38 questions · Mlops Infrastructure · All types, answers revealed
Which Azure feature is used to define infrastructure as code for your Azure ML workspace?
Bicep is a declarative language for deploying Azure infrastructure.
Why this answer
Azure Bicep is the recommended tool for defining and deploying Azure resources as code.
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?
Azure ML environments rely on tags; refreshing the environment definition ensures the latest image is used.
Why this answer
Updating the image tag and updating the environment definition in Azure ML forces the system to pull the new version.
Which TWO of the following can be used to manage Azure ML assets like environments and models?
Provides command-line management.
Why this answer
The Azure ML Studio UI and the CLI v2 are both primary interfaces for asset management.
A training job is consuming too much disk space on a compute cluster. What can you do to provide more persistent scratch space?
Mounting a Datastore provides external storage that can be used for large datasets or scratch space.
Why this answer
Mounting a Datastore or using Azure Files provides persistent, shared scratch space accessible by the compute cluster.
In Azure ML, what is a 'Component'?
Components are modular, reusable steps in an ML pipeline.
Why this answer
A component is a reusable, versioned building block for an ML pipeline.
Which TWO of the following are benefits of using Azure ML Environments?
Ensures reproducibility.
Why this answer
Environments ensure reproducibility and simplify dependency management across environments.
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?
Key Vault stores secrets securely and allows the workspace to retrieve them at runtime.
Why this answer
Azure Key Vault is integrated with Azure ML to securely manage and inject secrets into training jobs.
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?
Azure ML requires a Service Principal to manage and interact with the Kubernetes API on the attached cluster.
Why this answer
The Service Principal is the standard method for managing identity-based access for AKS clusters attached to Azure ML.
You need to orchestrate a series of ML steps (Data prep, Training, Evaluation). Which Azure ML feature is specifically designed for this?
Pipelines are the standard for chaining ML tasks.
Why this answer
Azure ML Pipelines allow you to string together components into a workflow.
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?
A Private Endpoint provides a private IP address for the workspace, blocking public access.
Why this answer
A Private Endpoint allows you to access your workspace securely via a private IP address within your virtual network.
What is the purpose of the Azure ML 'Model Registry'?
The registry keeps track of models, versions, and lineage.
Why this answer
The Registry manages versioning and metadata of trained models.
What is the primary function of an Azure ML 'Datastore'?
Datastores act as a bridge to underlying storage, keeping credentials abstract.
Why this answer
Datastores are abstractions to securely connect to underlying data storage services like Blob storage or Data Lake.
Which TWO of the following storage types can be registered as an Azure ML Datastore?
Standard datastore.
Why this answer
Azure Blob Storage and Azure Data Lake Storage Gen2 are both standard datastore types.
Which THREE of the following are types of Azure ML endpoints?
Standard batch inference endpoint.
Why this answer
Managed online, batch, and web service (legacy) represent the main endpoint categories.
You are experiencing latency issues with a model deployed on a Managed Online Endpoint. What is the first thing you should check?
Logs provide granular detail on request processing times and potential bottlenecks.
Why this answer
Reviewing logs in Application Insights identifies performance bottlenecks and latency issues.
Which TWO of the following are benefits of using the Azure ML CLI v2 for your MLOps pipelines?
Designed for automation in DevOps/GitHub.
Why this answer
CLI v2 enables YAML-based infrastructure-as-code and better cross-platform support.
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?
Application Insights collects telemetry and performance metrics from deployed endpoints.
Why this answer
Azure Application Insights is the native service for monitoring logs, metrics, and telemetry from deployed model endpoints.
You are using GitHub Actions. You want to authenticate to Azure without using hardcoded credentials. What should you use?
OIDC allows GitHub Actions to assume an Azure identity without secrets.
Why this answer
OIDC (OpenID Connect) is the secure way for GitHub Actions to authenticate to Azure.
You want to track your model experiments and compare their performance metrics. Which Azure ML feature provides this capability?
Experiments are the standard container for tracking and comparing ML runs.
Why this answer
The Experiments and Run History in Azure ML automatically log and compare metrics across different runs.
When configuring a compute instance, which setting allows you to automatically stop the instance during periods of inactivity?
