1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 3 — 15 Questions

Question 1

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?

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

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 2

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 3

An organization is deploying a generative AI model that requires GPU acceleration for inference. They are using OCI Data Science Model Deployment. The model is expected to handle variable traffic, with occasional spikes. Which scaling option should they configure to ensure cost-efficiency and responsiveness?

Accepted Answer

Configure autoscaling with a minimum of 1 and maximum of 10 GPU instances. Autoscaling with a minimum of 1 and maximum of 10 GPU instances allows the deployment to dynamically adjust capacity in response to variable traffic and spikes, ensuring cost-efficiency by scaling down during low demand and responsiveness by scaling up during peaks. OCI Data Science Model Deployment supports autoscaling policies that can be configured with GPU shapes, making it the optimal choice for a generative AI model requiring GPU acceleration.

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Use OCI Generative AI on-demand API with a serverless endpoint

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Use CPU-only instances and rely on batching

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Deploy with a fixed number of 1 GPU instance

Question 4

Which TWO factors should be considered when selecting a base model for fine-tuning on OCI Generative AI service?

Accepted Answer

The model's size and number of parameters. When selecting a base model for fine-tuning on OCI Generative AI service, the model's size and number of parameters (B) directly impact computational cost, training time, and the model's capacity to learn from your dataset. The model's license and terms of use (C) are critical because commercial use, redistribution, and fine-tuning rights vary per model (e.g., Llama 2 vs. GPT-based models), and violating these can lead to legal or compliance issues.

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The model's training dataset size

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The model's training framework (PyTorch vs TensorFlow)

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The model's built-in features like content filtering

Question 5

Which TWO methods can be used to invoke a generative AI model deployed on OCI?

Accepted Answer

Sending HTTP requests to the model endpoint URL. Generative AI models deployed on OCI expose a RESTful endpoint that accepts HTTP requests (typically POST with JSON payloads) for inference. This is the standard method for programmatic access to the model, allowing integration with any HTTP client.

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Using OCI Notifications service

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Using the OCI Console web interface

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Using OCI Events service

Question 6

Which THREE steps are required to deploy a custom generative AI model using OCI Data Science Model Deployment?

Accepted Answer

Create a model artifact (e.g., pickle, ONNX) with inference code. Deploying a custom generative AI model via OCI Data Science Model Deployment requires packaging the model and its inference code into a standardized artifact format (e.g., pickle, ONNX). This artifact is the core input that the deployment runtime loads to serve predictions, making it an essential step in the workflow.

Answer

Fine-tune the model using OCI Generative AI service

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Register the model in OCI Generative AI service

Question 7

Given the CLI output from `oci generative-ai model list`, what can be determined about the model 'my-fine-tuned-model'?

Accepted Answer

It was created by fine-tuning an existing base model. The CLI output from `oci generative-ai model list` includes a model named 'my-fine-tuned-model'. In OCI Generative AI, models listed with custom names that are not part of the base model catalog (e.g., cohere.command, meta.llama) indicate they were created by fine-tuning a base model using your own dataset. The presence of a custom name without a base model prefix confirms it is a fine-tuned model, not a pre-built one.

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It is a pre-built model provided by OCI

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It has been deployed to an endpoint

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It is currently being trained

Question 8

An administrator created the above IAM policies. A member of the GenerativeAIAdmins group reports they cannot invoke the model endpoint. Which permission is missing?

Accepted Answer

Permission to use or manage generative-ai-endpoint. The error occurs because the IAM policy grants permissions for 'generative-ai-model' but not for 'generative-ai-endpoint'. Invoking a model endpoint requires the 'use' or 'manage' permission on the 'generative-ai-endpoint' resource type, as the endpoint is the runtime interface that handles inference requests. Without this permission, the API call to the endpoint is denied, even if the user has access to the underlying model.

Answer

Permission to access the compartment

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Permission to manage generative-ai-model

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Permission to read the model's training data

Question 9

A developer receives the above error when trying to send a request to a model endpoint. What is the most likely reason?

