1Z0-1127-25 Using OCI Generative AI Service Practice Test 9 — 15 Questions

Question 1

A financial institution wants to use OCI Generative AI to analyze sensitive customer documents. They need to ensure no data leaves OCI and the model is fine-tuned on their proprietary data. Which deployment option should they choose?

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

Accepted Answer

Dedicated AI cluster with private endpoint.. A Dedicated AI cluster with private endpoint ensures that all data processing and model fine-tuning occur within OCI's network boundary, with no data egress to the public internet. This deployment option provides a fully isolated environment where the model can be fine-tuned on proprietary customer data while meeting strict data residency and security requirements.

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Serverless inference with data isolation.

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OCI Functions with GPU.

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OCI Data Science notebook session.

Question 2

Which THREE factors should be considered when choosing a model for a summarization task using OCI Generative AI?

Accepted Answer

Number of parameters.. Option B (Number of parameters) matters because parameter count drives the model's capacity to capture language patterns and its trade-off between output quality and inference cost/latency, which directly affects summarization quality. Option D (Context window size) is critical because summarization requires ingesting the full source document; if the input exceeds the model's token context window, the text must be truncated or chunked, degrading the summary. Option E (Supported languages) must be considered because OCI Generative AI models differ in language coverage, and a model must support the language of the source text to produce a coherent summary. Option A (Inference endpoint location) is not a model-selection factor for summarization quality; endpoints are a deployment concern, not a model capability. Option C (Model training data cut-off date) is not a primary selection criterion for summarization, since the task is text transformation rather than knowledge retrieval, so recency of training data does not determine summarization suitability.

Answer

Inference endpoint location.

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Model training data cut-off date.

Question 3

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.

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Decrease the temperature to 0.2.

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Increase the temperature to 0.9.

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Use a different base model like Llama.

Question 4

A healthcare organization is using OCI Generative AI to analyze medical records. They must comply with HIPAA. They have set up a dedicated AI cluster with private endpoints. However, they are concerned about model hallucinations that could lead to incorrect medical advice. They want to minimize hallucinations while maintaining usefulness. Which approach is most effective?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline with a verified medical knowledge base.. A Retrieval-Augmented Generation (RAG) pipeline grounds the model's responses in a verified medical knowledge base, directly reducing hallucinations by ensuring outputs are based on retrieved facts rather than purely generative predictions. This approach maintains usefulness by allowing the model to reference authoritative sources, which is critical for HIPAA compliance and accurate medical advice.

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Use a smaller model to reduce complexity.

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Increase the temperature to encourage diverse outputs.

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Reduce max tokens to force shorter responses.

Question 5

A startup is using OCI Generative AI serverless inference for a text generation application. They notice that the latency is high during peak hours. They have a budget to increase costs moderately. Which action would most effectively reduce latency?

Accepted Answer

Switch to dedicated AI cluster.. Switching to a dedicated AI cluster is the most effective way to reduce latency during peak hours because it provides dedicated, isolated compute capacity that is not subject to the shared, multi-tenant contention of serverless inference. With serverless, throughput and latency can degrade when many customers share the same model endpoints. A dedicated cluster gives predictable, reserved GPU capacity and can be sized to handle peak load, directly addressing the latency spike.

Answer

Enable content filtering.

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Increase the number of concurrent requests.

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Use a smaller model.

Question 6

A data scientist is fine-tuning a model on OCI Generative AI to generate code comments. They use a dataset of 10,000 examples. After fine-tuning, the model generates comments that are too similar to the training data and lack generalization. What is the most likely cause?

Accepted Answer

Too many training epochs.. When a fine-tuned model generates outputs that are too similar to the training data and lack generalization, it is a classic sign of overfitting. Overfitting occurs when the model is trained for too many epochs, causing it to memorize the training examples rather than learning the underlying patterns. In OCI Generative AI, the fine-tuning process adjusts model weights iteratively, and excessive epochs lead to poor performance on unseen data.

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Incorrect tokenizer.

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Insufficient training data.

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Too high learning rate.

Question 7

A company uses OCI Generative AI to generate legal document summaries. They have a custom model deployed on a dedicated AI cluster. They want to ensure that the model is not used by unauthorized users. They also need to log all inference requests for auditing. Which combination of OCI services should they use?

