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

Question 1

Refer to the exhibit. A data scientist received this output after submitting a fine-tuning job. What is the most effective change to resolve the out-of-memory error?

Accepted Answer

Increase the number of nodes in the cluster.. The out-of-memory error during fine-tuning indicates that the model's memory requirements exceed the available resources on the current node. Increasing the number of nodes in the cluster distributes the model parameters, gradients, and optimizer states across multiple GPUs or nodes, effectively increasing the total memory capacity and resolving the OOM error. This is a standard approach in distributed training frameworks like PyTorch DDP or FSDP, which OCI Data Science supports.

Answer

Increase the sequence length.

Answer

Reduce the learning rate.

Answer

Decrease the number of fine-tuning epochs.

Question 2

A financial institution needs to deploy a fine-tuned model on OCI with strict data residency requirements. They must ensure that data used for inference never leaves a specific OCI region. The model is stored in Object Storage in the same region. What additional configuration is needed?

Accepted Answer

Configure the dedicated AI cluster to use a private endpoint and restrict access to the region. Configuring the dedicated AI cluster to use a private endpoint ensures that inference traffic stays within the OCI region and never traverses the public internet. This satisfies the strict data residency requirement by keeping all data and model inference within the designated region, while the model stored in Object Storage in the same region is accessed via the private endpoint without leaving the region.

Answer

Use OCI Data Transfer service to move data

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Set up a VPN connection to on-premises

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Enable cross-region replication on the bucket

Question 3

A developer wants to integrate generative AI capabilities into an application using REST API calls. Which OCI Generative AI service endpoint should they use for text generation?

Accepted Answer

/completions. The OCI Generative AI service exposes a REST API endpoint at `/completions` specifically for text generation tasks. This endpoint accepts a prompt and returns a generated text completion, aligning directly with the developer's requirement to integrate generative AI capabilities via REST API calls.

Answer

/models

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/inference

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/chat

Question 4

An AI team is fine-tuning a large language model using OCI Data Science and plans to deploy the fine-tuned model using the Generative AI service's custom model deployment. What is the required format for the model artifacts?

Accepted Answer

A .zip archive containing model weights and configuration files. The OCI Generative AI service requires custom model artifacts to be packaged as a .zip archive containing the model weights, configuration files (e.g., config.json, tokenizer files), and any necessary inference code. This format ensures the service can extract and load the model correctly into its managed inference infrastructure, aligning with the standard Hugging Face model repository structure.

Answer

A Git repository URL

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A single .pth file

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A Docker image with the model and inference code

Question 5

A developer is getting a 401 Unauthorized error when calling the OCI Generative AI inference API. What is the most likely cause?

Accepted Answer

The request is missing or has an invalid authentication signature. A 401 Unauthorized error specifically indicates a failure in authentication, not authorization or resource availability. The OCI Generative AI inference API requires every request to include a valid signature based on the OCI Signature Version 1 algorithm (RFC 2104 HMAC-SHA256). If the request is missing the Authorization header or the signature is malformed (e.g., incorrect key ID, mismatched signing string, or expired timestamp), the API gateway rejects it with a 401 response.

Answer

The API endpoint has reached its rate limit

Answer

The model does not support the requested parameters

Answer

The model is not deployed

Question 6

A company is designing a generative AI solution on OCI that must comply with data privacy regulations. Which three best practices should they follow? (Choose three.)

Accepted Answer

Enable audit logging for all inference requests. Option A is correct because enabling audit logging for all inference requests creates an immutable record of who accessed the generative AI service, when, and with what data, which is essential for demonstrating regulatory compliance and supporting forensic investigations. Option C is correct because dedicated AI clusters with private endpoints keep inference traffic on the OCI network and prevent data from traversing the public internet, directly supporting data residency and privacy requirements. Option E is correct because customer-managed keys (CMK) via OCI Vault give the company control over the encryption lifecycle of model artifacts and inference data, satisfying regulations that mandate customer-controlled key management. Option B is not among the marked correct answers and, while compartmentalization is a general IAM best practice, it is not one of the three practices selected here. Option D is incorrect because storing inference inputs and outputs in a public bucket would expose potentially sensitive data and violate data privacy regulations rather than support compliance.

Answer

Allocate a dedicated compartment for generative AI resources to apply specific IAM policies

Answer

Store all inference inputs and outputs in a public bucket for transparency

Question 7

Refer to the exhibit. Users in the group cannot create a new custom model deployment on a Dedicated AI Cluster. What is the most likely missing permission?

Accepted Answer

Use of virtual-network-family for cluster networking. Creating a custom model deployment on a Dedicated AI Cluster requires the user to have the 'Use of virtual-network-family for cluster networking' permission. This permission allows the user to specify and manage the virtual network (VCN) and subnet that the cluster uses for networking. Without it, the deployment fails because the cluster cannot be attached to the required network resources.

Answer

Manage ai-document-understanding

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Manage ai-agents

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Manage instance-configurations

Question 8

A data scientist needs to fine-tune a model on OCI Generative AI. Which of the following is a required parameter in the fine-tuning request?

