1Z0-1127 Timed Practice Test 5 — 10 Questions

Question 1

An organization is concerned about the safety of generated content. Which OCI feature allows them to define custom policies to block inappropriate outputs?

Accepted Answer

Content filtering and safety controls in Generative AI. Option B is correct because OCI Generative AI includes built-in content filtering and safety controls that allow organizations to define custom policies to block inappropriate or harmful outputs. These controls operate at the model inference layer, enabling fine-grained filtering based on categories such as toxicity, hate speech, or personally identifiable information (PII). This directly addresses the concern about generated content safety.

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OCI IAM policies

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OCI Audit logs

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OCI Vault

Question 2

During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?

Accepted Answer

Top_p = 0.9, frequency_penalty = 0.5. Option B is correct because a high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.

Answer

Top_p = 0.1, frequency_penalty = 0.5

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Top_p = 0.9, frequency_penalty = 0.0

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Top_p = 1.0, frequency_penalty = 0.0

Question 3

During multi-turn conversation with an OCI GenAI model, the model repeats user messages from earlier turns. What is the most likely cause?

Accepted Answer

Low presence penalty. A low presence penalty reduces the model's incentive to avoid repeating previously mentioned content. In multi-turn conversations, this can cause the model to echo user messages from earlier turns because the penalty is too weak to discourage repetition of tokens that have already appeared in the context window.

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Low top-p

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High temperature

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High frequency penalty

Question 4

During fine-tuning of a Cohere model on OCI Data Science, the loss curve shows a sharp spike after epoch 3. What is the most appropriate action?

Accepted Answer

Gradient clipping.. A sharp spike in the loss curve after epoch 3 during fine-tuning indicates a gradient explosion, where the gradients become excessively large and destabilize the model's weights. Gradient clipping is the most appropriate action because it directly caps the gradient norm (e.g., using `max_grad_norm=1.0` in Cohere's fine-tuning API) to prevent these spikes, ensuring stable training without altering the learning dynamics.

Answer

Reduce learning rate.

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Add more training data.

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Increase batch size.

Question 5

A data scientist wants to improve the accuracy of a summarization model on medical texts. Which OCI service feature is most suitable?

Accepted Answer

OCI Generative AI fine-tuning. C is correct because OCI Generative AI fine-tuning allows a data scientist to adapt a pre-trained large language model (LLM) specifically for medical text summarization by training it on domain-specific data. This improves accuracy by aligning the model's outputs with the terminology, context, and nuances of medical literature, which generic models may not capture well.

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OCI Data Flow

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OCI Language service

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OCI Anomaly Detection

Question 6

What is the role of the softmax function in the output layer of an LLM?

Accepted Answer

Convert logits to probabilities. The softmax function in the output layer of an LLM converts the raw, unnormalized scores (logits) produced by the final linear layer into a probability distribution over the vocabulary. This allows the model to output a valid probability for each token, where all probabilities sum to 1, enabling sampling or greedy decoding for next-token prediction.

Answer

Apply attention

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Tokenize input

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Compute gradients

Question 7

A company uses OCI Generative AI to power a chatbot for customer support. They notice that the model's responses sometimes contain factual inaccuracies. Which strategy would best reduce hallucination?

Accepted Answer

Implementing Retrieval-Augmented Generation (RAG).. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved factual information, directly reducing hallucination. Increasing temperature increases randomness, fine-tuning on a larger corpus may not fix factual accuracy, and reducing max tokens does not affect correctness.

Answer

Increasing the temperature parameter.

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Reducing the max token limit.

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Fine-tuning the model on a larger general corpus.

Question 8

A developer uses OCI Generative AI's chat endpoint with a system message placed after user messages. The model ignores the system message. What is the most likely reason?

Accepted Answer

The system message is placed after user messages. In OCI Generative AI's chat endpoint, the system message must be placed before user messages to establish the model's behavior and context. When placed after user messages, the model treats it as part of the conversation history rather than a directive, causing it to be ignored. This ordering is a fundamental requirement for the chat API's message structure.

Answer

The system message is too long

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Temperature is set too high

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The model has not been fine-tuned for instruction following

Question 9

An architect is designing a multi-tenant application using OCI Generative AI. Each tenant has custom instructions and data. To minimize cost while maintaining isolation, which deployment approach is recommended?

Accepted Answer

Shared base model with per-tenant system prompts and retrieval.. A shared base model with per-tenant customization via system prompts and retrieval (RAG) is cost-effective and provides isolation through prompt engineering and data segregation. Dedicated fine-tuned endpoints are expensive, a single large model with conditional logic risks prompt injection, and on-premises deployment may not be feasible or scalable.

Answer

Dedicated fine-tuned endpoint per tenant.

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On-premises deployment of open-source models.

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Single large fine-tuned model with conditional logic.

Question 10

A development team wants to generate code snippets from natural language. Which model strategy should they adopt?

Accepted Answer

Use a code-specific model like Code Llama.. Code Llama is a specialized variant of Llama 2 that has been fine-tuned on code datasets, enabling it to generate syntactically and semantically correct code from natural language prompts. This makes it the optimal choice for code generation tasks, as general-purpose LLMs lack the targeted training on code structures and programming languages.

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Use a general-purpose LLM like Llama 2.

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Use a multimodal model.

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Use an embedding model for text.