Accepted Answer

OCI Identity and Access Management (IAM) policies and OCI Logging.. OCI IAM policies are the primary mechanism for controlling access to OCI resources, including custom models on dedicated AI clusters, by defining which users or groups can invoke the model. OCI Logging captures detailed logs of all inference requests, including metadata such as timestamps, source IPs, and request payloads, which satisfies the auditing requirement. Together, they provide both authorization enforcement and audit trail without additional services.

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OCI Vault for encryption and OCI Audit for logging.

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OCI Data Safe and OCI Monitoring.

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OCI API Gateway with authentication and OCI Audit.

Question 8

A developer needs to authenticate API calls to OCI Generative AI from a compute instance. Which TWO methods can be used?

Accepted Answer

Configure an API key in OCI IAM for the user. Option A is correct because OCI IAM API keys (a public/private RSA key pair uploaded to the user's IAM profile) are a standard way for a developer to sign OCI API requests, including calls to the Generative AI inference endpoints, using the OCI SDK/CLI with the user's credentials. Option D is correct because resource principal with instance principals lets code running on a compute instance authenticate to OCI services—including Generative AI—without embedding user credentials, by using the instance's dynamic group membership and IAM policies to obtain a short-lived security token. Option B is not an authentication method; a customer-managed key (CMK) in OCI Vault is used for encryption at rest, not for authenticating API calls. Option C is not an authentication mechanism; a service connector moves data between services via a connector hub and does not authenticate a developer's direct API calls to Generative AI. Option E is not appropriate here; an auth token is a user credential used for OCI registry (OCIR) and other token-based flows, not for signing Generative AI API requests from a compute instance.

Answer

Configure a customer-managed key (CMK) for encryption

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Set up a service connector to forward requests

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Use an auth token from OCI Identity

Question 9

A solutions architect is deploying a customer-facing application that uses OCI Generative AI on-demand models. The application experiences highly variable traffic, with occasional bursts of concurrent requests. The architect needs to ensure that the application handles throttling gracefully and does not fail during peak load. Which approach should the architect take?

Accepted Answer

Implement exponential backoff and retry logic in the application code to handle 429 Too Many Requests responses.. The service returns HTTP 429 when rate limits are exceeded. Exponential backoff with jitter is the standard client-side pattern to handle throttling: it retries after increasing delays, reduces contention, and prevents synchronized retries. Fixed intervals can worsen throttling, max tokens affects response length not rate, and dedicated clusters still have limits.

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Configure a fixed retry interval of 1 second for all failed requests to ensure rapid recovery.

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Increase the model's max tokens parameter to allow more requests per minute.

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Deploy the model on a dedicated AI cluster to eliminate rate limits entirely.

Question 10

A data scientist is using OCI Generative AI to generate summaries of legal documents. The documents are long, often exceeding the model's context window. The data scientist wants to ensure that the entire document is considered while minimizing information loss. Which strategy should they use?

Accepted Answer

Split the document into chunks, generate summaries for each chunk, and then combine the summaries into a final summary.. For long documents exceeding the context window, the map-reduce summarization pattern is the most reliable: chunk the document, summarize each chunk, then summarize the summaries. Truncation loses information, temperature does not extend context, and even large context models have limits and cost implications. This approach ensures the entire document is considered while staying within model constraints.

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Truncate the document to the first N tokens that fit the context window and generate a summary from that.

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Use a larger model that supports a longer context window, such as a 128k token model, and pass the entire document in one request.

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Increase the temperature parameter to encourage the model to generate more comprehensive summaries.

Question 11

A solutions architect is deploying a customer-facing application on OCI that uses the OCI Generative AI Service. Security policy requires that all API traffic to the service never traverse the public internet. The application runs on compute instances in a private subnet of a VCN. Which capability should the architect use to satisfy this requirement?

Accepted Answer

Configure a service gateway in the VCN and create a private endpoint for the OCI Generative AI Service.. Private connectivity to OCI Generative AI is achieved by combining a service gateway in the VCN with a private endpoint for the service. This keeps inference traffic on the Oracle network, avoiding the public internet entirely, and is the supported pattern for workloads that must not egress publicly. Other connectivity options either route over the internet or connect to on-premises networks rather than to the service itself.

