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

Question 1

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.

Answer

A local peering gateway

Answer

A dynamic group

Question 2

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

Answer

Network connectivity issues

Question 3

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. Option D is correct because 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

Answer

topP is set too high

Answer

The modelId is incorrect

Question 4

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.. Option C is correct because the OCI CLI paginates results by default; using the --all flag retrieves all models. Options A and D are possible but the most common cause is pagination; B is less likely since the user is querying a specific compartment.

Answer

The compartment does not contain the other models.

Answer

The other models are in a different region.

Answer

The user lacks permissions for other compartments.

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.. Option C is correct because 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.

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

Answer

OCI Functions

Answer

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

Answer

Create a dedicated AI cluster

Answer

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

Answer

Insufficient storage on the cluster

Answer

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. Option C is correct because 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

Answer

Enable OCI Audit service

Answer

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

Answer

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

Answer

Learning rate too high

Answer

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.

Answer

OCI Data Safe

Answer

OCI Vault

Answer

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.

Answer

User name and password

Answer

Session token

Answer

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

Answer

The cluster is in a compartment without permissions

Answer

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. Option D is correct because 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

Answer

Use a public endpoint with IP restrictions

Answer

Disable authentication for internal use