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

Question 1

A company is deploying a customer-facing chatbot using OCI Generative AI. They need to prevent the model from generating offensive or harmful content. Which feature should they implement?

Accepted Answer

Enable OCI Generative AI guardrails with content filtering.. OCI Generative AI guardrails provide built-in content filtering that automatically detects and blocks offensive or harmful content in both user inputs and model outputs. This is the recommended, native feature for safety compliance, eliminating the need for custom post-processing and ensuring consistent policy enforcement across all chatbot interactions.

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Custom post-processing to scan each response.

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Limit the user input length.

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Use a smaller model that is less capable of generating harm.

Question 2

Which TWO are benefits of using dedicated AI clusters for OCI Generative AI?

Accepted Answer

Guaranteed throughput. Dedicated AI clusters provide guaranteed throughput because the compute and networking resources are exclusively allocated to your workloads, eliminating contention from other tenants. This ensures that the model inference capacity is always available at the level you provision, which is critical for production applications with strict performance requirements.

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Automatic model updates

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Lower cost than on-demand for all workloads

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No need to manage scaling

Question 3

Refer to the exhibit. The user requested a long story but the response is cut short. What is the most likely cause?

Accepted Answer

The max_tokens parameter is too low for the requested length.. The `max_tokens` parameter explicitly limits the number of tokens (words/subwords) the model can generate in a single response. If the user requests a long story but `max_tokens` is set too low, the model will stop generating once the token limit is reached, resulting in a truncated output. This is a common configuration issue when using OCI Generative AI Service or any large language model API.

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The model content filter blocked part of the output.

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There was a network error during inference.

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The model is not capable of generating long stories.

Question 4

Refer to the exhibit. The group DataScientists can run inference but cannot fine-tune a model on a dedicated AI cluster. Which additional policy statement is required to allow fine-tuning?

Accepted Answer

Allow group DataScientists to manage dedicated-ai-clusters in compartment ABC. To fine-tune a model on a dedicated AI cluster, the user must have the `manage` permission on the `dedicated-ai-clusters` resource type in the compartment. The `inspect` permission (Option A) only allows viewing metadata, not performing write operations like fine-tuning. The `use` permission (Option D) is insufficient for management tasks. Option C is invalid because `generative-ai-fine-tune` is not a valid resource type in OCI IAM policies.

Answer

Allow group DataScientists to inspect dedicated-ai-clusters in compartment ABC

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Allow group DataScientists to use generative-ai-fine-tune in compartment ABC

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Allow group DataScientists to use dedicated-ai-clusters in compartment ABC

Question 5

Refer to the exhibit. In this RAG pipeline, what is the role of the 'embedding_model' variable?

Accepted Answer

It converts text into vector representations for similarity search.. In a Retrieval-Augmented Generation (RAG) pipeline, the 'embedding_model' variable is responsible for converting input text (such as user queries or document chunks) into dense vector representations. These vectors are then used to perform similarity search against a vector database, enabling the retrieval of the most relevant context for the generative model. This is a core function of the embedding step in RAG, not a guardrail, fine-tuning, or text generation role.

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It applies guardrails to filter content.

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It fine-tunes the model on the provided texts.

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It generates text completions based on prompts.

Question 6

A healthcare company wants to use OCI Generative AI to summarize patient medical records while ensuring PHI compliance. Which OCI service feature should they enable?

Accepted Answer

Use the data masking capability within OCI Generative AI. OCI Generative AI includes a built-in data masking capability specifically designed to automatically detect and redact sensitive information such as Protected Health Information (PHI) from input prompts and generated outputs. This feature ensures compliance with healthcare regulations like HIPAA by preventing PHI from being stored, logged, or exposed during inference, without requiring external preprocessing or post-processing.

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Configure a Virtual Cloud Network (VCN) with private subnets

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Deploy a Web Application Firewall (WAF) in front of the API

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Set up Identity and Access Management (IAM) policies to restrict access

Question 7

An enterprise is fine-tuning a Cohere model using OCI Generative AI for a domain-specific task. After training, the model shows high accuracy on validation data but poor performance on unseen test data. What is the most likely cause?

Accepted Answer

The model overfitted to training data. The scenario describes high accuracy on validation data but poor performance on unseen test data, which is the classic symptom of overfitting. In OCI Generative AI, when fine-tuning a Cohere model, overfitting occurs when the model memorizes the training data rather than learning generalizable patterns, often due to excessive training epochs, a small dataset, or high model capacity relative to the data.

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The training dataset was too small

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The number of training epochs was too low

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The learning rate was set too high

Question 8

A developer wants to generate text embeddings using OCI Generative AI. Which model endpoint should they call?

