1Z0-1127-25 Using OCI Generative AI Service Practice Test 4 — 15 Questions

Question 1

A developer runs this CLI command but receives only one response instead of three. What is the most likely cause?

Accepted Answer

The model specified does not support multiple generations. The OCI Generative AI service's `cohere.command` model does not support the `--num-generations` parameter for generating multiple responses in a single invocation. The `--num-generations` parameter is only supported by certain models like `cohere.command-light` or `meta.llama-2-70b-chat`, and using it with an unsupported model results in a single generation being returned, ignoring the parameter.

Answer

The parameter --num-generations is misspelled; should be --num-generations-to-generate

Answer

The --max-tokens limit is too low to return multiple generations

Answer

The API version is outdated and does not support the num-generations parameter

Question 2

An administrator creates this IAM policy to allow a group to use a specific generative AI model. However, users report a 403 Forbidden error. What is the most likely issue?

Accepted Answer

The policy syntax is invalid for OCI IAM; missing required fields. The most likely issue is that the policy syntax is invalid because OCI IAM policies require a 'verb' (e.g., 'allow', 'deny') at the beginning of each statement. Without this required field, the policy is syntactically incorrect and will be rejected by the IAM service, resulting in a 403 Forbidden error for all users in the group.

Answer

The resource OCID is incorrect

Answer

The policy does not specify the compartment where the model resides

Answer

The action name is wrong; it should be 'ai:generate-text' (correct already)

Question 3

A company wants to use OCI Generative AI service to automatically generate product descriptions for an e-commerce catalog. They have 10,000 products. What is the best approach to ensure high-quality, consistent descriptions?

Accepted Answer

Fine-tune a base model on a dataset of existing product descriptions.. Fine-tuning a base model on a dataset of existing product descriptions is the best approach because it adapts the model to the specific domain, style, and vocabulary of the e-commerce catalog. This ensures high-quality, consistent outputs across 10,000 products by learning the patterns and terminology from the company's own data, rather than relying on generic or template-based methods.

Answer

Use a pre-trained summarization model.

Answer

Use a template-based generation with keyword insertion.

Answer

Use the built-in chat model with few-shot examples in the prompt.

Question 4

A data scientist is fine-tuning a Cohere model on OCI Generative AI service for a custom classification task. They have a dataset of 1000 labeled examples. What is the minimum recommended dataset size for fine-tuning?

Accepted Answer

1000. Cohere models on OCI Generative AI require a minimum of 1000 labeled examples for fine-tuning to ensure sufficient signal for learning task-specific patterns without overfitting. This threshold is documented in OCI's fine-tuning requirements and applies to custom classification tasks.

Answer

500

Answer

5000

Answer

100

Question 5

Which TWO configuration steps are required to enable OCI Generative AI service in a tenancy? (Select two.)

Accepted Answer

Subscribe to the service in the tenancy.. Option D is correct because enabling OCI Generative AI in a tenancy requires subscribing to the service in the tenancy's region, which activates the service for that tenancy. Option E is correct because access to OCI Generative AI is governed by IAM policies; you must create a policy that grants the required permissions (for example, manage or use generative-ai-family) to the appropriate principals such as users, groups, or dynamic groups. Option A is not a required configuration step because service enablement is done through subscription rather than a separate console toggle. Option B is not required because a VCN with a service gateway is only needed for private network access to certain OCI services, not to enable Generative AI itself. Option C is not required because a dynamic group is only needed when granting access to OCI resources/instances via instance principals, not as a mandatory step to enable the service.

Answer

Enable the generative AI service in the console.

Answer

Set up a VCN with service gateway.

Answer

Create a dynamic group.

Question 6

Refer to the exhibit. The API Gateway fails to invoke the Generative AI service. What is the most likely missing configuration?

Accepted Answer

The API Gateway is not in the same VCN as the service's private endpoint.. The API Gateway must be in the same VCN as the Generative AI service's private endpoint to invoke the service. Private endpoints are accessible only within the same VCN or via VCN peering; if the gateway is in a different VCN, the request will fail due to network routing restrictions. This is a common misconfiguration when deploying OCI API Gateway with private endpoints for Generative AI.

