AI-300 Quick Quiz — 10 Questions

Question 1

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 2

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 3

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 4

A team is scaling a GenAI application that utilizes multiple models via model-as-a-service. You notice that inference requests are being throttled. What is the most effective infrastructure-level adjustment to handle the increased load?

Accepted Answer

Purchase and deploy Provisioned Throughput Units (PTUs) for the specific model deployment.. Provisioned throughput units (PTUs) allow for dedicated capacity, which is the standard way to prevent throttling for high-scale GenAI workloads in Azure.

Answer

Change the model version to a lower-parameter variant.

Answer

Move the deployment to a different subscription within the same Azure region.

Answer

Increase the number of instances in the associated Azure Kubernetes Service (AKS) cluster.

Question 5

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

Accepted Answer

In the 'Deployment' YAML file under 'instance_type'.. The instance type is defined in the 'Deployment' configuration object, typically in the 'instance_type' field.

Answer

In the 'Endpoint' YAML file.

Answer

In the 'Environment' definition.

Answer

In the 'Workspace' settings.

Question 6

You are deploying a GenAI app to production and want to track the 'Token Usage' and 'Latency' metrics per user session. Which service should you integrate with your application?

Accepted Answer

Azure Application Insights. Azure Application Insights is the APM service that captures custom metrics, request telemetry, dependencies, and traces, making it the right place to track per-session Token Usage and Latency. You can emit custom metrics (e.g., 'tokens_used', 'latency_ms') tagged with a session ID and visualize them in workbooks or query them with KQL.

Answer

Azure Container Registry

Answer

Azure Key Vault

Answer

Azure Policy

Answer

Azure AI Search

Question 7

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 8

You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?

Accepted Answer

Adjust 'process_count_per_node' and 'node_count'.. The 'node_count' and 'process_count_per_node' are the key parameters in 'ParallelRunConfig' that dictate the degree of parallelism.

Answer

Change the 'output_action' to 'append_row'.

Answer

Enable 'distributed_training'.

Answer

Set 'min_nodes' to 1.

Question 9

Which TWO of the following are valid ways to authenticate to an Azure AI Foundry project?

Accepted Answer

API Keys.. Option A (API Keys) is correct because Azure AI Foundry projects expose key-based authentication, where a project or resource key is passed in the request header (e.g., the api-key header) to authorize calls to the project's endpoints. Option B (Microsoft Entra ID with RBAC) is correct because Foundry projects support Entra ID token-based authentication, using role-based access control assignments (such as Cognitive Services User or Contributor) to authorize identities via OAuth 2.0 bearer tokens. Option C is not valid because hardcoding usernames in code is not an authentication mechanism and would be a security anti-pattern, not a supported credential type. Option D is not valid because local file-based passwords are not an authentication method offered by Azure AI Foundry. Option E is not valid because IP allow-listing is a network-level control, not an authentication method, and it does not verify caller identity.

Answer

Hardcoded usernames in the code.

Answer

Local file-based passwords.

Answer

Public IP allow-listing only.

Question 10

Which Azure AI Foundry feature allows you to evaluate your model's performance?

Accepted Answer

Evaluation. The Evaluation feature in Azure AI Foundry is specifically designed to assess model performance using metrics like accuracy, F1 score, and coherence. It provides built-in evaluators and supports custom evaluation flows, enabling systematic testing of models before and after deployment. This directly addresses the need to evaluate model performance, unlike other options which focus on monitoring, cataloging, or logging.

Answer

Endpoint monitoring

Answer

Model Catalog

Answer

Deployment logs