1Z0-1127-25 Using OCI Generative AI Service — Timed 30 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?

30 minute time limit — choose an answer to begin.

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.

Answer

Fine-tune the model with curated safe examples

Answer

Reduce the temperature parameter to 0

Answer

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. 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.

Answer

/generate

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/classify

Answer

/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. 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

Answer

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

Answer

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

Question 4

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

Answer

Temperature = 0, Top-p = 0

Answer

Temperature = 1, Top-p = 1

Question 5

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?

30 minute time limit — choose an answer to begin.

Accepted Answer

Enable data redaction in the service. 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

Answer

Use the moderation API to scan outputs

Answer

Use a dedicated model endpoint

Question 6

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

Accepted Answer

Use a dedicated AI cluster. Option A is correct because OCI Generative AI dedicated AI clusters provide reserved, isolated compute capacity for hosting models, so inference requests avoid contention on shared, multi-tenant endpoints and therefore experience lower, more predictable latency. Option B is correct because the max tokens parameter caps the number of tokens generated in the response; fewer generated tokens means less autoregressive decoding work, which directly shortens response time and thus reduces latency. Option C is not a valid general answer because moving to another region does not inherently lower latency and may even increase it depending on network distance to the client. Option D is wrong because larger models generally have more parameters and higher inference cost, which tends to increase rather than reduce latency. Option E is wrong because batching multiple requests increases per-request queueing and processing time, which raises latency rather than reducing it.

Answer

Deploy the model in a different region

Answer

Use a larger model

Answer

Batch multiple requests

Question 7

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

Accepted Answer

Text summarization. OCI Generative AI Service supports text summarization (A), which condenses long documents into concise summaries using its hosted large language models. It also supports question answering (D), allowing users to query a model or a dedicated endpoint backed by a knowledge base to retrieve answers from provided content. Code generation (E) is likewise supported, as the service's models can produce, complete, and explain code in multiple programming languages. Sentiment analysis (B) is not a core generative capability of the service; it is typically handled by OCI Language, a separate AI service. Image generation (C) is not offered by OCI Generative AI Service, which focuses on text and code generation rather than image synthesis.

Answer

Sentiment analysis

Answer

Image generation

Question 8

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. Option A is correct because fine-tuning in OCI Generative AI Service requires a training dataset uploaded to Object Storage in the JSONL format expected by the service, with each line containing prompt/completion pairs (or chat-style messages) that the tuning job consumes. Option B is correct because the fine-tuning job must reference the base model identifier (for example, a specific pretrained model such as cohere.command or meta.llama-3 variants) so the service knows which foundation model to adapt with the custom data. Option C is not required because quota and compartment are handled by IAM policies and service limits, not as a distinct fine-tuning input parameter. Option D is not required because authentication is handled through OCI IAM credentials or instance principals, not a separate API key specific to fine-tuning. Option E is not required because OCI Generative AI fine-tuning is a managed service and does not require the customer to provision a dedicated AI cluster.

Answer

A compartment with sufficient quota

Answer

An OCI API key

Answer

A dedicated AI cluster

Question 9

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

Answer

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

Question 10

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.. 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 11

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.. 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.

Question 12

A company is deploying a multi-language chatbot using OCI Generative AI Service. The chatbot must support English, Spanish, and French. The team finds that responses in Spanish are less accurate than in English. They have a small bilingual dataset. What is the best approach?

Accepted Answer

Use a multilingual base model (e.g., mT5) and fine-tune on the bilingual dataset (English and Spanish) using cross-lingual transfer learning.. Fine-tuning a multilingual base model like mT5 on a small bilingual dataset leverages cross-lingual transfer learning, where knowledge from high-resource languages (English) improves performance on low-resource languages (Spanish). This approach is specifically designed for scenarios with limited data and directly addresses the accuracy gap without requiring separate models or translation pipelines.