Answer

The API Gateway does not have an internet gateway.

Answer

The JWT token is expired.

Answer

The Generative AI model is not deployed.

Question 7

A security team requires that all OCI GenAI API calls be logged and audited. Despite enabling Audit logs in OCI, they do not see GenAI API calls. What is the most likely reason?

Accepted Answer

OCI Audit currently only records control-plane operations; data-plane operations like inference are not logged.. C is correct because OCI Audit service is designed to log control-plane operations (e.g., creating, updating, or deleting resources) but does not log data-plane operations such as inference API calls to the Generative AI service. The GenAI inference calls (e.g., generating text) are data-plane operations that occur on the service endpoint, not on the OCI control-plane API, so they are not captured by Audit logs. To log data-plane operations, you would need to use a different mechanism, such as OCI Vault for key usage or custom logging via API Gateway.

Answer

The audit log retention policy is too short and logs were overwritten.

Answer

The user is not a tenancy administrator.

Answer

The API calls are made by an OCI function, which is not logged.

Question 8

A company wants to deploy a custom fine-tuned model for retrieval-augmented generation (RAG) using dedicated AI cluster. They need to ensure the model can handle concurrent requests from multiple applications with consistent latency. What should they configure?

Accepted Answer

Increase the number of replicas in the dedicated cluster.. Increasing the number of replicas in the dedicated cluster distributes the load across multiple model copies, improving concurrency and latency stability. This allows the model to handle concurrent requests from multiple applications with consistent latency.

Answer

Set a high temperature to keep responses concise.

Answer

Enable auto-scaling on the cluster.

Answer

Use the managed serving endpoint instead.

Question 9

An architect is designing a GenAI solution for document summarization that must meet GDPR compliance. The data should not leave the EU. OCI GenAI models are available in Frankfurt, London, and Paris. Which is the best approach?

Accepted Answer

Deploy a dedicated AI cluster in Frankfurt and upload data to Object Storage in Frankfurt.. Deploying a dedicated AI cluster in Frankfurt ensures that all data processing and storage remain within the EU, meeting GDPR compliance. By using Object Storage in the same region, data never leaves the EU boundary, and the dedicated cluster provides full control over data residency and processing, unlike managed endpoints that may route data through other regions.

Answer

Use the managed serving endpoint in Frankfurt.

Answer

Use the playground in any EU region.

Answer

Use a pre-trained model from OCI's catalog.

Question 10

Which TWO actions are required to enable a user to access OCI Generative AI service?

Accepted Answer

Subscribe to the GenAI service in the OCI Console.. Option C is correct because OCI Generative AI must be subscribed to (enabled) in the tenancy via the OCI Console before the service can be used, which activates it for that tenancy. Option E is correct because access to OCI Generative AI is governed by IAM policies; the user (or their group) needs a policy granting permissions such as manage or use on generative-ai-family resources in the relevant compartment. Option A is incorrect because a dedicated AI cluster is not a prerequisite for using the managed OCI Generative AI service. Option B is incorrect because service limits are quotas, not an enablement step, and do not by themselves grant access. Option D is incorrect because installing the OCI SDK is optional—access can be obtained through the Console, CLI, or API—and it does not enable the service.

Answer

Create a dedicated AI cluster.

Answer

Enable the GenAI service in the region's service limits.

Answer

Install the OCI SDK.

Question 11

Which THREE are valid ways to interact with OCI Generative AI?

Accepted Answer

OCI REST API.. Option C (OCI REST API) is correct because OCI Generative AI exposes HTTPS REST endpoints under the Generative AI service that you can call directly with signed requests to invoke models, manage endpoints, and create dedicated AI clusters. Option D (OCI CLI) is correct because the OCI command-line interface includes the `oci generative-ai` command group, letting you perform the same operations from a terminal with your API key credentials. Option E (OCI Console Playground) is correct because the OCI Console provides a built-in chat/playground interface for testing Generative AI models interactively without writing code. Option A (OCI Mobile App) is not a supported way to interact with OCI Generative AI, as the mobile app is for account and resource monitoring, not model invocation. Option B (OCI Data Science Notebooks) is not itself a Generative AI interaction method; notebooks are a compute environment where you could call the REST API or SDK, but they are not a distinct valid interface for the service.

