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

Question 1

A company uses OCI Generative AI Service to build a chatbot for customer support. They notice that the model sometimes generates inappropriate responses. What is the MOST effective way to mitigate this without retraining the model?

Accepted Answer

Configure system instructions to define acceptable behavior. Configuring system instructions is the most effective approach because it allows you to define the model's behavior and constraints at inference time without modifying the underlying model weights. In OCI Generative AI Service, system instructions act as a persistent prompt that guides the model's responses, enabling you to explicitly prohibit inappropriate content and enforce safety guidelines. This is a non-invasive, immediate mitigation that does not require the time, cost, or data preparation associated with retraining or fine-tuning.

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Fine-tune the model with curated safe examples

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Reduce the temperature parameter to 0

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Use the moderation API to filter responses

Question 2

A developer wants to use OCI Generative AI Service to summarize long documents. Which endpoint should they use to send the document content?

Accepted Answer

/chat. Option D is correct because the /chat endpoint in OCI Generative AI Service is designed for conversational interactions and can handle long document summarization by accepting the document content as part of the chat context. This endpoint supports multi-turn dialogues and large input payloads, making it suitable for processing and summarizing lengthy documents.

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/generate

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/classify

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/embed

Question 3

An organization deploys a fine-tuned model for legal document analysis using OCI Generative AI Service. They need to ensure that only authorized users in the 'LegalTeam' group can access the model endpoint. Which policy statement should be used?

Accepted Answer

Allow group LegalTeam to use generative-ai-model in compartment ABC. Option A is correct because the 'use' verb on the 'generative-ai-model' resource type grants the LegalTeam group permission to invoke the model endpoint for inference, which is the minimum privilege required for accessing a deployed fine-tuned model in OCI Generative AI Service. The 'use' permission specifically allows calling the model for text generation or analysis without granting broader management or read capabilities.

Answer

Allow group LegalTeam to manage generative-ai-family in compartment ABC

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Allow group LegalTeam to read generative-ai-model in compartment ABC

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Allow group LegalTeam to inspect generative-ai-model in compartment ABC

Question 4

A data scientist is using OCI Generative AI Service to generate product descriptions. They notice that the output often repeats phrases. Which parameter adjustment would MOST directly address this issue?

Accepted Answer

Increase the frequency penalty. Option C is correct because the frequency penalty directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. In OCI Generative AI Service, this parameter subtracts a fixed value from the log-probability of each token each time it is generated, making repeated tokens less likely to be chosen again. This is the most direct mechanism to address repetitive output.

Answer

Increase the temperature

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Increase the max tokens

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Decrease the top-p value

Question 5

A company needs to integrate OCI Generative AI Service with an existing application that uses OCI IAM for authentication. They want to use resource principal to allow the application to call the service without storing API keys. Which step is REQUIRED?

Accepted Answer

Create a dynamic group and a policy granting access to the Generative AI Service. Resource principal authentication in OCI requires the application to be represented by a dynamic group, which matches instances or resources based on defined rules. A policy must then grant that dynamic group access to the Generative AI Service. This avoids storing API keys by using OCI IAM's built-in resource principal token exchange.

Answer

Create an OCI API key for the application

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Enable the Generative AI Service for resource principal in the tenancy

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Assign the application to a group with admin privileges

Question 6

A developer is using OCI Generative AI Service to generate code snippets. They want to ensure the output is as deterministic as possible for testing. Which combination of parameters should they use?

Accepted Answer

Temperature = 0, Top-p = 1. Setting Temperature=0 makes the model deterministic by always selecting the highest-probability token, while Top-p=1 includes all tokens in the sampling pool, ensuring no additional randomness is introduced. This combination eliminates stochastic variation, making outputs repeatable for testing.

Answer

Temperature = 0.5, Top-p = 0.5

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Temperature = 0, Top-p = 0

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Temperature = 1, Top-p = 1

Question 7

A company uses OCI Generative AI Service to generate personalized email content. They need to ensure that personally identifiable information (PII) is not included in the model's training data. What should they do?

Accepted Answer

Enable data redaction in the service. Option D is correct because OCI Generative AI Service provides a built-in data redaction feature that automatically detects and removes personally identifiable information (PII) from training data before it is used for model training. This ensures compliance with data privacy regulations without requiring manual preprocessing or external tools.

Answer

Encrypt the training data with OCI Vault

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Use the moderation API to scan outputs

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Use a dedicated model endpoint

Question 8

Which TWO of the following are valid ways to reduce latency when using OCI Generative AI Service?

Accepted Answer

Use a dedicated AI cluster. A dedicated AI cluster provides isolated compute resources (GPU nodes) for inference, eliminating resource contention from other tenants or workloads. This ensures consistent low-latency responses because the model is always warm and available without queueing delays, which is critical for real-time applications.

