1Z0-1127-25 Using OCI Generative AI Service Practice Test 2 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 2

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 3

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 4

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 5

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.

Question 6

Which TWO deployment options are available for using fine-tuned models with OCI Generative AI service?

Accepted Answer

On-Demand Endpoint. In OCI Generative AI, fine-tuned models can be hosted through a Managed Dedicated Endpoint (E), which provisions dedicated AI cluster capacity for the custom model and provides a private, scalable inference endpoint with an OCID. They can also be served via an On-Demand Endpoint (C), where the fine-tuned model is accessed on a pay-as-you-go basis without provisioning dedicated hosting infrastructure. Options A (BYOC), B (Serverless Endpoint), and D (Edge Deployment) are not deployment options offered for fine-tuned models in OCI Generative AI, so they do not apply to this scenario.

Answer

Bring Your Own Container (BYOC)

Answer

Serverless Endpoint

Answer

Edge Deployment

Question 7

Which THREE steps are necessary to secure access to the OCI Generative AI inference API in a production environment?

Accepted Answer

Configure network security groups to allow only trusted source IPs to the inference endpoint.. Option B is correct because restricting inbound traffic to the inference endpoint with network security groups (NSGs) that allow only trusted source IPs enforces network-level least privilege and reduces exposure of the OCI Generative AI inference API. Option C is correct because OCI IAM policies must explicitly grant the 'use' verb on generative-ai-family resources (e.g., allow group X to use generative-ai-family in compartment Y) so that only authorized principals can invoke the inference API. Option D is correct because using private endpoints lets clients reach the Generative AI service from within a VCN over the Oracle backbone rather than the public internet, which is a standard production hardening step. Option A is not required here because OCI already encrypts data at rest and in transit by default, and OCI Vault key management is not a mandatory step to secure inference API access. Option E is not required because data masking is a data-governance control for sensitive content in prompts, not an access-control mechanism for the inference API.

Answer

Enable encryption with OCI Vault keys for all inference data.

Answer

Apply data masking policies to obfuscate sensitive information in prompts.

Question 8

Which TWO operations are supported by the OCI Generative AI inference API?

Accepted Answer

EmbedText. The OCI Generative AI inference API supports EmbedText (A), which generates vector embeddings from input text for use in semantic search, RAG, and similarity tasks, and GenerateText (D), which produces text completions from a prompt using the hosted foundation models. These two operations map directly to the API's documented inference endpoints for embeddings and text generation. SummarizeText (B), TranslateText (C), and ChatCompletion (E) are not standalone operations exposed by the OCI Generative AI inference API; summarization or translation would instead be achieved by prompting GenerateText, and chat-style behavior is handled through the same text-generation endpoint rather than a separate ChatCompletion operation.

Answer

SummarizeText

Answer

TranslateText

Answer

ChatCompletion

Question 9

A company wants to use OCI Generative AI to summarize customer feedback. They need low latency and high throughput. Which configuration should they choose?

Accepted Answer

Dedicated AI cluster with base model. Dedicated AI clusters provide guaranteed compute resources (GPUs) with no multi-tenant contention, ensuring low latency and high throughput for inference workloads. Using a base model avoids the additional overhead of fine-tuning inference, which can introduce latency due to custom weight loading and optimization steps. This combination is optimal for real-time summarization of customer feedback where response time and volume are critical.

Answer

Serverless endpoint with fine-tuned model

Answer

Dedicated AI cluster with fine-tuned model

Answer

Serverless endpoint with base model

Question 10

A healthcare company must use OCI Generative AI for medical report generation. They need to ensure PHI is not sent to third-party models. Which approach best ensures data stays within OCI?