Answer

Use prompt engineering with language-specific instructions in the system prompt.

Answer

Translate all user queries to English, process them, then translate responses back.

Answer

Train separate fine-tuned models for each language.

Question 13

A retail company uses OCI Generative AI Agents to power a product recommendation chatbot on their e-commerce website. The chatbot is integrated with a knowledge base containing product descriptions, customer reviews, and inventory data. Recently, the chatbot has started recommending out-of-stock products frequently, leading to customer frustration. The development team verified that the knowledge base is updated in real-time with inventory data. The chatbot's configuration uses a chunking strategy with a chunk size of 500 tokens and an overlap of 50 tokens. The team suspects the issue is related to how the agent retrieves information. They have access to OCI Logging and Monitoring. Which course of action should the team take first?

Accepted Answer

Increase the chunk overlap from 50 to 150 tokens to ensure inventory status is captured in multiple chunks.. The core issue is that the chatbot retrieves chunks that contain product descriptions but may miss the inventory status because the chunking strategy does not reliably include both pieces of information together. Increasing the chunk overlap from 50 to 150 tokens ensures that inventory data, which may be at the boundary of a chunk, is captured in multiple overlapping chunks, thereby increasing the likelihood that the retrieval step returns a chunk containing both the product and its current stock level. This directly addresses the retrieval gap without altering model behavior or infrastructure.

Answer

Decrease the chunk size to 250 tokens to make chunks more specific.

Answer

Reduce the temperature parameter of the model to 0.2 to reduce hallucinations.

Answer

Enable auto-scaling on the AI cluster to improve response speed.

Question 14

You are a cloud architect at a healthcare company that uses OCI Generative AI Service to analyze patient records and generate clinical summaries. The service is deployed in the Frankfurt region with a dedicated AI cluster. Recently, the compliance team flagged that some generated summaries contain hallucinated diagnoses not present in the source records. They demand immediate mitigation. The current setup uses the default model (cohere.command-r-08-2024) with temperature=0.7, top_p=0.9, and max_tokens=2048. The application sends the entire patient record as a single prompt. You have access to OCI Logging, monitoring metrics (latency, request count, token count, safety filter rejections), and the AI service's model fine-tuning capability. You must reduce hallucinations while minimizing latency increase. What is the most effective course of action?

Accepted Answer

Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.. Reducing temperature and top_p makes the model more deterministic, directly reducing the likelihood of hallucinated content. Fine-tuning on a curated dataset of 5,000 clinical summaries teaches the model domain-specific patterns and constraints, further minimizing hallucinations. This approach addresses the root cause without significantly increasing latency, as fine-tuning does not affect inference speed and lower sampling parameters add no computational overhead.

Answer

Switch to cohere.command-light model for faster inference and add a post-processing step using a BERT-based NER model to validate entities.

Answer

Increase max_tokens to 4096 and use chunked processing with overlapping context windows to provide more context.

Answer

Enable the safety filter with strict content moderation and set up OCI Logging to audit all generations.

Question 15

A user has attached an IAM policy granting access to the generative-ai-family resource type, but API calls to the Generative AI service return a 403 Forbidden error. What is the most likely cause?

Accepted Answer

The policy does not specify the correct compartment or resource.. IAM policies in OCI are evaluated based on the resource type, the verb, and the compartment. Even if a policy grants access to the 'generative-ai-family' resource type, if the policy does not specify the correct compartment (or uses a condition that does not match the resource's compartment), the API call will be denied with a 403 Forbidden. The user must ensure the policy's 'allow' statement includes the appropriate compartment or uses a dynamic group or condition that matches the resource's location.

Answer

The service is not enabled in the tenancy.

Answer

The policy does not include the 'use' verb for the resource type.

Answer

The user has not created a Dedicated AI Cluster.

Question 16

A company is building a customer support chatbot that uses Retrieval-Augmented Generation (RAG) with OCI Generative AI. They need low-latency responses and the ability to update the knowledge base daily. Which architecture best meets these requirements?

