1Z0-1127-25 Practice Exam 2 — 20 Questions

Question 1

A team wants to use OCI Generative AI to generate synthetic data for training a model. They are concerned about the cost of API calls. Which pricing model would be most cost-effective for high-volume batch processing?

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

Accepted Answer

Reserved capacity with a fixed monthly fee. Reserved capacity with a fixed monthly fee provides the lowest per-request cost for high-volume batch processing. OCI Generative AI offers dedicated capacity pricing, which is ideal for predictable, large-scale workloads where you commit to a certain throughput, avoiding per-request charges that would accumulate significantly with high volume.

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OCI Universal Credits with per-request charges

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Monthly subscription with limited requests

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Pay-as-you-go per request

Question 2

A user wants to use OCI Generative AI to generate marketing copy. They want the output to be more creative and varied. Which parameter should they adjust?

Accepted Answer

Increase the temperature parameter.. Increasing the temperature parameter makes the model's output more random and diverse, which is ideal for creative tasks like generating marketing copy. A higher temperature (e.g., 0.7–1.0) increases the probability of sampling less likely tokens, leading to more varied and imaginative text. Setting temperature to 0 would make the output deterministic and repetitive, which is the opposite of what the user wants.

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Set temperature to 0.

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Decrease the temperature parameter.

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Increase the max_tokens parameter.

Question 3

A company is using OCI Generative AI service to generate product descriptions. They notice that the model sometimes generates biased content. Which approach should they take to mitigate bias while maintaining performance?

Accepted Answer

Fine-tune the model with a balanced, curated dataset that reduces bias. Fine-tuning with a balanced, curated dataset directly addresses the root cause of bias by adjusting the model's internal weights to reduce reliance on biased patterns in the original training data. This approach preserves the model's generative performance for product descriptions because it retrains only on domain-specific, unbiased examples, unlike post-processing which merely filters outputs without correcting the underlying model behavior.

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Use a larger model without fine-tuning

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Post-process outputs to remove biased phrases

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Switch to a different pre-built model from OCI

Question 4

Which TWO of the following are valid ways to consume OCI Generative AI models?

Accepted Answer

Using the OCI Console chat interface. A is correct because the OCI Console provides a built-in chat interface that allows users to interact directly with Generative AI models without writing any code. This interface is part of the OCI Generative AI service's web-based console, enabling prompt testing and model evaluation through a graphical user interface.

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Using the OCI CLI

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Using OCI SDK for Python

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Using OCI Data Flow

Question 5

A multi-turn chatbot needs to maintain context across user queries. The context window is limited. What design should be used?

Accepted Answer

Use a summary of previous turns and add new input.. Summarizing previous turns and appending the new input efficiently manages the limited context window of large language models (LLMs). This approach preserves essential conversational context without exceeding token limits, ensuring coherent multi-turn interactions.

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Store context in a separate database and retrieve each time.

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Reset context after each turn.

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Keep the entire conversation history in each request.

Question 6

A company deploys a large language model on a dedicated AI cluster with 4 nodes. The model requires 128 GB of memory per instance, but the nodes have only 64 GB each. During inference, the nodes experience out-of-memory errors. What is the best solution?

Accepted Answer

Enable model parallelism across nodes. Model parallelism splits the model's layers or parameters across multiple nodes, allowing the 128 GB model to be distributed across the 4 nodes (each with 64 GB) so that no single node exceeds its memory capacity. This is the best solution because it directly addresses the memory constraint without requiring additional hardware or sacrificing inference throughput, and it is a standard technique for deploying large language models on distributed AI clusters.

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Increase the number of nodes to 8

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Upgrade to higher memory node shapes

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Reduce the batch size in inference requests

Question 7

A healthcare company is using OCI GenAI to generate patient summaries from clinical notes. The model output sometimes includes hallucinated medical facts, such as incorrect dosages or diagnoses, which could be dangerous. The team needs to improve factual accuracy while maintaining data privacy. They have a large collection of internal medical knowledge bases (clinical guidelines, drug databases) that are stored in OCI Object Storage. The current implementation uses a zero-shot prompt with the base Cohere Command model. The data science team has limited GPU resources and wants to avoid building a complex pipeline. Which course of action best addresses the hallucination problem?

