1Z0-1127-25 Using OCI Generative AI Service Practice Test 8 — 15 Questions

Question 1

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. The size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

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Required response time

Answer

Internet connectivity

Question 2

Refer to the exhibit. A developer runs the command and receives the error. What is the issue?

Accepted Answer

The max-tokens value exceeds the allowed range.. The max-tokens parameter is set to 600, which exceeds the allowed range of 1 to 500.

Answer

The message is too short.

Answer

The chat-id is invalid.

Answer

The endpoint is incorrect.

Question 3

A data scientist wants to quickly test a prompt with different parameters like temperature and max tokens without writing code. Which OCI GenAI feature should they use?

Accepted Answer

OCI Generative AI Playground.. The OCI Generative AI Playground is a web-based, no-code interface that allows data scientists to interactively test prompts and adjust parameters like temperature and max tokens without writing any code. This directly matches the user's requirement for quick, code-free experimentation.

Answer

OCI CLI.

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OCI SDK.

Answer

OCI Data Science Notebooks.

Question 4

A company is fine-tuning a Llama model on OCI with dedicated AI cluster. They want to use their own training data stored in Oracle Object Storage. What must they do to ensure the fine-tuning job can access the data?

Accepted Answer

Configure a resource principal for the cluster to read the bucket.. OCI Generative AI dedicated AI clusters require a resource principal (an IAM principal) to authenticate and authorize access to data stored in Oracle Object Storage. By configuring the cluster with a resource principal that has read permissions on the bucket, the fine-tuning job can securely read the training data without exposing credentials or using public URLs.

Answer

Upload the data to the dedicated cluster's local storage.

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Use OCI Data Flow to transfer data.

Answer

Create a pre-authenticated request for the bucket.

Question 5

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.

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The user is not a tenancy administrator.

Answer

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

Question 6

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 7

A developer wants to integrate OCI GenAI into a Java application. Which SDK should they use?

Accepted Answer

OCI Java SDK.. The OCI Java SDK provides native Java support for calling OCI services, including OCI Generative AI, making it the appropriate choice for a Java application. Options A and B are SDKs for other languages, and option D is a CLI tool, not an SDK.

Answer

OCI JavaScript SDK.

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OCI Python SDK.

Answer

OCI CLI.

Question 8

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

Accepted Answer

OCI REST API.. OCI Generative AI exposes a comprehensive REST API that allows developers to programmatically interact with the service for tasks such as text generation, summarization, and chat. The REST API uses standard HTTP methods and JSON payloads, making it accessible from any language or tool that can send HTTP requests.

Answer

OCI Mobile App.

Answer

OCI Data Science Notebooks.

Question 9

A company is using dedicated AI cluster for fine-tuning. Which TWO best practices help optimize cost?

Accepted Answer

Manually scale down the cluster when not in use.. Manually scaling down the dedicated AI cluster when not in use directly reduces compute costs by stopping idle GPU/CPU resources. In OCI Generative AI, dedicated AI clusters incur charges for provisioned capacity, so scaling down during inactivity avoids paying for unused infrastructure.

Answer

Use the largest replica count.

Answer

Use the managed serving endpoint instead.

Answer

Leave the cluster running continuously.

Question 10

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 11

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.

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The API calls are not authenticated.

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

Question 12

A startup wants to quickly prototype a chatbot using OCI Generative AI service. They have no prior experience with OCI. They want to test different models and parameters without writing any code and within a few minutes. They also want to save prompts and compare results. Which approach should they use?

Accepted Answer

Use the OCI Generative AI Playground.. The OCI Generative AI Playground is a no-code, web-based interface that allows users to quickly test different models and parameters, save prompts, and compare results without writing any code or having prior OCI experience. It directly meets the startup's requirement for rapid prototyping within minutes.

Answer

Create a dedicated AI cluster and use the OCI SDK.

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Use OCI Data Science Notebooks with the GenAI SDK.

Answer

Use OCI Functions to invoke the GenAI API.

Question 13

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

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/v1/embeddings

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/v1/models

Question 14

A company deployed OCI Generative AI for a customer service chatbot. They are using the Cohere command model. The chatbot is generating responses that are too brief and often cut off mid-sentence. They have limited budget. What should they do?

Accepted Answer

Increase max tokens to 1024.. The chatbot's responses are too brief and cut off mid-sentence, which indicates the model is hitting the maximum token limit for generation. Increasing the max tokens to 1024 allows the Cohere command model to produce longer, complete responses without truncation. This is the most direct and cost-effective fix for the described symptom.

Answer

Decrease the temperature to 0.2.

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Increase the temperature to 0.9.

Answer

Use a different base model like Llama.

Question 15

An e-commerce company uses OCI Generative AI to generate product descriptions. They have fine-tuned the model on their product catalog. They notice that the descriptions are accurate but lack creativity and are repetitive. They want to maintain accuracy while adding variety. What change should they make?

Accepted Answer

Increase the temperature from 0.2 to 0.5.. Slightly increasing temperature (e.g., from 0.2 to 0.5) introduces controlled variability without significantly compromising accuracy. Option A is wrong because top_p=1.0 samples from the full distribution, which can add noise. Option C is wrong because adding more training data requires effort and time, and may not immediately add variety. Option D is wrong because changing the base model could hurt accuracy and requires retraining.

Answer

Increase the top_p sampling from 0.9 to 1.0.

Answer

Use a different base model.

Answer

Add more training examples with diverse descriptions.