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Coursera Microsoft Azure Data Scientist Associate (DP-100) • STUDY MODE

Practice Quiz

QUESTION 1 OF 18

You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. 

Region

Key

Resource_groupCorrect Answer

Subscription_idCorrect Answer

QUESTION 2 OF 18

You are developing a data science workspace that uses an Azure Machine Learning service. You need to select a compute target to deploy the workspace. What should you use?

Apache Spark for HDInsight

Azure Databricks

Azure Data Lake Analytics

Azure Container InstancesCorrect Answer

QUESTION 3 OF 18

A coworker registers a datastore in a Machine Learning services workspace by using the following code:

import azureml.core

from azureml.core import Workspace, Datastore

ws = Workspace.from_config()

datastore = <add answer here> .get( <add answer here>, ‘<add answer here>’)

Run, experiment, demo_datastore

Experiment, run, demo_account

Run, ws, demo_datastore

DataStore, ws, demo_datastoreCorrect Answer

QUESTION 4 OF 18

A set of CSV files contains sales records. All the CSV files have the same data schema.

Create a tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.

Create a tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.

Create a new tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.

Create a tabular dataset that references the datastore and specifies the path ‘sales/*/sales.csv’, register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

QUESTION 5 OF 18

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.You must deploy the model to a context that allows for real-time GPU-based inferencing. 

Azure Kubernetes ServiceCorrect Answer

Azure Container Instance

Field Programmable Gate Array

Machine Learning Compute

QUESTION 6 OF 18

You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.

HDInsight

Azure Kubernetes ServicesCorrect Answer

A new Machine Learning Compute resource

Azure Databricks

The existing Machine Learning Compute resource

QUESTION 7 OF 18

You deploy a model as an Azure Machine Learning real-time web service using the following code.

service.update_deployment_state()

service.serialize()

service.get_logs()Correct Answer

service.state

QUESTION 8 OF 18

You register a model that you plan to use in a batch inference pipeline.

error_threshold= “6”

node_count= “6”

process_count_per_node= “6”

mini_batch_size= “6”Correct Answer

QUESTION 9 OF 18

Yes or No?

YesCorrect Answer

QUESTION 10 OF 18

You deploy a real-time inference service for a trained model.

Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.Correct Answer

View the log files generated by the experiment used to train the model.

Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.

View the explanations for the registered model in Azure ML studio.

QUESTION 11 OF 18

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.

Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.

Create an Azure Data Lake store and move the bird photographs to the store.

Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.Correct Answer

Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.

Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.

QUESTION 12 OF 18

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

Increase the VM size of nodes in the compute cluster where the web service is deployed.

Increase the node count of the compute cluster where the web service is deployed

Increase the minimum node count of the compute cluster where the web service is deployed.Correct Answer

Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.

QUESTION 13 OF 18

You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

Restart the compute cluster where the pipeline experiment is configured to run.

Use a PipelineData object that references a datastore other than the default datastore.

Set the allow_reuse property of each step in the pipeline to False.

Set the outputs property of each step in the pipeline to True .Correct Answer

Set the regenerate_outputs property of the pipeline to True.Correct Answer

QUESTION 14 OF 18

You train and register a model in your Azure Machine Learning workspace.

batch()

score(mini_batch)

main()

run(mini_batch)Correct Answer

init()Correct Answer

QUESTION 15 OF 18

An organization uses Azure Machine Learning service and wants to expand their use of machine learning. You have the following compute environments. The organization does not want to create another compute environment.

1 nb_server, 2 mlc_cluster

1 mlc_cluster, 2 nb_server

1 nb_server, 2 aks_cluster

1 mlc_cluster, 2 aks_clusterCorrect Answer

QUESTION 16 OF 18

You must be able to explain the model’s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions. 

Yes

NoCorrect Answer

QUESTION 17 OF 18

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows: – Minimum nodes: 2 – Maximum nodes: 4. You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values: – Minimum nodes: 0 – Maximum nodes: 8

Run the refresh_state() method of the BatchCompute class in the Python SDK.

Use the Azure Machine Learning Designer.

Use the Azure portal.

Run the update method of the AmlCompute class in the Python SDK.Correct Answer

Use the Azure Machine Learning Studio.Correct Answer

QUESTION 18 OF 18

You create a new Azure subscription. No resources are provisioned in the subscription. You need to create an Azure Machine Learning workspace. 

Navigate to Azure Machine Learning studio and create a workspace.

Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.Correct Answer

Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with –name and –location parameters, and then the az ml workspace create function, specifying Cw and Cg parameters for the workspace name and resource group.Correct Answer

Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.Correct Answer

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Ready to test your recall?

You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. 

💡Select all 2 correct answers before submitting (0 of 2 selected).

Region

Key

Resource_group

Subscription_id

How confident are you in this answer?

Practice Quiz

You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer.&nbsp;

You are developing a data science workspace that uses an Azure Machine Learning service. You need to select a compute target to deploy the workspace. What should you use?

A coworker registers a datastore in a Machine Learning services workspace by using the following code:

A set of CSV files contains sales records. All the CSV files have the same data schema.

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.You must deploy the model to a context that allows for real-time GPU-based inferencing.&nbsp;

You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.

You deploy a model as an Azure Machine Learning real-time web service using the following code.

You register a model that you plan to use in a batch inference pipeline.

Yes or No?

You deploy a real-time inference service for a trained model.

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

You train and register a model in your Azure Machine Learning workspace.

An organization uses Azure Machine Learning service and wants to expand their use of machine learning. You have the following compute environments. The organization does not want to create another compute environment.

You must be able to explain the model&#8217;s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.&nbsp;

You create a new Azure subscription. No resources are provisioned in the subscription. You need to create an Azure Machine Learning workspace.&nbsp;

Ready to test your recall?

You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer.

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.You must deploy the model to a context that allows for real-time GPU-based inferencing.

You must be able to explain the model’s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.

You create a new Azure subscription. No resources are provisioned in the subscription. You need to create an Azure Machine Learning workspace.