1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 2 — 15 Questions

Question 1

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?

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

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.

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 2

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.

Answer

Pre-authenticated request

Answer

OAuth 2.0 client credentials

Answer

Username and password in the header

Question 3

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 4

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.

Question 5

A company has deployed a generative AI endpoint using a custom fine-tuned model. They observe that the endpoint is returning 429 (Too Many Requests) errors during business hours. They need to handle this without losing requests. What should they implement?

Accepted Answer

Implement client-side retry with exponential backoff.. Implementing client-side retry with exponential backoff is the standard approach to handle HTTP 429 (Too Many Requests) errors without losing requests. When the OCI Generative AI endpoint returns a 429 status, the client can automatically retry the request after a delay that increases exponentially, reducing the load on the endpoint while ensuring all requests are eventually processed. This pattern is recommended by OCI and follows best practices for rate-limited APIs, as it allows the system to recover from transient capacity issues without manual intervention.

Answer

Increase the endpoint's max tokens limit.

Answer

Reduce the number of concurrent requests from the application.

Answer

Use a dedicated AI cluster with higher capacity.

Question 6

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

Accepted Answer

Using the OCI Console chat interface. Option A (Using the OCI Console chat interface) is correct because OCI Generative AI provides a built-in chat playground in the OCI Console where users can interactively select a model, submit prompts, and view responses without writing any code, making it a valid consumption method. Option C (Using the OCI Generative AI REST API) is correct because the service exposes REST endpoints (e.g., under the generativeai inference API) that applications can call directly to generate text, chat, or embeddings, which is the foundational programmatic way to consume the models. Options B, D, and E are not among the two marked correct answers here: while the OCI CLI and OCI SDK for Python can wrap the REST API, and OCI Data Flow is a Spark-based processing service rather than a Generative AI consumption interface, they are not the options designated as correct for this question.

Answer

Using the OCI CLI

Answer

Using OCI SDK for Python

Answer

Using OCI Data Flow

Question 7

Which TWO of the following are sources of training data for fine-tuning a model in OCI Generative AI?

Accepted Answer

OCI Object Storage bucket. Option A (OCI Object Storage bucket) is correct because OCI Generative AI fine-tuning jobs read their training datasets (typically JSONL files) directly from an Object Storage bucket in the customer's tenancy, which is the standard supported data source. Option D (Local file uploaded through the OCI Console) is correct because the OCI Console lets you upload a local training file, which is then staged into Object Storage and used as the fine-tuning dataset. OCI File Storage (B) is a shared NFS file system, not an accepted fine-tuning data source, and OCI Database (C) and OCI Streaming (E) are not supported inputs for fine-tuning jobs, since the service expects files in Object Storage rather than a database or a streaming feed.

Answer

OCI File Storage

Answer

OCI Database

Answer

OCI Streaming

Question 8

A company has fine-tuned a custom Llama 3 model using OCI Data Science for a chatbot. They now need a production-grade inference endpoint with auto-scaling. Which OCI service should they use?

Accepted Answer

OCI Generative AI Service. OCI Generative AI Service provides a fully managed, production-grade inference endpoint with built-in auto-scaling for custom models like fine-tuned Llama 3. It abstracts infrastructure management, offers serverless deployment, and integrates with OCI Data Science for model import, making it the ideal choice for a chatbot requiring scalable inference.

Answer

OCI Functions

Answer

OCI Data Science Model Deployment

Answer

OCI Kubernetes Engine (OKE)

Question 9

A developer is deploying a fine-tuned model using OCI Generative AI service. They want to use a custom container image for inference. Which statement is true?

Accepted Answer

You can upload a container image to OCI Container Registry and reference it when creating a dedicated AI cluster.. OCI Generative AI service allows you to bring your own custom container image for inference by uploading it to OCI Container Registry (OCIR) and referencing it when creating a dedicated AI cluster. This enables you to deploy fine-tuned models with custom inference logic, dependencies, or frameworks that are not available in the built-in serving containers.

Answer

Custom containers are only supported with OCI Data Science, not Generative AI.

Answer

Custom containers are supported only for fine-tuning jobs, not inference.

Answer

Custom containers are not supported; only built-in models are available.

Question 10

A company wants to use OCI Generative AI service to generate marketing copy that adheres to brand guidelines. Which technique should they use?

Accepted Answer

Fine-tune the model with brand-specific data. Fine-tuning a pre-trained model with brand-specific data (Option D) is the correct approach because it adjusts the model's weights to align with the company's unique brand guidelines, tone, and vocabulary. This supervised learning process ensures the generated marketing copy consistently adheres to specific requirements, unlike prompt engineering which relies on ephemeral instructions that may not reliably enforce brand constraints.

