1Z0-1127-25 Practice Test 4 — 10 Questions

Question 1

Refer to the exhibit. A developer encounters this error. Which action should they take to resolve the issue?

Accepted Answer

Wait and retry after some time.. The error indicates a rate limit or throttling issue, typically returned by the OCI Generative AI service when the API request quota is exceeded. Waiting and retrying after the cooldown period allows the rate limit to reset, which is the correct resolution for transient throttling errors.

Answer

Change the model to cohere.command-light.

Answer

Increase the max-tokens value.

Answer

Decrease the temperature to 0.0.

Question 2

A company is deploying a chatbot powered by OCI Generative AI. They want to inject the conversation history into the model prompt to maintain context. However, they notice that after a long conversation, the model starts to ignore earlier messages. What is the most likely cause?

Accepted Answer

The model has a limited context window size.. The model's context window size limits the total number of tokens (input + output) it can process at once. When the conversation history grows beyond this limit, older messages are truncated or dropped, causing the model to lose context from earlier parts of the conversation. This is a fundamental constraint of transformer-based models like those used in OCI Generative AI.

Answer

The model's max_tokens limit is too low, truncating the prompt.

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The top_p parameter is set to 1, causing deterministic output.

Answer

The temperature setting is too high, causing randomness.

Question 3

Which THREE components are essential for a production-grade generative AI deployment on OCI? (Select THREE)

Accepted Answer

OCI Logging for audit. A is correct because OCI Logging provides centralized audit logging for all API calls and resource changes in the generative AI deployment. This is essential for compliance, security monitoring, and troubleshooting in a production environment, as it captures detailed logs of model invocations, data access, and configuration changes.

Answer

OCI Vault for secrets

Answer

OCI Data Flow for data processing

Question 4

During deployment of a generative AI model, the inference endpoint returns high latency and timeouts. The model is deployed on a dedicated AI cluster with multiple nodes. What is the most likely cause?

Accepted Answer

The cluster nodes are configured with insufficient parallelism or the model is not properly parallelized across nodes. High latency and timeouts in a distributed AI inference deployment typically indicate that the model workload is not efficiently distributed across the cluster nodes. Option C is correct because insufficient parallelism—either due to misconfigured node resources (e.g., insufficient vCPUs, GPU cores, or memory bandwidth) or improper model sharding/parallelization—causes some nodes to become bottlenecks while others remain underutilized, leading to queuing delays and eventual timeouts.

Answer

The inference request batch size is too small

Answer

The model is too large for the cluster memory

Answer

The client-side network is slow

Question 5

Which TWO factors most directly impact the consistency of text generated by an LLM when the same prompt is used multiple times?

Accepted Answer

Top_p. Top_p (nucleus sampling) directly impacts consistency by controlling the cumulative probability threshold for token selection. A lower Top_p (e.g., 0.1) restricts the model to only the most probable tokens, reducing randomness and making outputs more deterministic across repeated prompts. This parameter, along with Temperature, is a primary lever for managing output variability in LLMs.

Answer

Batch size

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Max_tokens

Answer

Seed

Question 6

A company wants to use OCI Generative AI to summarize customer reviews. Which model parameter should be adjusted to control the creativity of the summary?

Accepted Answer

Temperature. Temperature controls the randomness of token selection in the model's output distribution. A higher temperature (e.g., 0.9) makes the summary more creative and diverse, while a lower temperature (e.g., 0.1) makes it more deterministic and focused. For summarizing customer reviews, adjusting temperature directly influences how novel or conservative the generated text will be.

Answer

Frequency penalty

Answer

Top-k

Answer

Presence penalty

Question 7

Your team is deploying a generative AI model for a clinical decision support system. The model must meet HIPAA compliance requirements. You have trained a model using OCI Data Science and now need to deploy it so that patient data is protected. The application requires real-time inference. Which set of actions should you take to ensure compliance while maintaining low latency?

Accepted Answer

Deploy in a private VCN subnet, use a service gateway, store keys in OCI Vault, and enable OCI Logging and OCI Audit. Deploying the model in a private VCN subnet ensures the inference endpoint is not exposed to the internet, meeting HIPAA's requirement for network isolation. Using a service gateway allows private connectivity to OCI services without traversing the internet, while storing encryption keys in OCI Vault enables customer-managed key control for data at rest. Enabling OCI Logging and OCI Audit provides the necessary audit trail for compliance, and the private subnet with service gateway keeps latency low by avoiding internet hops.

Answer

Use OCI Functions with API Gateway and allow anonymous access

Answer

Deploy in a public subnet with HTTPS and enable OCI Audit

Answer

Use OCI Data Flow for batch inference and store results in Object Storage with SSE

Question 8

A developer runs an OCI GenAI chat request with system prompt "You are a sarcastic assistant." The output is offensive. How can the developer enforce safety policies?

Accepted Answer

Use the OCI GenAI content moderation filter.. The OCI GenAI content moderation filter is specifically designed to enforce safety policies by detecting and blocking offensive, harmful, or policy-violating content in both input prompts and model outputs. By enabling this filter, the developer can prevent the model from generating offensive responses even when a system prompt like 'You are a sarcastic assistant' encourages undesirable behavior.

Answer

Change model to LLAMA.

Answer

Increase maxTokens.

Answer

Set temperature to 0.

Question 9

A developer is testing a RAG application using OCI Generative AI. They receive an error: 'The model cohere.command-r-plus-v1:0 is not supported in this region.' What is the most likely cause?

Accepted Answer

The model is not available in the selected OCI region.. The error message explicitly states that the model 'cohere.command-r-plus-v1:0' is not supported in the region. OCI Generative AI models are region-specific; each model is deployed only in certain OCI regions (e.g., us-ashburn-1, eu-frankfurt-1). If the selected region does not host that model, the API returns this error regardless of endpoint formatting, tenancy configuration, or model name spelling.

Answer

The endpoint URL is incorrectly formatted.

Answer

The tenancy is in a different availability domain.

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The model name has a typo.

Question 10

A financial institution uses OCI GenAI to power a customer support chatbot. The compliance team requires that responses are strictly consistent with regulatory guidelines and approved responses. The company has a curated set of question-answer pairs that cover common scenarios. They want to ensure that the chatbot never deviates from these approved answers. The data science team is considering various approaches to enforce this consistency. Which approach is most effective?

Accepted Answer

Fine-tuning the model on the curated dataset of question-answer pairs.. Fine-tuning the model on the curated dataset of approved responses teaches the model to output similar responses for related questions, ensuring consistency. Option A is wrong because few-shot prompting may fail for unseen variations and does not guarantee strict adherence. Option C is wrong because using a large context window does not enforce specific content. Option D is wrong because setting a low temperature reduces randomness but does not guarantee the model will choose approved responses.

Answer

Few-shot prompting with three example responses in every query.

Answer

Using a large context window to include all regulatory guidelines in the prompt.

Answer

Setting a low temperature (0.1) to make outputs deterministic.