AI-300 Practice Test 9 — 10 Questions

Question 1

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

Accepted Answer

Azure ML Components. Azure ML components allow for independent versioning and reusability across different pipelines.

Answer

Azure ML Datasets

Answer

Azure ML Pipelines Steps

Answer

Azure Container Registry images

Question 2

You are monitoring model drift. Which TWO features are required to configure a Data Drift Monitor?

Accepted Answer

A target dataset to compare against.. You need a target dataset (baseline) and a comparison dataset (current).

Answer

An Azure SQL database.

Answer

A model version.

Answer

A custom Python script.

Question 3

Which THREE types of information are found in a Run Object in Azure ML?

Accepted Answer

Tags.. Metrics, parameters, and tags are all core components of a run record.

Answer

Billing history.

Answer

Network topology map.

Question 4

You are deploying a model to an Azure Kubernetes Service (AKS) cluster. You need to ensure that the deployment handles traffic spikes by scaling based on GPU usage. Which setting must be enabled in the inference configuration?

Accepted Answer

Set 'autoscale_enabled' to True and 'target_utilization' for GPU metrics.. To scale based on GPU usage, you must define an 'autoscale' configuration within the 'InferenceConfig' that references 'target_utilization' for custom metrics like GPU.

Answer

Use 'enable_gpu' in the 'DeploymentConfig'.

Answer

Configure 'max_concurrent_requests'.

Answer

Enable 'cluster_purpose' as 'FastProd'.

Question 5

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 6

Which THREE components are required to define a 'Managed Online Deployment'?

Accepted Answer

An environment definition.. An environment, a model artifact, and an inference script (score.py).

Answer

A GPU compute instance.

Answer

A training dataset.

Question 7

Which service allows you to track ML models and their associated artifacts?

Accepted Answer

Azure Model Registry.. The 'Azure Machine Learning Model Registry' is specifically designed to store, version, and manage models.

Answer

Azure Container Registry.

Answer

Azure Key Vault.

Answer

Azure Data Lake.

Question 8

You need to ensure that your Azure ML models are deployed with high availability across multiple regions. Which deployment strategy should you configure?

Accepted Answer

Managed Online Endpoints with multiple deployments. Managed Online Endpoints support multi-region traffic distribution by using managed deployments behind a single endpoint.

Answer

Kubernetes Online Endpoints

Answer

Batch Endpoints

Answer

Azure Container Instances (ACI)

Question 9

What is the 'Workspace' in Azure Machine Learning?

Accepted Answer

The top-level resource for managing all ML assets.. The Workspace is the top-level resource for Azure Machine Learning, providing a centralized place to manage all artifacts, computes, and jobs.

Answer

A specific model deployment.

Answer

A virtual machine for training.

Answer

A storage container for datasets.

Question 10

Which TWO types of compute can be used for training in Azure Machine Learning?

Accepted Answer

Compute Cluster.. Compute Clusters and Compute Instances are the standard training targets.

Answer

Key Vault.

Answer

Web App.

Answer

Azure SQL.