Answer

Use model distillation

Answer

Use prompt engineering with a pre-trained model

Answer

Use knowledge distillation

Question 11

An organization is deploying multiple generative AI models on a shared dedicated AI cluster. They need to isolate resource usage for each model to avoid interference. Which strategy is recommended?

Accepted Answer

Deploy each model on its own dedicated AI cluster. Deploying each model on its own dedicated AI cluster provides complete hardware-level isolation, ensuring that resource usage (e.g., GPU memory, compute cycles) for one model does not interfere with another. In OCI Generative AI, dedicated AI clusters are single-tenant instances, so each model gets exclusive access to its allocated infrastructure, eliminating contention. This is the recommended strategy for strict isolation in shared environments.

Answer

Use separate fine-tuning jobs for each model

Answer

Configure multiple virtual clusters within the dedicated AI cluster using compartment quotas

Answer

Use OCI Resource Manager to allocate resources

Question 12

A data scientist is preparing to fine-tune a foundation model on OCI. Which two actions should they take to optimize costs? (Select TWO.)

Accepted Answer

Use the smallest model that meets accuracy requirements. Option A is correct because selecting the smallest foundation model that still meets the required accuracy reduces the compute (GPU) resources and time needed for fine-tuning, directly lowering cost on OCI. Option D is correct because monitoring validation loss and stopping training early when it plateaus avoids wasting expensive GPU hours on runs that no longer improve the model. Option B is not appropriate because fine-tuning foundation models requires GPU shapes, not a single-OCPU CPU shape, which would be far too slow or infeasible. Option C is not suitable because OCI spot/preemptible instances can be reclaimed at any time, which risks interrupting long fine-tuning jobs and losing progress. Option E is not ideal because Archive Storage is for rarely accessed data with slow retrieval, whereas active training data must be readily available, and storage is a minor cost compared to GPU compute.

Answer

Use a single OCPU shape to minimize per-hour cost

Answer

Use spot preemptible instances to save on compute

Answer

Store training data in Archive Storage to reduce storage costs

Question 13

An enterprise is deploying a generative AI model that must comply with data residency regulations. Which two configurations should they implement? (Select TWO.)

Accepted Answer

Set up OCI IAM policies to prevent data egress from the region for the model's resources. Option A is correct because OCI IAM policies can be written to deny access or restrict actions on the model's resources to principals within the required region, effectively preventing data egress and enforcing residency boundaries. Option E is correct because deploying the dedicated AI cluster in the specific region that satisfies the data residency requirement ensures the model, its data, and processing remain physically within the compliant jurisdiction. Option B is not required for residency compliance; OCI Logging for API calls supports auditing and monitoring but does not by itself keep data in-region. Option C is wrong because cross-region replication deliberately copies data to another region, which violates data residency constraints. Option D is wrong because storing encryption keys in an OCI Vault in a different region moves sensitive key material outside the required jurisdiction and undermines residency compliance.

Answer

Enable OCI Logging for all API calls

Answer

Use OCI Object Storage with cross-region replication for redundancy

Answer

Store encryption keys in an OCI Vault in a different region

Question 14

Refer to the exhibit. A user receives this error when using the OCI CLI to chat with a model. What is the most likely cause?

Accepted Answer

The user does not have the required IAM policy to invoke the model.. The error occurs because the user lacks the necessary IAM policy to invoke the model. In OCI, even if the model is deployed and the CLI is correctly configured, the IAM policy must grant the user or group the 'inference' permission on the specific model or model family. Without this policy, the OCI CLI returns an authorization error when attempting to chat with the model.

Answer

The model is not deployed.

Answer

The model ID is incorrect.

Answer

The OCI CLI is not configured with the correct region.

Question 15

Refer to the exhibit. A team created this dedicated AI cluster. However, when they try to create a model deployment, the deployment fails with an error indicating insufficient public IPs. What change to the cluster configuration should they make?

Accepted Answer

Change assignPublicIp to true.. The error indicates insufficient public IPs because the cluster's subnet does not have enough available public IP addresses. Setting `assignPublicIp` to `true` in the cluster configuration allows the cluster to automatically allocate public IPs from the subnet's pool, resolving the shortage. This is required for model deployments that need public endpoints.

Answer

Increase the nodeCount to 8.

Answer

Attach a different subnet that has more available public IPs.

Answer

Change the AI cluster shape to VM.GPU.A10.2.