1Z0-1127-25 Using OCI Generative AI Service Practice Test 6 — 15 Questions

Question 1

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.. Network security groups (NSGs) allow you to restrict inbound traffic to the Generative AI inference endpoint to only trusted source IP addresses, reducing the attack surface. In a production environment, this is a fundamental network-layer security control to prevent unauthorized access to the API.

Answer

Enable encryption with OCI Vault keys for all inference data.

Answer

Apply data masking policies to obfuscate sensitive information in prompts.

Question 2

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

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Dedicated AI cluster with fine-tuned model

Answer

Serverless endpoint with base model

Question 3

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 4

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

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Use a third-party LLM

Question 5

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

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The request includes too many tokens

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The model is too large for the cluster

Question 6

A developer needs to integrate OCI Generative AI into a Python application. Which SDK should they use?

Accepted Answer

OCI Python SDK. The OCI Python SDK (Option B) is the correct choice because it provides the official set of libraries and tools for interacting with Oracle Cloud Infrastructure services, including the Generative AI service. This SDK handles authentication, request signing, and API calls specific to OCI, enabling seamless integration of OCI Generative AI into Python applications.

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Boto3

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Google Cloud client

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OpenAI library

Question 7

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

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Single serverless endpoint

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Multiple serverless endpoints in different regions

Question 8

Refer to the exhibit. A user receives this error when calling the OCI Gen AI inference endpoint. What is the most likely cause?

Accepted Answer

The model is not deployed in the region. The error indicates that the model is not available in the specified region. OCI Gen AI models are deployed regionally, and each region supports only a specific subset of models. If the user calls an endpoint in a region where the requested model has not been deployed, the service returns an error because the model's inference endpoint does not exist in that region's routing table.

Answer

The region name is misspelled

Answer

The API key is expired

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The model name is incorrect

Question 9

Refer to the exhibit. A user runs 'oci generative-ai model list' and sees this output. They then try to use 'cohere.command-light' but get an error. What is the most likely reason?

Accepted Answer

The model is in INACTIVE state. The model 'cohere.command-light' has lifecycle-state 'INACTIVE', meaning it cannot be used. Option B is false because the API key issue would produce a different error; Option C is false because the model is listed; Option D is false because a region mismatch would also produce a different error.

Answer

The API key does not have access

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The model is not listed

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The region is wrong

Question 10

A company requires a generative AI service to automatically summarize customer support transcripts. Which OCI Generative AI model is most suitable for this task?

Accepted Answer

Cohere Command. Cohere Command is a large language model specifically designed for text generation tasks such as summarization, making it the most suitable choice for automatically summarizing customer support transcripts. Unlike embedding models or base Llama variants, Command is optimized for instruction-following and generating coherent, concise summaries from conversational data.

Answer

Llama 3 70B

Answer

Cohere Embed

Answer

Fine-tuned Llama 2

Question 11

A team is building a Retrieval-Augmented Generation (RAG) pipeline using OCI Generative AI. They need to store and retrieve document embeddings for semantic search. Which OCI service is most appropriate as the vector store?

Accepted Answer

OCI Search with OpenSearch. OCI Search with OpenSearch is the most appropriate vector store for a RAG pipeline because it natively supports storing and querying high-dimensional vector embeddings using the k-nearest neighbor (k-NN) algorithm. It integrates directly with OCI Generative AI to enable semantic search over ingested documents, providing the required similarity search capabilities for retrieval-augmented generation.

Answer

OCI Streaming

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OCI Object Storage

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OCI Autonomous Database with AI Vector Search

Question 12

A data scientist wants to fine-tune a generative AI model on proprietary customer data. What is a best practice for preparing the training dataset?

Accepted Answer

Curate a dataset of domain-specific examples with clear input-output pairs.. Fine-tuning a generative AI model on proprietary data requires a curated, domain-specific dataset with clear input-output pairs. This ensures the model learns the desired task (e.g., summarization, classification) without introducing noise or irrelevant patterns, which is critical for OCI Generative AI Service fine-tuning where data quality directly impacts model performance.

Answer

Randomly sample 1000 records from production logs.

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Use the same dataset as the base model's pre-training data.

Answer

Use the largest available public dataset from the internet.

Question 13

Which authentication method should be used to securely call the OCI Generative AI API from a microservice running on OCI Compute?

Accepted Answer

OCI API signing key. The OCI API signing key method uses a pre-generated RSA key pair to sign each HTTP request cryptographically, which is the standard and most secure way to authenticate calls to OCI services, including the Generative AI API, from a microservice running on OCI Compute. This method does not rely on external identity providers or token exchanges, making it ideal for server-to-server communication within OCI.

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OAuth 2.0 client credentials

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SAML 2.0 assertion

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Instance principal

Question 14

A company runs batch inference jobs daily using the OCI Generative AI service. The current cost is higher than expected. Which change would most effectively reduce cost while maintaining throughput?

Accepted Answer

Switch from on-demand to dedicated AI cluster with batch endpoint.. Switching from on-demand to a dedicated AI cluster with a batch endpoint reduces cost because dedicated clusters provide reserved capacity at a lower per-token rate compared to on-demand pay-per-token pricing, and batch endpoints allow you to process multiple inference requests in a single job, amortizing overhead and reducing idle time. This combination directly addresses the high cost of per-request on-demand pricing while maintaining the same throughput for daily batch jobs.

Answer

Reduce the max token limit for all requests.

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Use a larger model to reduce retries.

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Increase the number of parallel requests to improve efficiency.

Question 15

A retail company wants to generate product descriptions from attribute data. They have no prior AI experience. Which approach is most appropriate?

Accepted Answer

Use the Cohere Command model with carefully crafted prompts.. The Cohere Command model is specifically designed for text generation tasks like creating product descriptions from attribute data. With no prior AI experience, using a pre-trained model with carefully crafted prompts is the most efficient and accessible approach, requiring no custom training or complex infrastructure.

Answer

Train a custom model from scratch.

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Fine-tune a model on a synthetic dataset.

Answer

Use the Cohere Embed model to generate embeddings and then decode.