AI-300 Practice Exam 1 — 20 Questions

Question 1

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

Accepted Answer

Model prediction precision. Accuracy and precision are key performance indicators that signify model decay.

Answer

CPU usage of the inference cluster

Answer

Network latency

Answer

Number of active users

Question 2

You want to enforce a policy that all models must be registered before being deployed. Which feature should you use to implement this constraint?

Accepted Answer

Azure Policy definitions.. Azure Policy for Azure Machine Learning can be used to restrict actions, such as preventing deployment of models that do not have a specific 'registered' status or tag.

Answer

Virtual Network peering.

Answer

Role-Based Access Control (RBAC).

Answer

Compute Instance quotas.

Question 3

You are optimizing your Azure ML pipeline performance. Which THREE steps should you take to reduce execution time?

Accepted Answer

Mount datasets instead of downloading them.. Caching (reuse), parallelizing steps, and using efficient data access methods are key.

Answer

Increase the number of workspaces.

Answer

Use smaller compute clusters for everything.

Question 4

You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?

Accepted Answer

Create a Data Drift Monitor. You must create a Data Drift Monitor object linked to your target dataset and baseline dataset to begin tracking.

Answer

Configure Application Insights

Answer

Run a manual evaluation script

Answer

Enable Azure Monitor logs

Question 5

You are managing model versioning in Azure Machine Learning Registry. You need to promote a model from 'Staging' to 'Production' without creating a new asset version. Which command or action should you perform?

Accepted Answer

Update the model asset by adding a 'Production' tag via the SDK or CLI.. You should use the 'update' command on the existing model version asset to update its tags or properties to reflect the production status.

Answer

Change the 'run_id' in the model metadata.

Answer

Delete the old version and re-register as 'Production'.

Answer

Run 'az ml model create' with a new version number.

Question 6

Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?

Accepted Answer

Use a shared Azure Machine Learning Registry.. Azure Machine Learning Registries allow for the sharing of model assets, environments, and components across multiple workspaces.

Answer

Export the model as a pickle file and upload to each workspace.

Answer

Duplicate the workspace storage account.

Answer

Re-train the model in every workspace.

Question 7

What is the purpose of a 'Datastore' in Azure Machine Learning?

Accepted Answer

To manage connectivity to storage services.. A Datastore is an abstraction layer over Azure storage services (Blob, Files, ADLS) used to manage connections and data access in ML tasks.

Answer

To store model weights.

Answer

To track model versions.

Answer

To process data in memory.

Question 8

You are configuring a 'Managed Online Endpoint' for a very large model (10GB+). The deployment is failing during the 'pulling image' phase. What is the most likely cause?

Accepted Answer

The 'readiness_probe' timeout is too short.. Large images or model artifacts often cause timeout issues during container startup. You might need to increase the 'readiness_probe' timeout in the deployment configuration.

Answer

The instance count is too high.

Answer

The ACR is private.

Answer

The compute is too small.

Question 9

You are troubleshooting a model deployment failure where the container fails to start due to missing environment variables. Where do you find the logs to identify the cause?

Accepted Answer

The 'get-logs' command for the online deployment.. The 'deployment logs' are accessible via the Azure ML Studio UI or the CLI command 'az ml online-deployment get-logs', which pulls from the container runtime.

Answer

The Key Vault access logs.

Answer

The pipeline run history.

Answer

The workspace diagnostic logs in Azure Monitor.

Question 10

You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?

Accepted Answer

'process_count' or 'node_count' in the configuration.. When using 'PyTorch' distribution, you must specify the 'process_count' or 'node_count' to correctly distribute the workload across the compute cluster.

Answer

'framework' set to 'TensorFlow'.

Answer

'shm_size' set to 1GB.

Answer

'enable_gpu' set to False.

Question 11

Which THREE metrics can be logged during training to track performance in Azure ML?

Accepted Answer

Model accuracy.. Accuracy, loss, and custom metrics are standard loggable items.

Answer

Compute instance IP address.

Answer

Workspace subscription ID.

Question 12

What is the primary purpose of a 'Labeling Project' in Azure Machine Learning?

Accepted Answer

To facilitate manual data annotation for supervised learning.. Labeling projects are used to manage the process of annotating data (images, text) to create datasets for supervised learning.

Answer

To automatically version datasets.

Answer

To monitor model deployment drift.

Answer

To define model evaluation metrics.

Question 13

You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?

Accepted Answer

'MedianStoppingPolicy'.. The 'BanditPolicy' is the standard early-termination policy that stops runs based on a slack factor/amount compared to the best-performing run.

Answer

'TruncationSelectionPolicy'.

Answer

'RandomParameterSampling'.

Question 14

What is the benefit of using 'Model Catalog' in Azure AI Foundry?

Accepted Answer

It provides pre-optimized models with one-click deployment.. The Model Catalog provides a curated list of models that are pre-optimized for deployment on Azure infrastructure.

Answer

It provides free LLM usage for all models.

Answer

It eliminates the need for authentication.

Answer

It automatically builds your application UI.

Question 15

To optimize the cost of your AI infrastructure, which THREE actions should you consider?

Accepted Answer

Deleting unused model deployments.. Choosing the right SKU, cleaning up unused deployments, and using reserved capacity are standard cost-optimization steps.

Answer

Deploying all models in every available region.

Answer

Always selecting the 'Premium' SKU.

Question 16

What is the purpose of a 'System-Assigned Managed Identity' in the context of an Azure AI project?

Accepted Answer

To provide a secure identity for the resource to interact with other Azure services.. Managed identities allow the project or compute resource to securely access other Azure resources (like Key Vault or storage) without managing explicit credentials.

Answer

To automatically encrypt all data in transit.

Answer

To allow users to log in to the project portal.

Answer

To provide public IP connectivity.

Question 17

Which THREE items should you keep track of in your AI Foundry project to ensure good governance?

Accepted Answer

Quota usage.. Governance relies on tracking tags, role assignments, and quotas.

Answer

User passwords.

Answer

Personal browser history.

Question 18

You are moving a model from a local environment to Azure Machine Learning. Which file is required to define the entry script for the model inference?

Accepted Answer

'score.py'.. The entry script (often named 'score.py') is required to define 'init()' and 'run()' functions for the deployment.

Answer

'inference_config.json'.

Answer

'model.pkl'.

Answer

'environment.yml'.

Question 19

Which TWO methods are best for debugging an LLM pipeline that fails on complex queries?

Accepted Answer

Inspecting raw input and output tokens via logs.. Prompt logging and trace inspection are the best ways to understand why a model fails on specific inputs.

Answer

Increasing the number of API users.

Answer

Disabling all security controls.

Answer

Deleting the deployment and rebuilding it.

Question 20

You are troubleshooting a failed agent deployment. Which TWO areas should you inspect first?

Accepted Answer

The endpoint availability status.. Deployment logs and endpoint connectivity are the primary sources for identifying deployment failures.

Answer

The Azure status page.

Answer

The user profile settings.

Answer

The billing usage report.