1Z0-1127-25 Using OCI Generative AI Service Practice Test 6 — 15 Questions

Question 1

A financial services company deployed a fine-tuned model using OCI Generative AI Service to generate investment advice based on quarterly reports. The model was trained on 10,000 labeled examples and achieved high accuracy in testing. However, after three months in production, the model's outputs have become inconsistent and sometimes recommend investments based on outdated market conditions. The team has received multiple complaints from users about inaccurate advice. The model is deployed on a dedicated AI cluster with auto-scaling disabled. The OCI audit logs show no configuration changes. The team suspects data drift and wants to mitigate it without incurring high costs. They have a pipeline that can collect new labeled data monthly, but it takes two weeks to process. What should the team do?

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

Accepted Answer

Set up a monthly retraining schedule using the new labeled data as soon as it is available, and use a champion/challenger deployment to validate the new model before full rollout.. It directly addresses data drift by establishing a regular retraining cycle with the new labeled data, which is the standard mitigation strategy for model degradation over time. The champion/challenger deployment pattern allows the team to validate the updated model's performance against the current production model before full rollout, ensuring no regression in accuracy. This approach balances cost efficiency (monthly retraining) with the operational constraint of a two-week data processing pipeline.

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Decrease the temperature parameter to 0.1 to make outputs more deterministic.

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Revert to the base model (Cohere Command) and use few-shot prompting with recent reports.

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Enable auto-scaling on the dedicated AI cluster to handle increased load.

Question 2

A developer wants to use the OCI Generative AI service to generate text using a Cohere model. Which SDK class should be used for inference calls?

Accepted Answer

GenerativeAiInferenceClient. The correct SDK class for inference calls with Cohere models in OCI Generative AI is `GenerativeAiInferenceClient`. This class is specifically designed to handle inference operations such as text generation, summarization, and chat completions, and it supports both Cohere and Meta Llama models. It provides methods like `generate_text`, `summarize_text`, and `chat` that directly interact with the inference endpoints.

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CustomModelClient

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AiInferenceClient

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GenerativeAiClient

Question 3

After fine-tuning a Cohere Command model on a dataset of customer emails, the model performs well on validation data but poorly on new, unseen emails. Which action is most likely to improve generalization?

Accepted Answer

Expand the training dataset with more diverse examples.. The model is overfitting to the training data, which is a common issue when the dataset lacks diversity. Expanding the training dataset with more diverse examples exposes the model to a wider range of patterns and variations, reducing overfitting and improving generalization to unseen customer emails. In the context of Cohere Command models, this aligns with best practices for fine-tuning on OCI Generative AI Service, where data quality and diversity are critical for robust performance.

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Increase the number of fine-tuning epochs.

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Reduce the number of layers being fine-tuned.

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Switch to a smaller model variant such as Cohere Light.

Question 4

A data scientist receives an error when calling the embed_text API: "InvalidRequest: input too long". What is the most likely cause and solution?

Accepted Answer

The input text exceeds the maximum token limit for the model; truncate the input.. The error 'InvalidRequest: input too long' indicates that the input text exceeds the maximum token limit for the embedding model. OCI Generative AI embedding models, like all transformer-based models, have a fixed context window (e.g., 512 or 1024 tokens). The solution is to truncate the input to fit within that limit, as the API will reject overly long inputs.

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The model specified is not supported for embeddings; use a different model.

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The API request rate exceeds the tenancy limit; reduce the request rate.

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The API key is invalid or expired; regenerate the key.

Question 5

A developer needs to integrate OCI Generative AI into a Python application. Which SDK should they use?

Accepted Answer

OCI Python SDK. The OCI Python SDK (Option B) is the correct choice because it provides the official set of libraries and tools for interacting with Oracle Cloud Infrastructure services, including the Generative AI service. This SDK handles authentication, request signing, and API calls specific to OCI, enabling seamless integration of OCI Generative AI into Python applications.

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Boto3

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Google Cloud client

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OpenAI library

Question 6

A data scientist fine-tuned a model on OCI Gen AI using a dedicated AI cluster. After deployment, the model gives inaccurate results. Which troubleshooting step should they take first?

Accepted Answer

Check the training data for bias or quality issues. The most common cause of inaccurate results after fine-tuning is poor training data quality or bias. Before adjusting infrastructure or switching models, you must validate the dataset for issues like label errors, class imbalance, or sampling bias, as these directly degrade model performance regardless of compute resources.

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Switch to a different base model

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Increase the cluster size

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Use a serverless endpoint

Question 7

Refer to the exhibit. A user runs this command and gets an error: 'InvalidParameter: unit-shape 'GPU.1' is not supported in this region. Supported shapes: GPU.2, GPU.4'. What should the user do?

Accepted Answer

Use unit-shape GPU.2. The error message explicitly states that 'GPU.1' is not supported in the current region and lists 'GPU.2' and 'GPU.4' as supported shapes. Option A correctly instructs the user to use a supported shape, such as GPU.2, which resolves the invalid parameter error without changing other configurations.

