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

Question 1

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.

Answer

API signing keys

Answer

User password and OCID

Answer

Resource principal

Question 2

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. Option C is correct because 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.

Answer

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

Answer

Use CPU-only instances and rely on batching

Answer

Deploy with a fixed number of 1 GPU instance

Question 3

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.

Answer

The model's training dataset size

Answer

The model's training framework (PyTorch vs TensorFlow)

Answer

The model's built-in features like content filtering

Question 4

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. Option C is correct because 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.

Answer

Using OCI Notifications service

Answer

Using the OCI Console web interface

Answer

Using OCI Events service

Question 5

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. Option B is correct because 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

Answer

Register the model in OCI Generative AI service

Question 6

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.

Answer

It is a pre-built model provided by OCI

Answer

It has been deployed to an endpoint

Answer

It is currently being trained

Question 7

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

Answer

Permission to manage generative-ai-model

Answer

Permission to read the model's training data

Question 8

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.

Answer

The network connection to OCI is down

Answer

The API key is invalid

Answer

The model is still being deployed

Question 9

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.

Answer

Enable model caching on the endpoint.

Answer

Use streaming responses.

Answer

Increase the max tokens parameter.

Question 10

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.

Answer

OCI Vision

Answer

OCI Speech

Answer

OCI Language

Question 11

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.. Option A is correct because 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.

Answer

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

Answer

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

Answer

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

Question 12

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.

Answer

OCI Events

Answer

OCI Notifications

Answer

OCI Logging

Question 13

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. Option B is correct because 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.

Answer

Pre-authenticated request

Answer

OAuth 2.0 client credentials

Answer

Username and password in the header

Question 14

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.

Question 15

A company wants to use OCI Generative AI to summarize customer support tickets. They need to ensure that the model does not output any sensitive information. Which technique should they implement?

Accepted Answer

Prompt engineering to instruct the model to exclude sensitive information.. Prompt engineering is the correct technique because it allows the company to explicitly instruct the generative AI model to exclude sensitive information from its outputs. By crafting a system prompt or user prompt with specific directives (e.g., 'Do not include any personally identifiable information, account numbers, or confidential data in your summary'), the model's behavior is directly controlled at inference time. This is a lightweight, flexible approach that does not require changing the model architecture or endpoint configuration, and it is the most direct way to enforce output constraints in OCI Generative AI.

Answer

Use a smaller model that is less likely to memorize data.

Answer

Enable content filtering on the endpoint.

Answer

Disable the use of training data in the endpoint configuration.