Accepted Answer

Implement a RAG pipeline that retrieves relevant documents from the internal knowledge bases and includes them in the prompt.. A Retrieval-Augmented Generation (RAG) pipeline directly addresses hallucination by grounding the model's output in verified, internal medical knowledge bases stored in OCI Object Storage. This approach retrieves relevant clinical guidelines or drug database entries and includes them in the prompt, providing factual context without requiring fine-tuning or complex GPU-intensive pipelines. It also preserves data privacy by keeping sensitive medical data within OCI and avoids exposing it to external model training.

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Increase the temperature parameter to 0.9 to encourage more deterministic outputs.

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Use prompt engineering to add 'Only provide facts that are absolutely certain.'

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Fine-tune the Cohere model on a publicly available medical dataset like PubMed.

Question 8

A team is deploying a generative AI model using OCI Functions for serverless inference. They are experiencing cold start latency of over 10 seconds for the first invocation after idle periods. What is the best strategy to reduce cold start latency?

Accepted Answer

Use provisioned concurrency to keep a set number of function instances warm.. OCI Functions supports provisioned concurrency, which keeps a specified number of instances warm. Option A (increasing memory) can reduce cold start but not as effectively. Option C (reducing timeout) might cause failures. Option D (using OCI Data Flow) is for data processing, not inference.

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Migrate the inference to OCI Data Flow for better performance.

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Reduce the function timeout to force faster execution.

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Increase the memory allocation for the function.

Question 9

Refer to the exhibit. An administrator receives the error shown when attempting to deploy a custom model. What is the most likely cause?

Accepted Answer

The user or service does not have permission to read the model artifact from Object Storage. The error indicates that the deployment process cannot access the model artifact stored in Object Storage. In OCI Generative AI, the service must have read permission on the bucket and object to download the artifact. If the user or service principal lacks the necessary IAM policy (e.g., `allow service generative-ai to read objects in compartment X where target.bucket.name='Y'`), the deployment fails with this access-denied error.

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The compartment ID is incorrect

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The model artifact file is corrupted

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The dedicated AI cluster ID is invalid

Question 10

A user wants to access the OCI Generative AI service programmatically. Which credential method is recommended for use in a production application running on OCI Compute?

Accepted Answer

Instance principal. Instance principal authentication is the recommended method for production applications running on OCI Compute because it allows the application to authenticate with OCI services without managing or embedding any credentials. The OCI Compute instance assumes a dynamic group and IAM policy that grants it permissions, and the SDK automatically handles token exchange via the instance metadata service, eliminating the need for long-lived secrets.

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API signing keys

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User password and OCID

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Resource principal

Question 11

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.

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Change the model to cohere.command-light.

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Increase the max-tokens value.

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Decrease the temperature to 0.0.

Question 12

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.

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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.

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The temperature setting is too high, causing randomness.

Question 13

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.

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OCI Vault for secrets

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OCI Data Flow for data processing

Question 14

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.

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The inference request batch size is too small

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The model is too large for the cluster memory

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The client-side network is slow

Question 15

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.

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Batch size

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Max_tokens

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Seed

Question 16

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.

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Frequency penalty

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Top-k

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Presence penalty

Question 17

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.

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Use OCI Functions with API Gateway and allow anonymous access

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Deploy in a public subnet with HTTPS and enable OCI Audit

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

Question 18

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.

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Change model to LLAMA.

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Increase maxTokens.

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Set temperature to 0.

Question 19

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.

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The endpoint URL is incorrectly formatted.

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The tenancy is in a different availability domain.

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The model name has a typo.

Question 20

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.

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

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