1Z0-1127 Practice Test 31 — 15 Questions

Question 1

An organization wants to deploy a generative AI chatbot using OCI Generative AI service. The chatbot must comply with data residency requirements by ensuring that all data processing occurs within a specific geographic region. What is the best practice to achieve this?

Accepted Answer

Use a dedicated AI cluster in the required region. Option A is correct because OCI Generative AI service allows you to provision a dedicated AI cluster within a specific region, ensuring all model inference and data processing remain within that geographic boundary. This dedicated cluster is isolated from other regions and complies with data residency requirements by design, as no data leaves the chosen region during processing.

Answer

Enable cross-region replication for disaster recovery

Answer

Configure a tenancy-wide policy to restrict region usage

Answer

Use IAM policies to block access from other regions

Question 2

A company has deployed a generative AI model on OCI to generate product descriptions. After a recent update, the model started producing outputs with repetitive phrases and poor coherence. The inference endpoint is configured with default parameters. Which single parameter adjustment is most likely to improve output quality?

Accepted Answer

Increase the frequency penalty parameter to 0.5. The correct answer is B because increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.

Answer

Increase the max-tokens parameter to 512

Answer

Increase the temperature parameter to 1.5

Answer

Decrease the top-p parameter to 0.8

Question 3

A company is using OCI Generative AI service with a dedicated AI cluster for text generation. They notice that the latency is higher than expected. The cluster is in the Ashburn region, and users are distributed globally. What is the most effective way to reduce latency?

Accepted Answer

Deploy dedicated AI clusters in regions closer to the users. Latency for globally distributed users is primarily driven by network distance and the speed of light. Deploying dedicated AI clusters in regions closer to the users reduces the physical distance data must travel, directly minimizing network round-trip time (RTT). This is the most effective architectural change because OCI's Generative AI service processes each request on the dedicated cluster and cannot bypass geographic latency through software optimizations alone.

Answer

Enable the OCI Generative AI inference optimizer

Answer

Increase the number of nodes in the dedicated AI cluster

Answer

Use a content delivery network (CDN) to cache responses

Question 4

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 5

Which TWO actions are recommended best practices for managing costs when using OCI Generative AI dedicated AI clusters?

Accepted Answer

Use preemptible instances for non-critical inference workloads. Option B is correct because preemptible instances in OCI are significantly cheaper than standard instances and are ideal for non-critical inference workloads that can tolerate interruptions. This aligns with cost optimization best practices by allowing you to use spare compute capacity at a reduced rate for tasks that do not require continuous availability.

Answer

Provision a fixed number of nodes to handle peak load

Answer

Stop the dedicated AI cluster when not in use

Answer

Use pay-as-you-go billing instead of preemptible instances

Question 6

A data scientist is deploying a custom generative AI model using OCI Data Science. After deploying the model to an endpoint, they notice that inference requests are failing with a timeout error when the payload size exceeds 1 MB. What is the most likely cause and solution?

Accepted Answer

The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.. The correct answer is D because OCI Data Science model deployments have a default payload size limit of approximately 1 MB. When inference requests exceed this limit, the load balancer or gateway times out the request. The solution is to increase the payload limit in the deployment configuration, which can be adjusted via the OCI console or API by modifying the `maximumRequestPayloadSize` setting.

Answer

The load balancer is misconfigured; reconfigure the load balancer timeout settings.

Answer

The model server lacks sufficient memory; scale out to more instances.

Answer

The model is not optimized for large payloads; use AutoML to optimize the model.

Question 7

Which THREE components are required to deploy a custom generative AI model on OCI Data Science model deployment?

Accepted Answer

An inference script (e.g., score.py) to handle prediction requests. Option B is correct because OCI Data Science model deployment requires an inference script (typically score.py) to define how the model processes incoming prediction requests. This script is the entry point that loads the model artifact and executes inference logic, making it an essential component for serving predictions.

Answer

A load balancer to distribute traffic

Answer

An API signing key for authentication

Question 8

A machine learning engineer is deploying a fine-tuned Llama 2 model on OCI Data Science model deployment. The deployment fails with an error: 'Model artifact exceeds the maximum allowed size of 10 GB.' The model files total 12 GB. What is the best approach to resolve this?

Accepted Answer

Store the model in Object Storage and reference it in the deployment configuration. Option A is correct because OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.

Answer

Use a different model that is smaller than 10 GB

Answer

Increase the model deployment artifact size limit via a service request

Answer

Compress the model artifact to under 10 GB using gzip

Question 9

A developer wants to call the OCI Generative AI service from a Python application running on an OCI Compute instance. Which method is the most secure for authenticating the API calls?

Accepted Answer

Use instance principals with a dynamic group and policy. Option C is correct because instance principals allow the Compute instance to authenticate to OCI services without storing any credentials on the instance. By assigning a dynamic group and policy, the instance obtains a temporary security token from the OCI metadata service, which is the most secure method for programmatic access from within OCI.

Answer

Use a resource principal

Answer

Use the OCI CLI with a config file containing credentials

Answer

Use an API signing key stored on the instance

Question 10

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 11

Which two actions are required when deploying a custom fine-tuned model using the OCI Generative AI service? (Choose two.)

Accepted Answer

Upload model artifacts to OCI Object Storage. Option D is correct because deploying a custom fine-tuned model in OCI Generative AI requires the model artifacts (e.g., weights, configuration files) to be stored in OCI Object Storage. The service pulls the artifacts from a designated bucket during deployment. Option E is correct because a dedicated AI cluster must be created to host the model, providing the necessary compute resources for inference.

Answer

Configure an API Gateway for the model endpoint

Answer

Register the model in the OCI Data Science Model Catalog

Answer

Set up a load balancer for the deployment

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

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 via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.

Answer

Fine-tune the model using OCI Generative AI service

Answer

Register the model in OCI Generative AI service

Question 15

A company wants to use OCI Generative AI to summarize customer support tickets. They need to ensure that the model does not output any sensitive information. Which technique should they implement?

Accepted Answer

Prompt engineering to instruct the model to exclude sensitive information.. Prompt engineering is the correct technique because it allows the company to explicitly instruct the generative AI model to exclude sensitive information from its outputs. By crafting a system prompt or user prompt with specific directives (e.g., 'Do not include any personally identifiable information, account numbers, or confidential data in your summary'), the model's behavior is directly controlled at inference time. This is a lightweight, flexible approach that does not require changing the model architecture or endpoint configuration, and it is the most direct way to enforce output constraints in OCI Generative AI.

Answer

Use a smaller model that is less likely to memorize data.

Answer

Enable content filtering on the endpoint.

Answer

Disable the use of training data in the endpoint configuration.