Answer

OCI Mobile App.

Answer

OCI Data Science Notebooks.

Question 12

A healthcare company is deploying an OCI Generative AI service to summarize patient notes. They have recently moved from a managed serving endpoint to a dedicated AI cluster to ensure data privacy. The fine-tuned model is deployed on a dedicated cluster in the US West region. Users report that the summarization responses are now slower and occasionally timeout. The IT team checks the metrics: the cluster has 1 replica and CPU utilization is at 90%. The Object Storage bucket containing the model artifacts is in the same region. They have increased the timeout in their client configuration to 120 seconds, but still get timeouts. What should they do first to address the issue?

Accepted Answer

Increase the number of replicas in the dedicated cluster.. The dedicated AI cluster has only 1 replica and CPU utilization is at 90%, indicating that the single replica is overloaded and cannot handle the inference request volume. Increasing the number of replicas distributes the load, reduces latency, and prevents timeouts. This is the most direct and scalable fix for performance bottlenecks in a dedicated OCI Generative AI cluster.

Answer

Move the Object Storage bucket to a local NVMe cache in the cluster.

Answer

Move the model back to a managed serving endpoint in a different region.

Answer

Increase the max tokens parameter in the API call.

Question 13

A financial services company has deployed a custom fine-tuned model using OCI Generative AI service on a dedicated AI cluster for automated report generation. They use a Python application that sends prompts via the OCI SDK. Recently, they started seeing 429 Too Many Requests errors intermittently. The dedicated cluster has 2 replicas and the application is making about 100 requests per second. The cluster's documented throughput is 50 requests per second per replica. The company has not set up any throttling limits. What is the most likely cause of the 429 errors?

Accepted Answer

The application is exceeding the cluster's replica capacity.. The dedicated AI cluster has 2 replicas, each supporting 50 requests per second, for a total capacity of 100 requests per second. The application is sending exactly 100 requests per second, which matches the cluster's maximum throughput. However, without any throttling limits configured, the service will still enforce its built-in rate limits at the replica level, and any transient burst or uneven distribution across replicas can trigger 429 Too Many Requests errors. The most likely cause is that the application is operating at the cluster's replica capacity boundary, leading to intermittent throttling.

Answer

The OCI SDK version is outdated.

Answer

The API calls are not authenticated.

Answer

The model's context window is too small for the prompts.

Question 14

A developer wants to generate text using the OCI Generative AI service via the API. Which endpoint should they use to send a text generation request?

Accepted Answer

/v1/completions. The OCI Generative AI service uses the /v1/completions endpoint for text generation requests, as documented in the OCI Generative AI API reference. This endpoint accepts a prompt and generates a continuation of the text, making it the appropriate choice for general text generation tasks.

Answer

/v1/chat/completions

Answer

/v1/embeddings

Answer

/v1/models

Question 15

Which TWO are benefits of using OCI Generative AI service's dedicated AI cluster?

Accepted Answer

Ability to fine-tune models on custom data.. Option C is correct because a dedicated AI cluster in OCI Generative AI provides the compute resources needed to host and fine-tune foundation models on your own custom datasets, which is a key advantage over the shared serverless inference endpoint. Option E is correct because a dedicated AI cluster gives you exclusive, provisioned GPU capacity rather than sharing a multi-tenant serverless endpoint, which reduces contention and yields lower, more predictable latency. Options A, B, and D are not benefits of the dedicated AI cluster: automatic scaling is characteristic of the serverless on-demand mode rather than a fixed dedicated cluster, built-in content filtering is a platform-level guardrail feature not tied to the dedicated cluster, and training data is still required for fine-tuning, so no option removes the need to supply it.

Answer

Automatic scaling to handle large workloads.

Answer

Built-in content filtering for all outputs.

Answer

No need to provide any training data.