AI-300 Mlops Infrastructure — 20 Questions

Question 1

You need to attach an existing Azure Kubernetes Service (AKS) cluster to your Azure ML workspace for model inference. Which credential type is required to establish this connection?

Accepted Answer

Service Principal. Attaching an existing AKS cluster to an Azure ML workspace requires a Service Principal with the appropriate role assignments (e.g., Contributor on the AKS cluster or at least the ability to read the cluster's kubeconfig). The Azure ML workspace uses this Service Principal's credentials to authenticate to the AKS cluster and deploy models. Managed Identity is used for other Azure ML compute types (like compute instances and clusters) but not for attaching external AKS clusters.

Answer

SAS Token

Answer

Managed Identity

Answer

Azure Active Directory B2C

Question 2

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 3

You are setting up an MLOps pipeline and need to ensure that only approved models are deployed. Which feature should you use?

Accepted Answer

Model Registry statuses. In Azure Machine Learning, the Model Registry tracks model versions and each version carries a status (for example, 'Registered' or 'Approved'). Gating deployment pipelines on the 'Approved' status ensures only models that have passed review are promoted to production endpoints.

Answer

Data labeling

Answer

Compute targets

Answer

Auto-scaling

Question 4

A data scientist needs to access data in an Azure Data Lake Storage Gen2 account from Azure ML. You need to ensure the workspace uses the most secure method to access this data. What should you configure?

Accepted Answer

User-Assigned Managed Identity. A User-Assigned Managed Identity lets the Azure ML workspace authenticate to ADLS Gen2 via Microsoft Entra ID without storing credentials, and it can be scoped to specific resources with least-privilege RBAC (e.g., Storage Blob Data Reader). This is Microsoft's recommended, most secure access pattern for workspace-to-storage connectivity.

Answer

Shared Key

Answer

Public Access

Answer

Account SAS

Question 5

You want to automate the deployment of a model using GitHub Actions. Which file format is standard for defining the Azure ML CLI v2 deployment configuration?

Accepted Answer

YAML. Azure ML CLI v2 uses YAML files to define configurations for jobs, endpoints, and deployments.

Answer

JSON

Answer

XML

Answer

Bicep

Question 6

Your Azure ML training job fails with an 'Out of Memory' error on a GPU cluster. You need to ensure the job runs successfully without modifying the model code. What should you do?

Accepted Answer

Change the VM size to one with higher memory. The 'Out of Memory' error on a GPU cluster indicates the VM size has insufficient GPU memory (VRAM) for the training job. Changing to a VM size with higher memory (e.g., from NC6 to NC12 or ND series) directly addresses the root cause without modifying model code. Azure ML supports resizing the compute cluster by selecting a different VM SKU, which provides more GPU memory and allows the job to run successfully.

Answer

Increase the idle time for the compute cluster

Answer

Enable auto-scale to zero

Answer

Update the workspace storage account

Question 7

You are creating a CI/CD pipeline in Azure DevOps. Which extension is essential to integrate Azure ML tasks into your build/release pipeline?

Accepted Answer

Azure Machine Learning extension. The Azure Machine Learning extension for Azure DevOps installs the pipeline tasks (such as AzureML job, model registration, and pipeline tasks) that let a build or release pipeline invoke Azure ML workspaces. Without this extension, the hosted agent has no task definitions to call Azure ML CLI v2 or SDK operations, so CI/CD integration with Azure ML cannot be orchestrated.

Answer

Visual Studio Code extension

Answer

Terraform extension

Answer

Kubernetes extension

Question 8

You are setting up an Azure Machine Learning workspace and need to ensure that the workspace is not accessible from the public internet. Which networking feature should you configure?

Accepted Answer

Private Endpoint. A Private Endpoint creates a private IP address within your virtual network for the Azure Machine Learning workspace, effectively removing public internet exposure. It maps to a specific private link resource (e.g., 'amlworkspace') and allows secure access over the Azure backbone network. This is the correct feature to ensure the workspace is not accessible from the public internet.