Accepted Answer

The endpoint was deleted by an administrator. The error message indicates that the model endpoint is not found. In OCI Generative AI, when an administrator deletes an endpoint, subsequent requests to that endpoint's URL return a 404 Not Found error. This is the most likely reason because the endpoint resource no longer exists in the tenancy, and the request cannot be routed to any model.

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The network connection to OCI is down

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The API key is invalid

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The model is still being deployed

Question 10

A company is deploying a fine-tuned Cohere model on OCI Generative AI service for real-time inference. They need to ensure low latency even during demand spikes. Which configuration should they prioritize?

Accepted Answer

Use a dedicated AI cluster for the endpoint.. A dedicated AI cluster provides isolated compute resources (GPUs) that are not shared with other tenants or workloads, ensuring consistent low latency even under demand spikes. This is critical for real-time inference because shared endpoints can experience resource contention and throttling during high traffic, while a dedicated cluster guarantees predictable performance.

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Enable model caching on the endpoint.

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Use streaming responses.

Answer

Increase the max tokens parameter.

Question 11

A data scientist needs to generate vector embeddings for a large corpus of text documents to use in a semantic search application. Which OCI service is best suited for this task?

Accepted Answer

OCI Generative AI. OCI Generative AI is the correct choice because it provides a managed service for generating vector embeddings from text using large language models (LLMs) like Cohere. This service is specifically designed for tasks such as semantic search, where embeddings capture the meaning of text to enable similarity comparisons. OCI Vision, Speech, and Language focus on other modalities (images, audio, and NLP tasks like sentiment analysis) and do not offer embedding generation for semantic search.

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OCI Vision

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OCI Speech

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OCI Language

Question 12

An organization is fine-tuning a large language model on OCI Data Science. They must ensure that the training data remains within a specific geographic region and is encrypted at rest. Which combination of resources should they use?

Accepted Answer

OCI Object Storage bucket with a bucket policy and default encryption, created in the required region.. OCI Object Storage with default encryption ensures data is encrypted at rest using AES-256, and a bucket policy can enforce that data remains within a specific geographic region by restricting cross-region replication or access. This combination directly meets the requirements of regional data residency and encryption at rest for training data used in OCI Data Science.

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OCI Database with Transparent Data Encryption, storing the training data in tables.

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OCI File Storage with export options and encryption, mounted to the Data Science session.

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OCI Block Volume with encryption, attached to the Data Science notebook session.

Question 13

A company has deployed a generative AI model endpoint on OCI. They want to monitor token usage and latency for cost optimization. Which OCI service should they use to collect these metrics?

Accepted Answer

OCI Monitoring. A is correct because OCI Monitoring is the native telemetry service that collects and stores metrics such as token usage (e.g., input/output token counts) and latency (e.g., model inference latency) from OCI Generative AI endpoints. These metrics are automatically emitted by the OCI Generative AI service and can be queried via the Monitoring API or visualized in the Console, enabling cost optimization by tracking consumption patterns.

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OCI Events

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OCI Notifications

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OCI Logging

Question 14

A developer wants to integrate OCI Generative AI into a web application. Which API authentication method is recommended for programmatic access?

Accepted Answer

API key-based signing. OCI APIs require request signing using an API signing key (an RSA key pair) for programmatic access. The developer must generate a key pair, upload the public key to the OCI console, and then use the private key to sign each HTTP request using the OCI Signature Version 1 algorithm (based on RFC 2104 HMAC-SHA256). This ensures authentication without transmitting secrets over the wire.

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Pre-authenticated request

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OAuth 2.0 client credentials

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Username and password in the header

Question 15

A data science team is using OCI Data Science to fine-tune a model. They notice that training jobs are failing due to out-of-memory errors on the notebook session. What should they do to resolve this?

Accepted Answer

Switch to a larger notebook session shape.. Out-of-memory errors during training on a notebook session indicate that the current shape's memory capacity is insufficient for the model or data being processed. Switching to a larger notebook session shape directly increases available RAM and compute resources, resolving the memory constraint without altering the training logic or infrastructure type.

Answer

Enable autoscaling on the notebook session.

Answer

Use OCI Data Flow instead.

Answer

Reduce the batch size in the training script.