1Z0-1127 Timed Practice Test 4 — 10 Questions

Question 1

Which THREE factors should be considered when choosing between fine-tuning a model and using a pre-trained model with prompt engineering? (Select three.)

Accepted Answer

Size of available dataset. Option B is correct because the size of the available dataset is a critical factor: fine-tuning requires a sufficiently large, labeled dataset (typically thousands of examples) to adjust model weights effectively, while prompt engineering can work with zero or few examples. If the dataset is too small, fine-tuning risks overfitting and poor generalization, making prompt engineering the safer choice.

Answer

Required response time

Answer

Internet connectivity

Question 2

A company wants to deploy a custom generative AI model for generating synthetic data for training other models. The model requires approximately 20GB of memory and must be accessible via a REST API with authentication. Additionally, the team needs to monitor for data drift over time. Which combination of OCI services best meets these requirements with minimal operational overhead?

Accepted Answer

OCI Data Science Model Deployment with OCI Monitoring and OCI Logging. Option B is correct because OCI Data Science Model Deployment provides a managed environment for hosting custom generative AI models with REST API endpoints and built-in authentication via OCI IAM. It integrates natively with OCI Monitoring and OCI Logging to track data drift and operational metrics without requiring additional infrastructure setup, minimizing operational overhead.

Answer

OCI Compute with custom Docker container and Prometheus monitoring

Answer

OCI Functions with API Gateway for authentication

Answer

OCI Data Flow with OCI Data Catalog for model registry

Question 3

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 4

A company is deploying a generative AI model for a real-time inference API. To ensure high availability and cost efficiency under variable load, which two configurations should they implement? (Choose two.)

Accepted Answer

Set the number of model deployment replicas to at least 2. Option D is correct because deploying at least two replicas ensures high availability by eliminating a single point of failure; if one replica fails, the other can still serve inference requests. This is a standard best practice for production workloads on OCI, where model deployment replicas are distributed across fault domains to maintain service continuity.

Answer

Use a single replica with a larger GPU to handle all traffic

Answer

Deploy the model in a single availability domain to simplify management

Answer

Disable connection draining on the load balancer

Question 5

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 6

Your team has deployed a fine-tuned GPT-2 model on OCI Model Deployment for a simple text generation API. The model performs text completion for short prompts (e.g., 50 tokens). The endpoint is working but response times are over 10 seconds for these short prompts. The model size is approximately 500MB and you used a VM.Standard.E3.Flex shape (2 OCPU, 16GB RAM). The deployment is in a single replica with no autoscaling. You have verified that the network latency is minimal (<5ms). The model was trained in OCI Data Science using a GPU shape, but during deployment you selected a CPU shape to reduce cost. The model is a transformer-based neural network. You've also confirmed that the deployment is healthy and there are no errors in the logs. The memory usage is within limits. What is the most likely cause of the high latency?

Accepted Answer

Missing GPU acceleration for inference. Option D is correct because GPT-2 is a transformer-based neural network that relies heavily on matrix multiplications, which are far more efficiently executed on GPUs due to their parallel architecture. Even though the model is only 500MB, CPU inference for transformer models is notoriously slow because CPUs process sequential operations, while GPUs can parallelize the attention mechanism and feed-forward layers. The 10-second latency for a 50-token prompt is a classic symptom of missing GPU acceleration, as the CPU shape (2 OCPU) lacks the specialized tensor cores needed for fast transformer inference.

Answer

High network latency between the client and the model endpoint

Answer

Model is too large for the VM.Standard.E3.Flex shape

Answer

Insufficient CPU resources for the model size

Question 7

You manage a generative AI model deployed on OCI Model Deployment that serves a chatbot application. The model is a 13B parameter LLM on a VM.GPU.A100.1 shape. Recently, you rolled out a new version of the model that is supposed to improve response quality. However, after the update, the application starts returning HTTP 500 errors and memory usage spikes. You need to update to the new version without causing downtime. The current deployment has 2 replicas with autoscaling enabled. Which strategy should you use to safely deploy the new model version?

Accepted Answer

Create a second deployment with the new model, test it, then shift traffic using a load balancer. Option B is correct because it implements a blue/green deployment strategy: you create a second deployment with the new model, test it in isolation, and then shift traffic using a load balancer. This avoids downtime and allows you to validate the new model before exposing it to production traffic, which is critical given the observed HTTP 500 errors and memory spikes.

Answer

Directly update the existing model deployment with the new model artifact

Answer

Stop the existing deployment, update the model artifact, then start the deployment

Answer

Increase the number of replicas to 4, then update the model

Question 8

A company is deploying a generative AI model on OCI for an internal application that must comply with strict security policies. The model will be accessed by a limited group of users. Which three actions should the administrator take to ensure security? (Choose three.)

Accepted Answer

Deploy the model in a private VCN subnet. Deploying the model in a private VCN subnet ensures that the model endpoint is not exposed to the internet, which is a fundamental security requirement for compliance with strict security policies. By placing the model in a private subnet, all traffic must traverse through a bastion host, VPN, or FastConnect, providing network isolation and reducing the attack surface. This aligns with OCI's shared responsibility model where the customer controls network security.

Answer

Expose the model endpoint to the internet for ease of access

Answer

Disable audit logging to minimize storage costs

Question 9

A data scientist wants to quickly test a prompt with different parameters like temperature and max tokens without writing code. Which OCI GenAI feature should they use?

Accepted Answer

OCI Generative AI Playground.. The OCI Generative AI Playground is a web-based, no-code interface that allows data scientists to interactively test prompts and adjust parameters like temperature and max tokens without writing any code. This directly matches the user's requirement for quick, code-free experimentation.

Answer

OCI CLI.

Answer

OCI SDK.

Answer

OCI Data Science Notebooks.

Question 10

During fine-tuning of a large language model on OCI, you notice that the model's performance on the validation set is not improving after several epochs, but the training loss continues to decrease. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. When training loss decreases but validation performance stagnates or worsens, the model is overfitting to the training data. It memorizes the training examples but fails to generalize. A high learning rate might cause divergence, not this pattern. Too small training data can contribute to overfitting but is not the direct symptom. An unrepresentative validation set could cause mismatch, but the described pattern is classic overfitting.

Answer

The learning rate is too high.

Answer

The validation set is not representative.

Answer

The training data is too small.