1Z0-1127-25 Using OCI Generative AI Service Practice Test 7 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

A data scientist is fine-tuning a model on OCI Generative AI with a custom dataset. They receive a "QuotaExceeded" error during training. What is the most likely cause?

Accepted Answer

Exceeded the training compute unit quota. The 'QuotaExceeded' error during fine-tuning on OCI Generative AI specifically indicates that the training job has consumed more compute units than allocated in the service limit. Fine-tuning requires dedicated training compute units (TCUs) which are a separate quota from inference or API calls. When this quota is exhausted, the service rejects new training jobs with this error.

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Exceeded the API call rate limit

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Exceeded the model storage limit

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Exceeded the data transfer out limit

Question 6

Which OCI Generative AI parameter controls the diversity of generated text by increasing the probability of less likely tokens?

Accepted Answer

temperature. The temperature parameter in OCI Generative AI controls the randomness of token selection by scaling the logits before applying the softmax function. A higher temperature (e.g., > 1.0) increases the probability of less likely tokens, making the output more diverse and creative, while a lower temperature (e.g., < 1.0) sharpens the distribution toward the most likely tokens, producing more deterministic and conservative text.

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frequency_penalty

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top_k

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top_p

Question 7

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 8

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 9

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. Option A is correct because OCI Generative AI fine-tuning performs supervised training that updates the model's weights using the customer's custom dataset, so the model learns task-specific patterns rather than relying only on prompt context. Option E is correct because fine-tuning requires a labeled dataset of input-output pairs (for example, JSONL records with prompt and completion fields) that the service uses as training examples. Option B is wrong because fine-tuning supports datasets far larger than 10 examples, with service limits typically in the thousands of records depending on the model. Option C is wrong because fine-tuning creates a custom, dedicated fine-tuned model in the customer's tenancy and does not permanently alter the shared base model. Option D is wrong because few-shot prompting supplies examples at inference time without changing weights, whereas fine-tuning actually trains and persists new weights.

Answer

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 10

A developer makes an API call to generate text with top_p=1.5. What is the correct way to fix this error?

Accepted Answer

Set top_p to a value between 0 and 1, e.g., 0.9. The `top_p` parameter, also known as nucleus sampling, must be a probability value between 0 and 1. Setting it to 1.5 is invalid because it exceeds the allowed range, which would cause the API to reject the request. The correct fix is to set `top_p` to a valid value such as 0.9, which restricts token selection to the smallest set whose cumulative probability exceeds that threshold.

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Remove the top_p parameter from the request

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Increase the temperature parameter to compensate

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Use the top_k parameter instead

Question 11

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.

Question 12

A company is using OCI Generative AI to generate code snippets and notices that the model sometimes produces code with security vulnerabilities. They have a small dataset of secure code examples. Which approach would be most effective to reduce vulnerabilities?

Accepted Answer

Use prompt engineering with security constraints in the instruction.. Prompt engineering allows the company to inject security constraints directly into the instruction without requiring additional training data or infrastructure. By crafting a prompt that explicitly requests secure code (e.g., 'Generate code that follows OWASP Top 10 best practices and avoids SQL injection, XSS, and buffer overflows'), the model can leverage its existing knowledge to produce safer outputs. This approach is immediate, cost-effective, and does not depend on the size or quality of the small secure code dataset.

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Use a different base model.

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Fine-tune the model on the small secure code dataset.

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Deploy a custom model hosted elsewhere.

Question 13

An organization wants to use OCI Generative AI to summarize long legal documents. They need to ensure the summary is concise and retains key information. Which model parameter should they set to control the length of the summary?

Accepted Answer

max_tokens. The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) the model can generate in its response. By setting a lower value for `max_tokens`, the organization can enforce a concise summary, ensuring the output length is capped while retaining key information. Other parameters like `temperature` or `top_p` affect randomness or diversity, not output length.

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frequency_penalty

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top_p

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temperature

Question 14

A company uses OCI Generative AI service with a Cohere Command model for a real-time chat application and experiences high latency. They have already set max_tokens to 50 and temperature to 0.2. Which further change would be most effective in reducing latency?

Accepted Answer

Switch to a smaller model variant.. Switching to a smaller model variant (e.g., from Command to Command-Light) directly reduces the number of parameters and computational steps per token, which lowers inference latency. Since the company has already minimized max_tokens and temperature, the next most impactful change is to use a less resource-intensive model. This is a common optimization for real-time applications where response speed is critical.

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Use asynchronous invocation.

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Disable context caching.

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Increase the number of GPUs.

Question 15

Which OCI Generative AI service model family supports fine-tuning with custom datasets?

Accepted Answer

Cohere Command. Cohere Command is the model family within OCI Generative AI that supports fine-tuning with custom datasets, allowing users to adapt the model for domain-specific tasks like summarization or classification. In contrast, Cohere Embed is designed for generating text embeddings, Cohere Summarize is a specialized endpoint for summarization without fine-tuning support, and GPT-3 is not natively available in OCI Generative AI for fine-tuning.

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Cohere Embed

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Cohere Summarize

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GPT-3