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

Question 1

An application using OCI Generative AI produces inconsistent responses to the same user query. The developer suspects the model's output variability is too high. Which parameter adjustment would most directly reduce output randomness?

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

Accepted Answer

Decrease the temperature parameter.. Temperature directly controls the randomness of token sampling in the model's output distribution. Lowering temperature (e.g., from 0.7 to 0.2) makes the model more deterministic by concentrating probability mass on the most likely next tokens, thus reducing output variability for the same query.

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Increase the max tokens parameter.

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Increase the top_p parameter.

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Change the model to a smaller variant.

Question 2

Which THREE models are available as part of the OCI Generative AI service?

Accepted Answer

Llama 3. Llama 3 is one of the open-source large language models (LLMs) available through the OCI Generative AI service, alongside Cohere models. OCI Generative AI provides managed access to Llama 3 for text generation tasks, allowing users to deploy and fine-tune it within Oracle Cloud Infrastructure.

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GPT-4

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Stable Diffusion

Question 3

Which TWO factors are most important when deciding between on-demand and dedicated AI clusters for OCI GenAI?

Accepted Answer

Number of concurrent requests. The number of concurrent requests (D) is critical because dedicated AI clusters provide guaranteed throughput and predictable performance for high-volume workloads, while on-demand clusters may throttle or queue requests under heavy load. Latency requirements (E) are equally important because dedicated clusters offer consistent low-latency inference by avoiding resource contention, whereas on-demand clusters can introduce variable latency due to shared infrastructure. Together, these factors directly determine whether a workload needs the isolation and guaranteed resources of a dedicated cluster or can tolerate the elasticity and potential variability of on-demand provisioning.

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Fine-tuning capability

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Model size

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Data residency

Question 4

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 5

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 6

A company wants to use OCI Generative AI to analyze legal documents and extract key clauses. Which model type is best suited for this task?

Accepted Answer

Cohere Summarize. Cohere Summarize is specifically designed to condense long documents into concise summaries, making it ideal for extracting key clauses from legal documents. Unlike other Cohere models, Summarize focuses on distilling the most important information from text, which aligns with the task of identifying and extracting critical clauses.

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Cohere Command (generate)

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Cohere Chat

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Cohere Embed

Question 7

Which TWO actions are required to use a custom fine-tuned model via OCI Generative AI? (Choose two.)

Accepted Answer

Deploy the model to an endpoint. To use a custom fine-tuned model via OCI Generative AI, you must first successfully complete the fine-tuning job (E) to create the model, and then deploy it to an endpoint (A) to enable inference. Options B (private endpoint) is optional for networking control, not required. C (cross-region replication) is not needed. D (granting access to other tenancies) is only required if you want to share the model across tenancies, not for your own usage.

Answer

Provision a private endpoint for the model

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Enable cross-region replication

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Grant access to other tenancies

Question 8

Which THREE parameters can be adjusted to reduce repetition in generated text? (Choose three.)

Accepted Answer

presence_penalty. A is correct because presence_penalty directly penalizes tokens that have already appeared in the generated text, reducing the likelihood of repeating the same concepts or phrases. This parameter works by subtracting a fixed penalty from the logits of tokens that are already present in the sequence, making the model less likely to reuse them.

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max_tokens

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temperature

Question 9

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 10

A company wants to use OCI Generative AI service to automatically generate product descriptions for an e-commerce catalog. They have 10,000 products. What is the best approach to ensure high-quality, consistent descriptions?

Accepted Answer

Fine-tune a base model on a dataset of existing product descriptions.. Fine-tuning a base model on a dataset of existing product descriptions is the best approach because it adapts the model to the specific domain, style, and vocabulary of the e-commerce catalog. This ensures high-quality, consistent outputs across 10,000 products by learning the patterns and terminology from the company's own data, rather than relying on generic or template-based methods.

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Use a pre-trained summarization model.

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Use a template-based generation with keyword insertion.

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Use the built-in chat model with few-shot examples in the prompt.

Question 11

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 12

A data scientist is fine-tuning a Cohere model on OCI Generative AI service for a custom classification task. They have a dataset of 1000 labeled examples. What is the minimum recommended dataset size for fine-tuning?

Accepted Answer

1000. Cohere models on OCI Generative AI require a minimum of 1000 labeled examples for fine-tuning to ensure sufficient signal for learning task-specific patterns without overfitting. This threshold is documented in OCI's fine-tuning requirements and applies to custom classification tasks.

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500

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5000

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100

Question 13

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 14

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

Question 15

A developer is using the OCI Generative AI service API and receives a '400 Bad Request' with error 'Model not found'. What is the most likely cause?

Accepted Answer

The model ID is misspelled or does not exist.. The '400 Bad Request' with 'Model not found' error indicates that the model ID provided in the API request does not match any available model in the OCI Generative AI service. This is most commonly caused by a typo, incorrect model name, or referencing a model that has been deprecated or removed from the service catalog.

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The API request lacks authentication.

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The input exceeds the maximum token limit.

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The endpoint region is incorrect.