1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Test 7 — 15 Questions

Question 1

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. The model's training data cutoff date determines the temporal scope of the model's knowledge. For generative AI applications requiring up-to-date information or compliance with data recency requirements, selecting a model with a cutoff date that aligns with the use case is critical. OCI Generative AI models have specific cutoff dates (e.g., June 2023 for certain models), and using a model with an older cutoff may produce outdated or factually incorrect responses.

Answer

Availability in all OCI regions

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Supported languages

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Built-in safety filters

Question 2

An OCI administrator is configuring access control for OCI Generative AI. Which three IAM components are required to allow a group of data scientists to call the GenerateText API? (Choose three.)

Accepted Answer

An IAM group for the data scientists. An IAM group is required to organize the data scientists into a logical set of principals. IAM policies are then attached to this group to grant permissions, ensuring only members of the group can call the GenerateText API. Without a group, you cannot apply a policy to a collection of users.

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A local peering gateway

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A dynamic group

Question 3

A developer is troubleshooting an OCI Generative AI inference request that returns a 400 Bad Request error. Which three common causes could result in this error? (Choose three.)

Accepted Answer

Incorrect endpoint URL. A 400 Bad Request error indicates the server cannot process the request due to client-side issues. An incorrect endpoint URL (A) is a common cause because the request is sent to the wrong OCI Generative AI service endpoint (e.g., using a chat endpoint for a text generation model), leading to a malformed request that the server rejects. Missing required parameters (C) in the request body, such as 'compartmentId' or 'modelId', also triggers a 400 error as the API cannot validate or process the inference without them. Exceeding the model's maximum token limit (D) results in a 400 error because the input or output exceeds the model's configured token capacity, which the API validates before processing.

Answer

Invalid API key in the request header

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Network connectivity issues

Question 4

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.

Answer

maxTokens is set too high

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topP is set too high

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The modelId is incorrect

Question 5

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.

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

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The data scientist is not in the DataScientists group.

Question 6

A company deploys a fine-tuned Llama 2 model using OCI Generative AI service. They want to ensure low-latency inference for a real-time chat application. Which deployment option should they use?

Accepted Answer

Dedicated AI cluster. A dedicated AI cluster provides reserved compute resources (GPUs) for low-latency, real-time inference by eliminating resource contention. This is essential for a fine-tuned Llama 2 model in a chat application where consistent sub-second response times are required, unlike shared or serverless options that introduce cold starts or queuing delays.

Answer

Batch inference job

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OCI Functions

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Serverless endpoint (standard)

Question 7

A data scientist fine-tunes a model using OCI Data Science and wants to deploy it as a managed endpoint in OCI Generative AI. What must they do first?

Accepted Answer

Upload model artifacts to Object Storage and register in Model Catalog. To deploy a fine-tuned model as a managed endpoint in OCI Generative AI, the model artifacts must first be uploaded to Object Storage and registered in the Model Catalog. This is a prerequisite because OCI Generative AI endpoints pull model artifacts from the Model Catalog, which references the storage location. Without registration, the service cannot locate or serve the model.

Answer

Write a custom container

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Create a dedicated AI cluster

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Use OCI CLI to create an endpoint

Question 8

A team deploys a generative AI model endpoint and notices intermittent 429 Too Many Requests errors. The endpoint is configured with auto-scaling using a dedicated AI cluster. What is the most likely cause?

Accepted Answer

The auto-scaling policy is not aggressive enough. The 429 Too Many Requests error indicates that the endpoint is receiving more requests than it can handle. With auto-scaling enabled on a dedicated AI cluster, the most likely cause is that the auto-scaling policy is not aggressive enough to keep up with the request rate, meaning it scales up too slowly or has insufficient maximum instance limits to handle the traffic spike.

Answer

The model's context window exceeded

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Insufficient storage on the cluster

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Rate limiting at the OCI API Gateway

Question 9

An organization wants to use OCI Generative AI to build a summarization tool but must ensure that all inference requests are logged for audit purposes. Which approach should they take?