Answer

Deploy the model in a different region

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Use a larger model

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Batch multiple requests

Question 9

Which THREE of the following are supported capabilities of OCI Generative AI Service?

Accepted Answer

Text summarization. Option A is correct because OCI Generative AI Service includes a dedicated text summarization capability that uses large language models (LLMs) to generate concise summaries from longer documents. This feature is part of the service's core generative AI offerings, supporting use cases like meeting notes summarization and document abstraction.

Answer

Sentiment analysis

Answer

Image generation

Question 10

Which TWO of the following are required to fine-tune a model using OCI Generative AI Service?

Accepted Answer

A training dataset in the required format. A is correct because fine-tuning a model in OCI Generative AI Service requires a training dataset in the required format (JSONL with prompt-completion pairs) to provide the task-specific examples that adjust the model's weights. B is correct because you must specify the base model identifier (e.g., 'cohere.command-light-14-07-2024') to indicate which pre-trained model to fine-tune, as the service uses this to load the correct architecture and initial parameters.

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A compartment with sufficient quota

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An OCI API key

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A dedicated AI cluster

Question 11

Refer to the exhibit. A user runs the command shown and receives the error: 'ServiceError: NotAuthorizedOrNotFound'. What is the MOST likely cause?

Accepted Answer

The user does not have the 'inspect' permission on the model. The error 'NotAuthorizedOrNotFound' typically indicates either the model ID does not exist or the user lacks permission to view it. Option D is correct because the error message is generic to avoid information leakage. Option A would give a different error (e.g., invalid model ID), but the generic error suggests authorization or existence issues.

Answer

The CLI is not configured with OCI credentials

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The model ID is incorrectly formatted

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The model is in a different region than iad

Question 12

Refer to the exhibit. A developer sends this JSON payload to the /chat endpoint. The response includes an error that 'maxTokens' must be an integer. What is the issue?

Accepted Answer

The parameter should be 'max_tokens' instead of 'maxTokens'. The OCI Generative AI service expects the parameter name 'max_tokens' (snake_case) for specifying the maximum number of tokens in the response, not 'maxTokens' (camelCase). The error message indicates that the value is not being recognized as an integer because the JSON key itself is incorrect, causing the service to fail validation.

Answer

The compartmentId is missing

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The temperature value is too low

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The model name 'cohere.command-light' is incorrect

Question 13

A startup is building a chatbot for customer support using OCI Generative AI Service. The chatbot needs to answer queries about product features based on a knowledge base of product documentation. Which configuration is most appropriate for this use case?

Accepted Answer

Use a Cohere Command model with the knowledge base as context in a prompt, and enable retrieval-augmented generation (RAG) via OCI Generative AI Agents.. Option B is correct because OCI Generative AI Agents with retrieval-augmented generation (RAG) allows the chatbot to dynamically retrieve relevant chunks from the product documentation knowledge base and inject them as context into a Cohere Command model prompt. This approach ensures answers are grounded in the latest documentation without requiring fine-tuning, and it scales efficiently as the knowledge base grows.

Answer

Use the Summarization task type to generate concise answers from the documentation.

Answer

Fine-tune a Llama 2 70B model on the product documentation to create a custom model.

Answer

Use the Code Generation model to produce SQL queries that retrieve answers from a database.

Question 14

An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?

Accepted Answer

Retrain the model on the latest financial data and monitor for drift.. Option D is correct because retraining with up-to-date data addresses the root cause of data drift. Option A is wrong because adjusting temperature may reduce creativity but not fix factual accuracy. Option B is wrong because increasing max tokens does not improve accuracy. Option C is wrong because switching to a different base model without retraining does not address drift.

Answer

Switch to a newer base model like Llama 3.1 without retraining.

Answer

Decrease the temperature parameter to 0.1 to reduce model creativity.

Answer

Increase the max tokens value to allow longer responses.

Question 15

A healthcare company is deploying OCI Generative AI Service for clinical decision support. They must ensure that model outputs are auditable, explainable, and free from patient data exposure. Which combination of OCI features should they use?

Accepted Answer

Use OCI Data Masking to de-identify inputs, and enable model monitoring with explainability outputs via OCI Monitoring and OCI Logging.. Option C is correct because OCI Data Masking can de-identify patient data in inputs before they reach the generative AI model, ensuring no protected health information (PHI) is exposed. Enabling model monitoring with explainability outputs via OCI Monitoring and OCI Logging provides an auditable trail of model decisions and explanations, meeting the requirements for auditability and explainability in clinical decision support.

Answer

Fine-tune a model on de-identified patient notes and use default inference settings.

Answer

Use Retrieval-Augmented Generation with an internet search index for up-to-date medical knowledge.

Answer

Deploy the model in a private endpoint and disable all logging to prevent data leaks.