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Increase unit count

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Change compartment

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Use a different region

Question 8

A company requires a generative AI service to automatically summarize customer support transcripts. Which OCI Generative AI model is most suitable for this task?

Accepted Answer

Cohere Command. Cohere Command is a large language model specifically designed for text generation tasks such as summarization, making it the most suitable choice for automatically summarizing customer support transcripts. Unlike embedding models or base Llama variants, Command is optimized for instruction-following and generating coherent, concise summaries from conversational data.

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Llama 3 70B

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Cohere Embed

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Fine-tuned Llama 2

Question 9

Users report that inference requests to the OCI Generative AI service are taking longer than expected. The application uses the on-demand endpoint. What is the most likely cause of the increased latency?

Accepted Answer

The on-demand endpoint experiences shared resource contention.. The on-demand endpoint in OCI Generative AI uses a shared infrastructure pool where compute resources are allocated dynamically. When multiple users or applications send concurrent inference requests, resource contention occurs, leading to queuing delays and increased latency. This is the most likely cause because the question specifies that users are experiencing slower-than-expected responses, and shared resource contention is a known behavior of on-demand endpoints.

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The inference model is not fine-tuned for the use case.

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The selected model is too large for the use case.

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The API request timeout is set too low.

Question 10

A data scientist wants to fine-tune a generative AI model on proprietary customer data. What is a best practice for preparing the training dataset?

Accepted Answer

Curate a dataset of domain-specific examples with clear input-output pairs.. Fine-tuning a generative AI model on proprietary data requires a curated, domain-specific dataset with clear input-output pairs. This ensures the model learns the desired task (e.g., summarization, classification) without introducing noise or irrelevant patterns, which is critical for OCI Generative AI Service fine-tuning where data quality directly impacts model performance.

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Randomly sample 1000 records from production logs.

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Use the same dataset as the base model's pre-training data.

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Use the largest available public dataset from the internet.

Question 11

A retail company wants to generate product descriptions from attribute data. They have no prior AI experience. Which approach is most appropriate?

Accepted Answer

Use the Cohere Command model with carefully crafted prompts.. The Cohere Command model is specifically designed for text generation tasks like creating product descriptions from attribute data. With no prior AI experience, using a pre-trained model with carefully crafted prompts is the most efficient and accessible approach, requiring no custom training or complex infrastructure.

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Train a custom model from scratch.

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Fine-tune a model on a synthetic dataset.

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Use the Cohere Embed model to generate embeddings and then decode.

Question 12

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?

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 13

A company is deploying a customer-facing chatbot using OCI Generative AI. They need to prevent the model from generating offensive or harmful content. Which feature should they implement?

Accepted Answer

Enable OCI Generative AI guardrails with content filtering.. OCI Generative AI guardrails provide built-in content filtering that automatically detects and blocks offensive or harmful content in both user inputs and model outputs. This is the recommended, native feature for safety compliance, eliminating the need for custom post-processing and ensuring consistent policy enforcement across all chatbot interactions.

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Custom post-processing to scan each response.

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Limit the user input length.

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Use a smaller model that is less capable of generating harm.

Question 14

Which TWO are benefits of using dedicated AI clusters for OCI Generative AI?

Accepted Answer

Guaranteed throughput. Option B (Guaranteed throughput) is correct because dedicated AI clusters on OCI reserve compute capacity exclusively for a tenant, so the provisioned throughput is not shared with or throttled by other on-demand users. Option E (Predictable inference latency) is correct because that same reserved, isolated capacity avoids the noisy-neighbor contention typical of shared on-demand endpoints, yielding consistent response times. The unmarked options do not belong: automatic model updates (A) are not a property of dedicated clusters, since model versioning is controlled by the customer; lower cost than on-demand for all workloads (C) is false because dedicated capacity is only cheaper for sustained, high-utilization workloads and more expensive for intermittent ones; and no need to manage scaling (D) is incorrect because dedicated clusters still require capacity planning and scaling decisions by the customer.

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Automatic model updates

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Lower cost than on-demand for all workloads

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No need to manage scaling

Question 15

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

Accepted Answer

Llama 3. Llama 3 (A) is correct because OCI Generative AI offers Meta's Llama family, including Llama 3, as pre-trained and fine-tunable chat models in the playground and API. Cohere Command (C) is correct because Cohere's Command models (e.g., Command R/R+) are among the foundational text-generation models hosted by the OCI Generative AI service. Cohere Embed (E) is correct because Cohere's Embed models for generating text embeddings are also provided by the service for semantic search and RAG use cases. GPT-4 (B) is not offered by OCI Generative AI, as it is an OpenAI model accessed through Azure OpenAI or OpenAI's own APIs. Stable Diffusion (D) is an image-generation model and is not part of the OCI Generative AI text/embedding model catalog.

Answer

GPT-4

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Stable Diffusion