Accepted Answer

Enable OCI Logging on the generative AI endpoint. OCI Logging can be enabled directly on the Generative AI endpoint to capture all inference requests and responses as logs, which can then be used for audit purposes. This is the native, recommended approach for logging API calls without introducing additional infrastructure or complexity.

Answer

Implement a custom proxy with logging

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Enable OCI Audit service

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Use OCI Vault to store logs

Question 10

A company deploys a large language model on a dedicated AI cluster with 4 nodes. The model requires 128 GB of memory per instance, but the nodes have only 64 GB each. During inference, the nodes experience out-of-memory errors. What is the best solution?

Accepted Answer

Enable model parallelism across nodes. Model parallelism splits the model's layers or parameters across multiple nodes, allowing the 128 GB model to be distributed across the 4 nodes (each with 64 GB) so that no single node exceeds its memory capacity. This is the best solution because it directly addresses the memory constraint without requiring additional hardware or sacrificing inference throughput, and it is a standard technique for deploying large language models on distributed AI clusters.

Answer

Increase the number of nodes to 8

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Upgrade to higher memory node shapes

Answer

Reduce the batch size in inference requests

Question 11

A team uses OCI Generative AI’s fine-tuning capability to adapt a base model. After fine-tuning, they evaluate the model but see degraded performance on certain edge cases. What is the most likely cause?

Accepted Answer

Overfitting on the training data. Fine-tuning adapts a base model to a specific dataset, but if the training data is too narrow or the model is trained for too many epochs, it can memorize the training examples rather than learning generalizable patterns. This overfitting causes the model to perform well on training-like inputs but poorly on edge cases that deviate from the training distribution. In OCI Generative AI, overfitting is a common pitfall when fine-tuning hyperparameters like the number of epochs or learning rate are not properly validated.

Answer

Validation data leakage

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Learning rate too high

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Insufficient training epochs

Question 12

A financial company deploys a generative AI model for document analysis. They need to ensure that the model does not expose sensitive information in its responses. Which OCI service should they use to implement content filtering?

Accepted Answer

OCI AI Content Moderation. OCI AI Content Moderation is the correct service because it provides pre-trained models and APIs specifically designed to detect and filter sensitive content such as personally identifiable information (PII), profanity, and other unsafe text in generative AI outputs. This allows the financial company to enforce content safety policies on document analysis responses, preventing exposure of sensitive information.

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OCI Data Safe

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OCI Vault

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OCI WAF

Question 13

A user wants to invoke an OCI Generative AI endpoint from a cloud function. What is the required authentication method?

Accepted Answer

API signing key. OCI Generative AI endpoints require API signing keys for authentication because they are REST APIs that use the Signature Version 1 algorithm (based on HMAC-SHA256) to sign requests. Cloud Functions must include a signed HTTP header using a user's or service principal's OCI API signing key pair (private key for signing, public key uploaded to OCI) to prove identity and authorization. This is the standard method for programmatic access to OCI services, including Generative AI, and is enforced by the OCI Identity and Access Management (IAM) policy layer.

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User name and password

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Session token

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OCI certificate

Question 14

An administrator notices that a dedicated AI cluster is not scaling down after a period of low traffic. What could be the cause?

Accepted Answer

The cluster has a minimum size set to the current number of nodes. A dedicated AI cluster in OCI has a minimum size configuration that prevents the autoscaler from reducing the node count below that threshold. If the current number of nodes equals the configured minimum, the cluster will not scale down even during low traffic, as the autoscaler respects this lower bound. This ensures baseline capacity is always available for inference workloads.

Answer

There are pending inference requests

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The cluster is in a compartment without permissions

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The autoscaling policy uses a cooldown period that is too short

Question 15

Which TWO are best practices for securing a generative AI endpoint on OCI? (Select TWO)

Accepted Answer

Store API keys in OCI Vault. OCI Vault provides a secure, centralized service for storing and managing API keys used to authenticate requests to generative AI endpoints. Storing keys in Vault prevents hardcoding them in application code or configuration files, reducing the risk of exposure and enabling automated rotation and access control via IAM policies.

Answer

Enable OCI Logging for audit

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Use a public endpoint with IP restrictions

Answer

Disable authentication for internal use