Accepted Answer

Use OCI Search with OpenSearch for the vector database, OCI Generative AI for inference, and Oracle Database for metadata.. It combines OCI Search with OpenSearch as a vector database for efficient similarity search, OCI Generative AI for inference, and Oracle Database for metadata management. This architecture provides low-latency responses by leveraging OpenSearch's optimized vector indexing and allows daily knowledge base updates through Oracle Database's robust data management capabilities.

Answer

Store embeddings in OCI Object Storage and use OCI Functions to perform similarity search.

Answer

Use OCI Data Science Notebook Sessions to run the RAG pipeline with a managed Cohere model.

Answer

Use OCI Streaming to ingest documents and OCI Data Flow to update a knowledge base in OCI Object Storage.

Question 17

Which model family is NOT currently available in OCI Generative AI service?

Accepted Answer

OpenAI GPT-4. OpenAI GPT-4 is not available in OCI Generative AI service because OCI's native generative AI offerings are built on open-source and partner models like Meta Llama, Anthropic Claude, and Cohere, but not on OpenAI's proprietary models. OCI Generative AI service provides access to models hosted on OCI, and OpenAI GPT-4 is only accessible via Azure OpenAI Service or direct OpenAI API, not through OCI's managed service.

Answer

Meta Llama

Answer

Anthropic Claude

Answer

Cohere

Question 18

A multinational corporation plans to deploy OCI Generative AI in multiple OCI regions for disaster recovery. They have fine-tuned a custom model in the primary region. What is the recommended approach to make the fine-tuned model available in the secondary region with minimal manual effort?

Accepted Answer

Copy the model artifact to the secondary region's object storage bucket and create a new dedicated endpoint there.. The recommended approach to make a fine-tuned custom model available in a secondary OCI region with minimal manual effort is to copy the model artifact (the trained weights and configuration files) to the secondary region's object storage bucket and then create a new dedicated endpoint there. This avoids re-running the expensive fine-tuning job and leverages OCI's object storage cross-region copy capabilities, while the dedicated AI cluster in the secondary region can serve the model directly from the copied artifact.

Answer

Create an IAM policy to allow cross-region access to the model from the secondary region.

Answer

Use OCI Cross-Region Replication for the model's underlying object storage bucket and the dedicated AI cluster.

Answer

Redeploy the fine-tuning job in the secondary region using the same training data.

Question 19

When using OCI Generative AI with a fine-tuned model, what is the primary benefit of creating a dedicated AI cluster?

Accepted Answer

Consistent low latency and high throughput for production workloads.. A dedicated AI cluster in OCI Generative AI provides reserved compute capacity, ensuring consistent low latency and high throughput for production workloads. Unlike on-demand or auto-scaling setups, a dedicated cluster avoids resource contention with other tenants, making it ideal for latency-sensitive inference tasks with fine-tuned models.

Answer

Automatic scaling based on demand.

Answer

Reduced cost per inference token compared to on-demand.

Answer

Enhanced security through network isolation from other tenants.

Question 20

A financial services firm needs to extract named entities from legal contracts using OCI Generative AI. They require high accuracy and must handle domain-specific terminology. Which approach is most effective?

Accepted Answer

Fine-tune a Cohere Command model using a dataset of annotated legal contracts.. Fine-tuning a Cohere Command model on a domain-specific dataset of annotated legal contracts allows the model to learn the unique terminology, document structure, and entity boundaries required for high-accuracy extraction. This supervised approach adapts the model's weights to the target task, significantly outperforming zero-shot or prompt-based methods for specialized domains like legal contracts.

Answer

Use the base Cohere Command model with zero-shot prompting for entity extraction.

Answer

Use the Cohere Chat model with system prompts describing the entities.

Answer

Use the Cohere Embed model to generate embeddings and then train a separate classifier.