Accepted Answer

All of the above. In OCI Generative AI, the fine-tuning request requires all three parameters: hyperparameters (to define training behavior like learning rate and epochs), model_name (to specify the base model being fine-tuned), and dataset_type (to indicate the format of the training data, such as 'TEXT' or 'MULTI_TURN'). Therefore, 'All of the above' is correct because each listed option is a mandatory field in the fine-tuning API call.

Answer

hyperparameters

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model_name

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dataset_type

Question 9

A team wants to use OCI Generative AI to generate synthetic data for training a model. They are concerned about the cost of API calls. Which pricing model would be most cost-effective for high-volume batch processing?

Accepted Answer

Reserved capacity with a fixed monthly fee. Reserved capacity with a fixed monthly fee provides the lowest per-request cost for high-volume batch processing. OCI Generative AI offers dedicated capacity pricing, which is ideal for predictable, large-scale workloads where you commit to a certain throughput, avoiding per-request charges that would accumulate significantly with high volume.

Answer

OCI Universal Credits with per-request charges

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Monthly subscription with limited requests

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Pay-as-you-go per request

Question 10

An enterprise with strict data residency requirements wants to use OCI Generative AI. They must ensure that no training data or inference data leaves a specific OCI region. Which configuration option should they choose?

Accepted Answer

Use a dedicated AI cluster in the desired region and disable cross-region access.. A dedicated AI cluster in the desired region, with cross-region access disabled, ensures that all compute, training data, and inference data remain physically within that OCI region. This satisfies strict data residency requirements because the cluster is isolated from other regions at the network and infrastructure level, preventing any data egress.

Answer

Configure a service gateway with a private endpoint.

Answer

Implement a policy restricting data transfer via OCI Identity and Access Management.

Answer

Use OCI Data Transfer Service to keep data within the region.

Question 11

A machine learning engineer evaluates OCI Generative AI for a real-time content generation application. They need to meet a SLAs of 99.9% availability. Which deployment architecture satisfies the requirement with the lowest cost?

Accepted Answer

Two dedicated AI clusters in different availability domains.. Deploying two dedicated AI clusters in different availability domains within a single region provides high availability (HA) to meet the 99.9% SLA while minimizing cost. OCI's dedicated AI clusters are regional resources, and placing replicas across availability domains protects against domain-level failures without the cross-region data transfer and egress costs incurred by multi-region deployments.

Answer

Two dedicated AI clusters in different regions.

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Two dedicated AI clusters in the same availability domain.

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Single dedicated AI cluster with a single replica.

Question 12

A company is designing a generative AI application using OCI Generative AI. Which two factors should be considered when selecting the appropriate model? (Choose two.)

Accepted Answer

Model's training data cutoff date. Option A is correct because the model's training data cutoff date determines how current its knowledge is, which directly affects the accuracy and relevance of responses for time-sensitive use cases. Option D is correct because the maximum token output limit constrains how long a generated response can be, which is critical when the application must produce long-form content such as reports or summaries. Option B is not a valid selection factor because OCI Generative AI model availability varies by region and is not a model-selection criterion for the application design. Option C is not marked correct here because, while language support matters, it is not one of the two factors identified for this scenario. Option E is not marked correct because built-in safety filters are a platform-level guardrail rather than a primary factor in choosing the model itself.

Answer

Availability in all OCI regions

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Supported languages

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Built-in safety filters

Question 13

A user sends an inference request with the JSON parameters shown. They notice the model is returning very short responses. What is the most likely cause?

Accepted Answer

temperature is set too low. A low temperature value (close to 0) makes the model highly deterministic, reducing randomness and often leading to shorter, more conservative responses. In generative AI, temperature controls the probability distribution over tokens; lower values cause the model to favor the most likely tokens, which can result in repetitive or truncated outputs. The user's inference request likely includes a temperature setting that is too low, causing the model to produce very short responses.

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maxTokens is set too high

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topP is set too high

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The modelId is incorrect

Question 14

A user runs the CLI command shown but receives only one model in the list, even though they know there are more models available in the compartment. What is the most likely reason?

Accepted Answer

The CLI command requires the --all flag to list all models.. C is correct because the OCI CLI's `oci generative-ai model list` command, by default, returns only the first page of results (typically 25 items). To retrieve all models in the compartment, the `--all` flag must be explicitly added to the command, which internally handles pagination by making successive API calls until all results are collected. Without this flag, the CLI only shows the first page, which may contain only one model if the page size is small or if the model appears first in the list.

Answer

The compartment does not contain the other models.

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The other models are in a different region.

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The user lacks permissions for other compartments.

Question 15

A data scientist in group DataScientists uses the OCI Generative AI SDK to start a fine-tuning job in compartment AIResources. They receive the error shown. What is the most likely cause?

Accepted Answer

The fine-tuning job requires additional IAM policies for accessing the training data in Object Storage.. The error message indicates a permissions issue related to accessing training data in Object Storage. When using the OCI Generative AI SDK to start a fine-tuning job, the data scientist's IAM policies must explicitly grant read access to the bucket and objects containing the training data. Without these policies, the API call fails even if the user is in the correct group and the compartment exists.

Answer

The compartment AIResources does not exist.

Answer

The fine-tuning API is not yet available in that region.

Answer

The data scientist is not in the DataScientists group.