Answer

Enable a NAT gateway in the VCN so outbound calls from the private subnet are translated and remain invisible to external observers.

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Use a dynamic routing gateway with a Site-to-Site VPN back to the on-premises data center, then proxy Generative AI calls from there.

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Attach an internet gateway to the private subnet and restrict access with a network security group rule that allows only the Generative AI service CIDR.

Question 12

A data scientist fine-tuned a base model in OCI Generative AI on a proprietary dataset and now wants to serve inference. They must keep unit costs predictable and avoid the operational burden of provisioning GPU infrastructure themselves. Which deployment choice should they make?

Accepted Answer

Use on-demand model hosting for the fine-tuned model and call it through the inference endpoint using its OCID.. On-demand hosting serves a fine-tuned model through the managed inference endpoint without the customer managing GPU infrastructure, and its consumption-based billing keeps unit costs predictable. Referencing the model by OCID lets applications switch between base and fine-tuned models with minimal change. Dedicated AI clusters offer isolation and reserved capacity but shift scaling and capacity planning back to the customer.

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Create a dedicated AI cluster with the fine-tuned model and manage the cluster's scaling manually.

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Deploy the fine-tuned model to a dedicated AI cluster that auto-scales with a minimum of one node and a maximum of ten nodes.

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Export the fine-tuned model artifacts and run them on a self-managed GPU instance in a compute cluster.

Question 13

A developer is building an OCI Generative AI application that streams generated text to end users. They call the OCI Generative AI inference endpoint with the appropriate chat request and need to process the response incrementally as tokens arrive rather than waiting for the full completion. Which capability of the OCI Generative AI inference API should they use?

Accepted Answer

Set the streaming flag on the chat request so the service returns a server-sent event stream of partial responses.. The OCI Generative AI chat inference API offers a streaming mode that emits partial responses as the model generates tokens, which is exactly what an application needs to display output incrementally. Other chat parameters such as maxTokens shape the completion but do not change how it is delivered, and unrelated operations like summarizeText or dedicated cluster hosting do not provide token-level streaming.

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Increase the maxTokens parameter so the entire completion is buffered and returned faster in a single response.

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Enable a dedicated AI cluster endpoint and poll the inference job status until the full text is available.

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Call the summarizeText operation repeatedly with a growing conversation buffer to emulate incremental output.

Question 14

A developer is building a summarization feature that calls the OCI Generative AI chat API. They want the model to produce short, factual summaries and avoid inventing details that are not present in the source text. Which parameter setting best supports this goal?

Accepted Answer

Set temperature to a low value such as 0.1 and craft a prompt that instructs the model to summarize only using information in the provided text.. Faithful summarization is best encouraged by combining a low temperature with a prompt that explicitly limits the model to information in the source. Low temperature reduces random sampling variance, and the prompt constrains the model's scope. Parameters such as top_k and max_tokens control diversity and length limits but do not themselves enforce factual grounding.

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Set temperature to a high value such as 0.9 and leave top_p at its default.

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Set top_k to a large value so more candidate tokens are considered at each step.

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Increase max_tokens substantially so the model has more room to explain each summary point.

Question 15

A solutions architect must let an application call OCI Generative AI inference endpoints without embedding long-lived user credentials in source code. The application runs on OCI Compute instances, and the security team wants authentication handled through OCI Identity and Access Management with automatic credential rotation. Which approach meets these requirements?

Accepted Answer

Create an IAM policy that grants the Generative AI family permissions to a dynamic group matching the compute instances, and configure the SDK to use instance principals.. Instance principals give compute instances a platform-managed identity with automatically rotated certificates, so no secret needs to live in code. Authorizing a dynamic group that matches those instances through an IAM policy scoped to generative-ai-family permissions completes the least-privilege, rotation-friendly design. Static API keys, customer secret keys, or anonymous access all fail the stated security constraints.

Answer

Create an API signing key for an IAM user, embed the private key in the application configuration file, and rotate it quarterly with a script.

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Generate a customer secret key for a service user, store it in the instance metadata, and read it at runtime from the metadata service.

Answer

Configure the OCI Generative AI endpoint to accept anonymous calls by disabling authorization on the inference route.