Accepted Answer

Use OCI Gen AI service with fine-tuned model on a dedicated AI cluster. Using OCI Gen AI service with a fine-tuned model on a dedicated AI cluster ensures that all data (including PHI) remains within OCI's infrastructure, as the dedicated cluster is isolated and no data is sent to third-party models. Fine-tuning also provides the accuracy needed for medical report generation. Option B uses a multi-tenant environment, which may have data isolation concerns but still within OCI; however, it does not involve fine-tuning, so it is less optimal for the medical domain. Option C uses a dedicated cluster but with a base model, which means no fine-tuning on medical data, so it may not provide the required accuracy, though data stays within OCI. Option D sends data to a third-party LLM, violating PHI requirements.

Answer

Use OCI Gen AI service with base model in a multi-tenant environment

Answer

Use OCI Gen AI service with base model in a dedicated AI cluster

Answer

Use a third-party LLM via API Gateway

Question 11

A financial firm wants to use OCI Generative AI for contract analysis. They need to reduce costs by using a smaller, specialized model. Which approach should they take?

Accepted Answer

Fine-tune a smaller base model on a dedicated AI cluster. Fine-tuning a smaller base model on a dedicated AI cluster allows the financial firm to tailor the model specifically for contract analysis tasks, reducing computational overhead and cost compared to using a large general-purpose model. OCI Generative AI supports fine-tuning of smaller models like Cohere Command Light on dedicated AI clusters, enabling domain-specific optimization without the expense of running a large model for every inference.

Answer

Use a large base model (e.g., Cohere Command) on a serverless endpoint

Answer

Use a large base model on a dedicated AI cluster

Answer

Use a third-party LLM

Question 12

A company is deploying OCI Generative AI for a chatbot that must answer customer queries within 500ms. They choose a dedicated AI cluster but observe 2-second latency. What is the most likely cause?

Accepted Answer

The cluster is configured for batch inference. A dedicated AI cluster in OCI Generative AI is designed for real-time inference with low latency. When the cluster is configured for batch inference, it processes requests in batches rather than individually, which introduces queuing and processing delays that can easily exceed the 500ms target. This explains the observed 2-second latency, as batch mode prioritizes throughput over per-request response time.

Answer

The endpoint is not cached

Answer

The request includes too many tokens

Answer

The model is too large for the cluster

Question 13

An organization wants to use OCI Generative AI for real-time document translation. They need high availability across regions. Which deployment option meets this requirement?

Accepted Answer

Multiple dedicated AI clusters in different regions with a load balancer. Deploying multiple dedicated AI clusters across different regions with a load balancer ensures high availability by distributing traffic and providing failover if one region becomes unavailable. OCI Generative AI dedicated AI clusters are provisioned per region, and a load balancer can route requests to healthy clusters, meeting the requirement for real-time document translation with cross-region redundancy.

Answer

Single dedicated AI cluster in one region

Answer

Single serverless endpoint

Answer

Multiple serverless endpoints in different regions

Question 14

A company wants to use OCI Generative AI but must comply with GDPR. Which feature ensures data residency?

Accepted Answer

Data localization with dedicated AI clusters. OCI Generative AI's data localization feature with dedicated AI clusters ensures that customer data remains within a specific geographic region, directly addressing GDPR's data residency requirements. By provisioning dedicated clusters in a chosen OCI region, the company can guarantee that data processing and storage do not cross borders without explicit consent, meeting the GDPR principle of restricting data transfer outside the EU/EEA.

Answer

Encryption at rest

Answer

Access control policies

Answer

Audit logging

Question 15

A user gets a 'Model not found' error when calling an OCI Gen AI endpoint. What is the most likely cause?

Accepted Answer

The endpoint is not deployed. The 'Model not found' error in OCI Generative AI typically occurs when the model endpoint has not been deployed or activated in the user's tenancy. Even if the model is available in the region and the request format is correct, the endpoint must be explicitly deployed (e.g., via the OCI Console, CLI, or SDK) before inference calls can succeed. This is a common prerequisite for using dedicated AI endpoints in OCI.

Answer

The model is not available in the region

Answer

The request format is wrong

Answer

The API key is invalid