataScientists group to create and manage endpoints within the specified compartment without granting broader access to all resources.

Answer

Allow group DataScientists to manage all-resources in compartment Production

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Allow group DataScientists to use generative-ai-model-family in compartment Production

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Allow group DataScientists to read generative-ai-model-family in compartment Production

Question 14

Which THREE of the following are best practices when deploying a generative AI model on OCI?

Accepted Answer

Set up autoscaling for the endpoint.. Option B is correct because configuring autoscaling on an OCI generative AI endpoint (via the endpoint's scaling policy) lets the service dynamically add or remove dedicated AI cluster capacity as inference traffic fluctuates, maintaining performance while controlling cost. Option D is correct because production endpoints should run on a dedicated AI cluster, which provides isolated, reserved GPU capacity and predictable performance rather than sharing a multi-tenant host pool. Option E is correct because enabling content filtering (guardrails/moderation on the endpoint) helps block harmful, unsafe, or policy-violating prompts and responses, which is a security and responsible-AI best practice for generative AI deployments. Option A is wrong because API keys are secrets and must be stored in OCI Vault (or equivalent secret management), never embedded in endpoint configuration. Option C is wrong because disabling logging removes auditability and observability; logging should be enabled and routed to OCI Logging rather than turned off to save costs.

Answer

Store API keys in the model endpoint configuration.

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Disable logging to save costs.

Question 15

An organization is deploying a large language model on OCI using a dedicated AI cluster. They need to minimize inference latency. Which configuration step is most critical?

Accepted Answer

Configure the cluster to use high-bandwidth RDMA networking. RDMA (Remote Direct Memory Access) bypasses the CPU and kernel to transfer data directly between GPU memories, drastically reducing latency and CPU overhead. In a dedicated AI cluster on OCI, high-bandwidth RDMA networking (e.g., using RoCE v2 or InfiniBand) is the most critical step to minimize inference latency because model parallelism and tensor parallelism across nodes depend on fast, low-latency interconnects. Without RDMA, even with optimized model parallelism, the network becomes the bottleneck, increasing per-token latency.

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Set up a load balancer across multiple regions

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Use a single VM shape to reduce network hops

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Disable model parallelism to simplify setup