Answer

Azure Bastion

Answer

Application Gateway

Answer

Service Tags

Question 9

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 10

Which component in an Azure ML pipeline definition is responsible for specifying the runtime environment, including Python packages and Conda dependencies?

Accepted Answer

Environment. In an Azure ML pipeline, the Environment object encapsulates the runtime (Docker base image), Python packages, and Conda dependencies used by a component. Components reference an Environment to define their execution context, so the Environment is the correct answer for specifying packages and Conda dependencies.

Answer

Compute Target

Answer

Datastore

Answer

Component

Question 11

What is the primary function of an Azure ML 'Datastore'?

Accepted Answer

Connection to storage services. Datastores are abstractions to securely connect to underlying data storage services like Blob storage or Data Lake.

Answer

Experiment tracking

Answer

Model registry

Answer

Compute resource management

Question 12

Which Azure feature is used to define infrastructure as code for your Azure ML workspace?

Accepted Answer

Azure Bicep. Azure Bicep is the recommended tool for defining and deploying Azure resources as code.

Answer

Azure Portal

Answer

PowerShell

Answer

Azure CLI

Question 13

You are managing a multi-workspace environment. You need to restrict the ability to create new compute clusters to only specific users. Where should you apply this control?

Accepted Answer

Azure RBAC on the Workspace. RBAC (Role-Based Access Control) at the workspace level determines which users can interact with compute resources.

Answer

Network Security Group

Answer

Azure Storage Access Policies

Answer

Azure ML Datastore configuration

Question 14

You need to ensure that a training script running on Azure ML has access to secrets (like API keys) without hardcoding them in the script. What should you use?

Accepted Answer

Azure Key Vault. Azure Key Vault is integrated with Azure ML to securely manage and inject secrets into training jobs.

Answer

Environment variables in the Dockerfile

Answer

Local configuration files

Answer

Hardcoding in the script

Question 15

Your team uses a custom Docker image for training. You need to update the image in the Azure Container Registry (ACR). What is the recommended workflow to ensure Azure ML picks up the latest version?

Accepted Answer

Update the image tag and refresh the Environment. Azure ML references container images through Environment objects, which pin a specific image tag (or digest) from ACR. To pick up a new image, you must push the updated image with a new tag (or overwrite the tag and force a refresh), then update the Environment definition to reference that tag and re-register it. This ensures training jobs pull the correct, latest image rather than a cached older version.

Answer

Modify the storage account permissions

Answer

Restart the compute cluster

Answer

Delete and recreate the workspace

Question 16

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 17

You are deploying a model as a real-time endpoint. You want to ensure the deployment can handle sudden spikes in traffic. Which scaling configuration should you enable?

Accepted Answer

Auto-scaling. Auto-scaling allows the endpoint to dynamically adjust node count based on CPU/Memory usage.

Answer

Static node count

Answer

Batch deployment

Answer

Pre-warmed instances

Answer

None of the above

Question 18

You want to track your model experiments and compare their performance metrics. Which Azure ML feature provides this capability?

Accepted Answer

Experiments. In Azure Machine Learning, an experiment is the logical container for tracking runs of a model training script. Each run automatically logs metrics, parameters, artifacts, and outputs, enabling you to compare performance across different configurations. This is the core capability for experiment tracking and comparison.

Answer

Environments

Answer

Model Registry

Answer

Compute targets

Question 19

In Azure ML, what is a 'Component'?

Accepted Answer

A reusable pipeline step. A component is a reusable, versioned building block for an ML pipeline.

Answer

A storage account

Answer

A user role

Answer

A physical server

Question 20

You need to debug a training job that is failing inside a specific Docker container. What is the best way to investigate?

Accepted Answer

Use 'az ml job stream' to view logs. The 'az ml job stream' command streams the logs of a running or completed Azure ML job in real time, including container logs from the Docker environment. This allows you to see error messages, stack traces, and output from the training script inside the container, making it the most direct way to debug a failing job.

Answer

Delete the datastore

Answer

Use the Azure ML SDK to download the container image

Answer

Submit a new training job without parameters