1Z0-1127-25 Deploying and Managing Generative AI on OCI — 40 Questions

Question 1

A data scientist is fine-tuning a generative AI model on OCI Data Science using a custom container with GPU resources. The training job fails with an out-of-memory error despite the GPU instance having sufficient memory. The job works fine on a smaller dataset. What is the most likely cause?

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

Accepted Answer

The batch size is too large for the GPU memory. The most likely cause is that the batch size is too large for the GPU memory. Even though the GPU instance has sufficient total memory, a batch size that exceeds the available GPU memory (after accounting for model parameters, gradients, and optimizer states) will trigger an out-of-memory (OOM) error. Reducing the batch size allows the model to fit within the GPU's memory limits, which explains why the job works on a smaller dataset but fails on a larger one.

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The training script has a memory leak

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The GPU instance is not supported by OCI Data Science

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The model is not compatible with the PyTorch version

Question 2

An organization wants to deploy a generative AI chatbot using OCI Generative AI service. The chatbot must comply with data residency requirements by ensuring that all data processing occurs within a specific geographic region. What is the best practice to achieve this?

Accepted Answer

Use a dedicated AI cluster in the required region. OCI Generative AI service allows you to provision a dedicated AI cluster within a specific region, ensuring all model inference and data processing remain within that geographic boundary. This dedicated cluster is isolated from other regions and complies with data residency requirements by design, as no data leaves the chosen region during processing.

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Enable cross-region replication for disaster recovery

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Configure a tenancy-wide policy to restrict region usage

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Use IAM policies to block access from other regions

Question 3

A team has deployed a generative AI model using OCI Data Science model deployment. The endpoint is behind a load balancer. Users report that after 5 minutes of inactivity, the first request takes over 30 seconds to respond, while subsequent requests are fast. What is the most likely cause and solution?

Accepted Answer

The model deployment has an idle timeout that scales down to zero; configure a minimum number of instances or use a warm-up request. The described behavior—first request after 5 minutes of inactivity taking over 30 seconds, with subsequent requests fast—is a classic symptom of an idle timeout that scales the model deployment to zero instances. OCI Data Science model deployments support auto-scaling with an idle timeout (default 5 minutes) that can reduce the number of instances to zero when no requests are received. When a new request arrives, it must wait for a new instance to spin up, causing the delay. The solution is to configure a minimum number of instances (e.g., 1) to keep the model warm, or use a warm-up request to prevent the idle timeout from triggering.

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The load balancer is scaling based on CPU utilization; increase the CPU threshold

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The VCN has a network latency issue; use a different availability domain

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The inference code has a lazy initialization; pre-load the model in the deployment script

Question 4

A machine learning engineer is deploying a fine-tuned Llama 2 model on OCI Data Science model deployment. The deployment fails with an error: 'Model artifact exceeds the maximum allowed size of 10 GB.' The model files total 12 GB. What is the best approach to resolve this?

Accepted Answer

Store the model in Object Storage and reference it in the deployment configuration. OCI Data Science model deployment has a hard limit of 10 GB for the model artifact uploaded directly. By storing the model in Object Storage and referencing it in the deployment configuration, you bypass this limit entirely, as the deployment service can load the model from Object Storage at runtime without requiring the artifact to be part of the deployment package.

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Use a different model that is smaller than 10 GB

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Increase the model deployment artifact size limit via a service request

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Compress the model artifact to under 10 GB using gzip

Question 5

Which TWO actions are recommended best practices for managing costs when using OCI Generative AI dedicated AI clusters?

Accepted Answer

Use preemptible instances for non-critical inference workloads. Option B is correct because preemptible instances cost significantly less than on-demand capacity and are well suited to non-critical inference workloads that can tolerate interruption, helping reduce overall spend on dedicated AI clusters. Option C is correct because autoscaling dynamically adjusts the number of nodes in the dedicated AI cluster to match actual demand, so you avoid paying for idle capacity during low-traffic periods while still scaling up when needed. Option A is not recommended because provisioning a fixed number of nodes sized for peak load means paying for maximum capacity even when utilization is low. Option D is not a valid cost practice for dedicated AI clusters, since clusters are provisioned capacity rather than a resource you simply stop and start like a compute instance. Option E is incorrect because preemptible instances are a lower-cost option, and pay-as-you-go billing is not a substitute that reduces dedicated AI cluster costs in the way described.

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Provision a fixed number of nodes to handle peak load

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Stop the dedicated AI cluster when not in use

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Use pay-as-you-go billing instead of preemptible instances

Question 6

An organization wants to fine-tune a large language model on OCI using their proprietary data. They are concerned about data privacy and want to ensure that fine-tuning data does not leave the OCI region. Which OCI service should they use to securely store and manage their training data?

Accepted Answer

OCI Object Storage. C is correct because OCI Object Storage is a regional service that stores data within a specific OCI region, ensuring that fine-tuning data does not leave that region. It provides secure, durable, and scalable storage for large datasets, such as training data for LLMs, with encryption at rest and in transit, and supports direct integration with OCI Data Science and Generative AI services for fine-tuning workflows.

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OCI Block Volume

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OCI File Storage

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Oracle Autonomous Database

Question 7

A data scientist is deploying a custom generative AI model using OCI Data Science. After deploying the model to an endpoint, they notice that inference requests are failing with a timeout error when the payload size exceeds 1 MB. What is the most likely cause and solution?

