AI-300 Mlops Infrastructure Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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

Question 6

What is the primary benefit of using Azure ML compute clusters instead of compute instances for training?

Accepted Answer

Support for multiple nodes. Compute clusters are designed for distributed, scalable training across multiple nodes.

Answer

Clusters have more RAM per node

Answer

Instances are deprecated

Answer

Cost is always lower

Question 7

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 8

You need to orchestrate a series of ML steps (Data prep, Training, Evaluation). Which Azure ML feature is specifically designed for this?

Accepted Answer

Azure ML Pipelines. Azure ML Pipelines allow you to string together components into a workflow.

Answer

Notebooks

Answer

Batch endpoints

Answer

AutoML

Question 9

You need to prevent data egress from your Azure ML environment. What configuration is required?

Accepted Answer

Managed VNet and egress locks. A workspace with no public IP and a managed virtual network with egress controls prevents unauthorized data movement.

Answer

Enable public access

Answer

Use a public load balancer

Answer

Create a new resource group

Question 10

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 11

You are using GitHub Actions. You want to authenticate to Azure without using hardcoded credentials. What should you use?

Accepted Answer

OIDC. OIDC (OpenID Connect) is the secure way for GitHub Actions to authenticate to Azure.

Answer

SSH keys

Answer

Azure Storage keys

Answer

User name and password

Question 12

A pipeline step fails because it cannot find a file in the datastore. What is the most likely cause?

Accepted Answer

Incorrect path definition in the component. The mount path or the relative path defined in the pipeline step component configuration is likely incorrect.

Answer

The workspace is too large

Answer

The compute cluster is off

Answer

The subscription is expired

Question 13

Which TWO of the following are benefits of using the Azure ML CLI v2 for your MLOps pipelines?

Accepted Answer

Native integration with CI/CD tools. Option A is correct because the Azure ML CLI v2 is designed to be invoked from automation systems such as GitHub Actions, Azure Pipelines, and Jenkins, allowing pipeline steps (e.g., `az ml job create`) to be scripted and triggered as part of CI/CD workflows. Option E is correct because CLI v2 relies on declarative YAML files (such as job, component, and pipeline YAML schemas) to define environments, inputs, outputs, and pipeline steps, which makes configurations versionable and reproducible. Automatic model training (B) is not a benefit of the CLI itself, since training algorithms and jobs must still be defined by the user. No need for Python (C) is false because CLI v2 is a wrapper over the Azure ML Python SDK v2 and many pipeline components are authored in Python. Built-in model visualization (D) is not a CLI v2 feature; visualization is provided through Azure ML studio and related tooling, not the command-line interface.

Answer

Automatic model training

Answer

No need for Python

Answer

Built-in model visualization

Question 14

Which THREE of the following are key components of a robust MLOps strategy in Azure?

Accepted Answer

Automated CI/CD pipelines. Automated CI/CD pipelines (B) are a core MLOps component because Azure DevOps or GitHub Actions can automatically build, test, and deploy ML assets via Azure Machine Learning pipelines, ensuring repeatable and reliable releases. Version control for code and models (C) is essential since Azure ML and Git track code, datasets, and model artifacts, enabling reproducibility, auditability, and rollback of experiments and deployments. Automated model monitoring (E) is also key because Azure Machine Learning model monitoring detects data drift, prediction drift, and performance degradation in production, triggering alerts or retraining. Manual model testing (A) is not a robust MLOps practice because it is error-prone, slow, and non-repeatable compared with automated testing in CI/CD. Using only local workstations (D) is incorrect because MLOps relies on shared, scalable cloud resources such as Azure Machine Learning compute, registries, and pipelines rather than isolated local environments.

Answer

Manual model testing

Answer

Using only local workstations

Question 15

Which TWO of the following storage types can be registered as an Azure ML Datastore?

Accepted Answer

Azure Blob Storage. Azure Blob Storage (B) is correct because it is one of the core Azure storage services natively supported by Azure Machine Learning for registering as a datastore, allowing ML workspaces to reference blob containers for datasets, training data, and model artifacts via the azureml-core SDK or studio. Azure Data Lake Storage Gen2 (C) is also correct because it is a supported datastore type in Azure ML, providing hierarchical namespace access over blob storage for big-data analytics and ML workloads. Azure App Service (A) is a PaaS web hosting platform, not a storage service, so it cannot be registered as a datastore. Microsoft Entra ID (D) is an identity and access management service, not a storage backend. Azure Bot Service (E) is a conversational AI service, not a storage service, and therefore cannot be registered as an Azure ML datastore.

Answer

Azure App Service

Answer

Azure Active Directory

Answer

Azure Bot Service