AI-300 Practice Test 11 — 15 Questions

Question 1

You are configuring a 'Managed Online Endpoint' with SSL termination. Where do you manage the SSL certificates?

Accepted Answer

Via the Azure front-end load balancer/gateway service.. The SSL certificates are managed at the Azure 'Front Door' or 'Application Gateway' level, or by using the built-in certificate management if using Azure-provided domains for endpoints.

Answer

In the 'Python' score script.

Answer

Inside the Endpoint configuration YAML.

Answer

In the 'workspace.json' file.

Question 2

You are experiencing latency issues with a model deployed on a Managed Online Endpoint. What is the first thing you should check?

Accepted Answer

Application Insights logs. Reviewing logs in Application Insights identifies performance bottlenecks and latency issues.

Answer

The Azure ML workspace name

Answer

The subscription limit

Answer

The storage account size

Question 3

Which THREE of the following are valid compute targets for Azure Machine Learning training?

Accepted Answer

Compute Cluster. Azure ML supports Compute Instances, Compute Clusters, and attached Kubernetes clusters for training.

Answer

Azure Function

Answer

SQL Database

Question 4

You need to ensure that training data is encrypted at rest in the Blob Storage linked to your Azure Machine Learning workspace. How do you ensure this?

Accepted Answer

Enable encryption at the storage account level.. Azure Storage accounts support Storage Service Encryption (SSE) by default. You can also use Customer-Managed Keys (CMK) for additional control.

Answer

Configure the training script to encrypt files.

Answer

Use SSL/TLS for all communication.

Answer

Use an encrypted VM for training.

Question 5

You need to monitor the health of your deployed Azure ML models. Which service is integrated directly with Azure ML to provide automated metrics and logging?

Accepted Answer

Azure Application Insights. Azure Application Insights is the native service for monitoring logs, metrics, and telemetry from deployed model endpoints.

Answer

Azure Event Grid

Answer

Azure Policy

Answer

Azure Data Factory

Question 6

When configuring a compute instance, which setting allows you to automatically stop the instance during periods of inactivity?

Accepted Answer

Idle shutdown. Idle shutdown automatically turns off the compute instance when no active processes are detected, saving costs.

Answer

Provisioning timeout

Answer

Auto-scaling

Answer

Priority based scheduling

Question 7

You are debugging an Azure ML pipeline. You want to see the stdout of a specific step that failed. How do you access this?

Accepted Answer

Run 'az ml job get-logs' with the job ID.. You can access the logs for each step by clicking on the 'Outputs + logs' tab of that specific step run in the Azure ML Studio interface.

Answer

Check the 'Experiment' dashboard settings.

Answer

Download the 'run.json' file.

Answer

Look in the 'Workspace' logs folder.

Question 8

You need to ensure that your model inference code has access to the latest secret keys without hardcoding them. What is the recommended integration?

Accepted Answer

Use the workspace Key Vault to store and retrieve secrets.. You should integrate your scoring script with Azure Key Vault using the workspace 'get_default_keyvault()' function to retrieve secrets at runtime.

Answer

Store keys as environment variables in the Dockerfile.

Answer

Use a configuration file on the VM.

Answer

Pass them as arguments to the 'init()' function.

Question 9

Which THREE items are captured in the experiment lineage in Azure Machine Learning?

Accepted Answer

Training code/snapshot.. Datasets, code, and environments are the pillars of reproducibility.

Answer

Hardware manufacturer.

Answer

Number of team members.

Question 10

You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?

Accepted Answer

Implement logic in the pipeline to gate the registration step.. You must include logic in your pipeline or script to evaluate the metric (e.g., via a 'PythonScriptStep') and only call the 'az ml model create' command if the condition is met.

Answer

Use the 'condition' parameter in the 'model create' command.

Answer

Use an 'Azure Function' to trigger registration.

Answer

Configure a 'ValidationThreshold' in the registry.

Question 11

A training job is consuming too much disk space on a compute cluster. What can you do to provide more persistent scratch space?

Accepted Answer

Mount a Datastore as a mount point. Mounting a Datastore or using Azure Files provides persistent, shared scratch space accessible by the compute cluster.

Answer

Use a smaller Docker container

Answer

Increase the OS disk size of the VM

Answer

Change the experiment name

Question 12

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

Accepted Answer

'docker build' and 'docker push' to the ACR URL.. The standard approach is 'docker build' followed by 'docker push' to the ACR URL, ensuring authentication via 'az acr login'.

Answer

'az ml environment create --custom'.

Answer

'az container create'.

Answer

'az ml model deploy --image'.

Question 13

Which THREE security features are essential for a production Azure ML deployment?

Accepted Answer

Key Vault integration.. Managed Identities, VNet integration, and Key Vault are essential for secure enterprise ML.

Answer

Open inbound ports in NSG.

Answer

Public IP exposure.

Question 14

What is the purpose of the Azure ML 'Model Registry'?

Accepted Answer

Storing model version history. The Registry manages versioning and metadata of trained models.

Answer

Managing network access

Answer

Calculating accuracy metrics

Answer

Executing training jobs

Question 15

You are creating a 'Pipeline' and want to share a dataset across multiple steps. What is the most efficient way to access this data?

Accepted Answer

Use a 'Dataset' input object that mounts the storage.. You should define the dataset as an 'Input' to the pipeline or use a 'Dataset' object that can be mounted by the compute for each step.

Answer

Download the data to each step's local directory.

Answer

Pass the file path as a string argument.

Answer

Copy the data between steps.