Accepted Answer

The model deployment has a default payload size limit of ~1 MB; increase the payload limit in the deployment configuration.. OCI Data Science model deployments have a default payload size limit of approximately 1 MB. When inference requests exceed this limit, the load balancer or gateway times out the request. The solution is to increase the payload limit in the deployment configuration, which can be adjusted via the OCI console or API by modifying the `maximumRequestPayloadSize` setting.

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The load balancer is misconfigured; reconfigure the load balancer timeout settings.

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The model server lacks sufficient memory; scale out to more instances.

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The model is not optimized for large payloads; use AutoML to optimize the model.

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.. Provisioned concurrency in OCI Functions pre-initializes a specified number of function instances and keeps them warm, so the first invocation after an idle period does not trigger a cold start. This directly eliminates the container initialization and runtime bootstrap overhead that causes the 10+ second latency. It is the only option that addresses the root cause of cold starts rather than attempting to mask or shift the problem.

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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

A company is deploying a large generative AI model on OCI using GPU compute instances. They want to optimize inference cost while maintaining acceptable latency. Which TWO strategies should they implement?

Accepted Answer

Select the smallest GPU instance type that meets latency requirements.. Option B is correct because right-sizing the GPU instance to the smallest type that still satisfies the latency SLA avoids paying for excess GPU capacity, directly reducing inference cost while keeping acceptable latency. Option E is correct because an inference endpoint with auto-scaling dynamically adjusts the number of replicas to match actual demand, so you avoid over-provisioning during low traffic and scale up only when needed, optimizing cost without violating latency targets. Option A is not appropriate because provisioned concurrency keeps capacity always warm and billed even when idle, which raises cost rather than optimizing it. Option C is wrong because increasing max-tokens produces longer outputs, consuming more compute and memory bandwidth per request and increasing latency and cost. Option D is wrong because deploying on multiple large GPU instances over-provisions fixed capacity for peak load, wasting money during non-peak periods instead of scaling elastically.

Answer

Enable provisioned concurrency on all models.

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Increase the max-tokens parameter to generate longer responses.

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Deploy the model on multiple large GPU instances to handle peak load.

Question 10

A global enterprise is deploying a generative AI application that requires high availability across multiple OCI regions. The application must automatically fail over to a secondary region if the primary region becomes unavailable. What is the recommended architecture to achieve this?

Accepted Answer

Deploy endpoints in two regions behind an OCI Load Balancer with cross-region failover. OCI Load Balancer supports cross-region failover by distributing traffic across backend sets in multiple regions, enabling automatic failover to a secondary region when the primary region becomes unavailable. This architecture ensures high availability for generative AI applications by leveraging health checks and failover policies at the load balancer level, which is the recommended approach for multi-region active-passive setups.

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Deploy OCI Generative AI endpoints in two regions and use a global DNS round-robin

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Use OCI Streaming to replicate requests between regions

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Use DNS failover with a single endpoint in the primary region

Question 11

A financial services company is concerned about data privacy when using OCI Generative AI service for processing sensitive customer data. They want to ensure that their data is not used to improve the model and is encrypted at rest and in transit. Which combination of OCI features should they implement?

Accepted Answer

Provision a dedicated endpoint, configure data privacy opt-out, and use OCI Vault for encryption keys. It addresses all three requirements: a dedicated endpoint ensures network isolation and encryption in transit, data privacy opt-out prevents OCI from using customer data for model improvement, and OCI Vault integration allows customers to manage their own encryption keys for data at rest, meeting the financial services company's strict data privacy and encryption needs.

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Use OCI Object Storage buckets with encryption to store prompts and responses

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Deploy the model in an OCI Data Science project with a private endpoint

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Use the on-demand API with default encryption

Question 12

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. Option B is correct because the model's size and parameter count directly affect fine-tuning feasibility on OCI Generative AI — larger models demand more compute, memory, and time, and OCI imposes limits on which base models can be fine-tuned and with what dedicated AI cluster capacity. Option C is correct because the model's license and terms of use govern whether you may legally fine-tune, modify, and commercially deploy the resulting custom model, which is a mandatory consideration before selecting a base model. Option A is not a selection factor because the base model's original training dataset size is not exposed or adjustable during fine-tuning and does not determine fine-tuning suitability. Option D is not relevant because OCI Generative AI manages the training framework internally; customers do not choose PyTorch versus TensorFlow when fine-tuning a hosted base model. Option E is not a primary selection factor because built-in features like content filtering are platform-level guardrails applied regardless of which base model is chosen, not a differentiator for fine-tuning.

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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 13

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 a generative AI model deployed on OCI exposes an inference endpoint URL, and you invoke it by sending HTTP requests (typically REST POST with a JSON payload containing the prompt) to that endpoint. Option E is correct because the OCI SDKs (such as the Python or Java SDK) provide programmatic clients, e.g., the GenerativeAiInferenceClient, that authenticate with OCI credentials and call the same model endpoint, making SDK-based invocation a supported method. Options A and D are incorrect because OCI Notifications and OCI Events are messaging/event-routing services used to publish and react to state changes, not to send inference prompts to a model. Option B is incorrect because the OCI Console is an administrative web UI for managing resources and configurations, not a supported channel for invoking a deployed generative AI model's inference API.

Answer

Using OCI Notifications service

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Using the OCI Console web interface

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Using OCI Events service

Question 14

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 15

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 16

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 17

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 18

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.

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Enable autoscaling on the notebook session.

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Use OCI Data Flow instead.

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Reduce the batch size in the training script.

Question 19

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.

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Enable content filtering on the endpoint.

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Disable the use of training data in the endpoint configuration.

Question 20

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.

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Reduce the number of concurrent requests from the application.

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Use a dedicated AI cluster with higher capacity.