Idle shutdown provides automated cost management based on inactivity.
Why this answer
Idle shutdown automatically turns off the compute instance when no active processes are detected, saving costs.
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?
CLI v2 utilizes YAML configuration files for defining infrastructure as code.
Why this answer
Azure ML CLI v2 uses YAML files to define configurations for jobs, endpoints, and deployments.
Which THREE of the following are key components of a robust MLOps strategy in Azure?
Essential for deployment speed and consistency.
Why this answer
Version control, automated pipelines, and model monitoring are pillars of MLOps.
You are creating a CI/CD pipeline in Azure DevOps. Which extension is essential to integrate Azure ML tasks into your build/release pipeline?
This extension enables native tasks like model registration, training runs, and deployment.
Why this answer
The Azure Machine Learning extension provides the necessary tasks to interact with the Azure ML CLI from DevOps pipelines.
You are setting up an MLOps pipeline and need to ensure that only approved models are deployed. Which feature should you use?
Registry statuses allow you to control and gate which versions move to production.
Why this answer
Model tagging and status (e.g., 'Production', 'Staging') in the Model Registry allow for governance and gates.
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?
Managed identities provide secure, secret-less authentication to Azure resources.
Why this answer
Using a User-Assigned Managed Identity allows the Azure ML workspace to authenticate with the storage account without storing keys.
Which TWO of the following are required to secure an Azure ML workspace using a Private Link?
Required to host the private endpoint.
Why this answer
A Private Endpoint and a specific VNet configuration are core to the Private Link setup.
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?
Azure RBAC allows for granular permissions, specifically who can manage compute clusters.
Why this answer
RBAC (Role-Based Access Control) at the workspace level determines which users can interact with compute resources.
You need to prevent data egress from your Azure ML environment. What configuration is required?
Managed VNets allow for granular control over network traffic, including blocking egress.
Why this answer
A workspace with no public IP and a managed virtual network with egress controls prevents unauthorized data movement.
What is the primary benefit of using Azure ML compute clusters instead of compute instances for training?
Compute clusters support multi-node scaling for distributed training.
Why this answer
Compute clusters are designed for distributed, scalable training across multiple nodes.
You need to debug a training job that is failing inside a specific Docker container. What is the best way to investigate?
Streaming logs provides real-time visibility into the containerized process.
Why this answer
The standard approach is to use 'az ml job stream' to view logs or access the compute node via SSH if enabled.
Which component in an Azure ML pipeline definition is responsible for specifying the runtime environment, including Python packages and Conda dependencies?
The Environment defines the software configuration, packages, and Docker image for the execution.
Why this answer
The Environment object defines the software stack, including dependencies and Docker images, needed for a job.
A pipeline step fails because it cannot find a file in the datastore. What is the most likely cause?
Pipeline components must accurately reference paths within the mounted datastore.
Why this answer
The mount path or the relative path defined in the pipeline step component configuration is likely incorrect.
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?
Auto-scaling dynamically adjusts resources based on defined metrics like CPU utilization.
Why this answer
Auto-scaling allows the endpoint to dynamically adjust node count based on CPU/Memory usage.
Which THREE of the following are valid compute targets for Azure Machine Learning training?
Valid training target.
Why this answer
Azure ML supports Compute Instances, Compute Clusters, and attached Kubernetes clusters for training.
Which THREE of the following represent lifecycle stages of an ML model in Azure ML?
The model generation phase.
Why this answer
Training, Registration, and Deployment are sequential stages in the model lifecycle.
Which THREE of the following are common reasons for a model deployment to fail on a Managed Online Endpoint?
Causes runtime import errors.
Why this answer
Invalid scoring scripts, missing dependencies in the environment, and insufficient resource limits are frequent causes.
Which THREE factors influence the cost of an Azure ML Compute Cluster?
Reduces cost during inactivity.
Why this answer
VM SKU, node count, and idle time settings directly impact spending.
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?
Selecting a VM SKU with more RAM and GPU memory resolves OOM errors.
Why this answer
Scaling up to a VM size with more RAM or GPU memory is the most direct way to resolve OOM issues in training jobs.
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