Accepted Answer

POST /v1/embed. The correct endpoint for generating text embeddings in OCI Generative AI is POST /v1/embed. Embeddings convert text into dense vector representations that capture semantic meaning, enabling tasks like similarity search and clustering. The other endpoints serve different purposes: /v1/generate for text generation, /v1/summarize for summarization, and /v1/chat for conversational interactions.

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POST /v1/generate

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POST /v1/summarize

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POST /v1/chat

Question 9

A company is using OCI Generative AI for customer support chatbots. They notice that responses sometimes include offensive content. Which built-in safety feature should they configure?

Accepted Answer

Content moderation filters. Content moderation filters are the built-in safety feature in OCI Generative AI that automatically detect and block offensive, harmful, or inappropriate content in model responses. This is the correct configuration to prevent offensive outputs in customer support chatbots, as it applies predefined safety policies to filter toxic language, hate speech, and other prohibited content before the response is delivered.

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Configure stop sequences

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Set a maximum token limit

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Adjust the temperature parameter

Question 10

A developer is using the OCI Generative AI SDK to call a custom fine-tuned model. They get an HTTP 404 error. What is the most likely issue?

Accepted Answer

The model endpoint ID is incorrect. An HTTP 404 error indicates that the requested resource was not found on the server. When using the OCI Generative AI SDK to call a custom fine-tuned model, the model endpoint ID is a unique identifier that maps to the deployed model's endpoint. If this ID is incorrect, the API request cannot locate the model, resulting in a 404 error. The other options would typically cause different HTTP status codes, such as 401 for invalid API keys or 400 for missing required parameters.

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The API key is invalid

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The compartment OCID is missing in the request

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The region is not specified

Question 11

Which OCI Generative AI capability allows you to provide example input-output pairs to guide the model's behavior without fine-tuning?

Accepted Answer

Few-shot learning. Few-shot learning is the correct answer because it allows you to provide a small number of example input-output pairs directly in the prompt to guide the model's behavior for a specific task, without updating the model's weights through fine-tuning. In OCI Generative AI, this technique leverages the model's in-context learning ability to adapt its responses based on the provided examples, making it ideal for quick task adaptation without the cost or complexity of retraining.

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Reinforcement Learning from Human Feedback (RLHF)

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Prompt engineering

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Fine-tuning

Question 12

Which TWO statements about OCI Generative AI fine-tuning are true? (Choose two.)

Accepted Answer

Fine-tuning adjusts the model's weights based on custom data. Fine-tuning in OCI Generative AI adjusts the model's weights using custom training data, which allows the model to learn domain-specific patterns and improve performance on targeted tasks. This process modifies the internal parameters of the base model, making option A correct because it directly describes the core mechanism of fine-tuning.

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Fine-tuning can only handle up to 10 examples

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Fine-tuning permanently alters the base model in OCI

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Fine-tuning is equivalent to providing few-shot examples in the prompt

Question 13

A developer runs this CLI command but receives only one response instead of three. What is the most likely cause?

Accepted Answer

The model specified does not support multiple generations. The OCI Generative AI service's `cohere.command` model does not support the `--num-generations` parameter for generating multiple responses in a single invocation. The `--num-generations` parameter is only supported by certain models like `cohere.command-light` or `meta.llama-2-70b-chat`, and using it with an unsupported model results in a single generation being returned, ignoring the parameter.

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The parameter --num-generations is misspelled; should be --num-generations-to-generate

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The --max-tokens limit is too low to return multiple generations

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The API version is outdated and does not support the num-generations parameter

Question 14

An administrator creates this IAM policy to allow a group to use a specific generative AI model. However, users report a 403 Forbidden error. What is the most likely issue?

Accepted Answer

The policy syntax is invalid for OCI IAM; missing required fields. The most likely issue is that the policy syntax is invalid because OCI IAM policies require a 'verb' (e.g., 'allow', 'deny') at the beginning of each statement. Without this required field, the policy is syntactically incorrect and will be rejected by the IAM service, resulting in a 403 Forbidden error for all users in the group.

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The resource OCID is incorrect

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The policy does not specify the compartment where the model resides

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The action name is wrong; it should be 'ai:generate-text' (correct already)

Question 15

A developer is using OCI Generative AI service's CLI to generate text but gets a 'Rate limit exceeded' error. What is the most likely cause?

Accepted Answer

The number of requests per minute exceeds the allowed quota.. The 'Rate limit exceeded' error indicates that the client has sent more requests than the allowed quota within a specific time window. OCI Generative AI service enforces rate limits (e.g., requests per minute) to protect backend resources and ensure fair usage. When the developer exceeds this threshold, the service returns a 429 HTTP status code with the 'Rate limit exceeded' message.

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The API key is invalid.

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The model is not available in that region.

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The input text is too long.