In this article i am gone to share Prepare for DP-100: Data Science on Microsoft Azure Exam | Week 4 | Practice exam covering Course 3: Build and operate machine learning solutions with Azure Machine Learning Quiz Answers with you..

---

Also visit:  Practice exam covering Course 2: Create no-code predictive models with Azure Machine Learning Quiz Answers

---

 

Practice exam covering Course 3: Build and operate machine learning solutions with Azure Machine Learning Quiz Answers

Question 1)
You create a new Azure subscription. No resources are provisioned in the subscription. You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

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

Question 2)
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?

• Azure Databricks
• Azure Data Lake Analytics
• Azure Container Instances
• Apache Spark for HDInsight

Question 3)
The finance team asked you to train a model using data in an Azure Storage blob container named finance-data.
You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

How should you complete the code?

Datastore = Datastore.<add answer here> (workspace = ws,
datastore_name = ‘finance_datastore’,
container_name = ‘finance-data’,
account_name = ‘fintrainingdatastorage’,
account_key = ‘FdhIWHDaiwh2…’
<add answer here>

• register_azure_blob_container, create_if_not_exists = False
• register_azure_data_lake, overwrite = False
• register_azure_data_lake, create_if_not_exists = False
• register_azure_blob_container, overwrite = True

Question 4)
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.
You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription. You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training.
You must minimize data movement. What should you do?

• 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.
• Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
• Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
• Create an Azure Data Lake store and move the bird photographs to the store.

Question 5)
You train a machine learning model. You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead. Which compute target should you use?

• Azure Kubernetes Service (AKS) inference cluster
• attached Azure Databricks cluster
• Azure Machine Learning compute cluster
• Azure Container Instance (ACI)

Question 6)
You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.
You train the model and prepare the real-time pipeline for deployment.
You need to publish the inference pipeline as a web service. Which compute type should you use?

• The existing Machine Learning Compute resource
• A new Machine Learning Compute resource
• HDInsight
• Azure Kubernetes Services
• Azure Databricks

Question 7)
You deploy a model as an Azure Machine Learning real-time web service using the following code.

# ws, model, inference_config, and deployment_config defined previously
service = Model.deploy(ws, ‘classification-service’, [model], inference_config, deployment_config)
service.wait_for_deployment(True)
The deployment fails.
You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?

• service.serialize()
• service.get_logs()
• service.state
• service.update_deployment_state()

Question 8)
You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing.
You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution.

• run(mini_batch)
• init()
• main()
• batch()
• score(mini_batch)

Question 9)
Yes or No?
You train a classification model by using a logistic regression algorithm. 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 need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer. Does the solution meet the goal?

• Yes
• No

Question 10)
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?

• 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.
• Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
• View the explanations for the registered model in Azure ML studio.

 

Question 11)
You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer.

You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file:
{ “workspace_name” : “ml-workspace” }
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace. You need to configure the config.json file to connect to the workspace from the Python environment. Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.

• Key
• Login
• Subscription_id
• Resource_group
• Region

Question 12)
A coworker registers a datastore in a Machine Learning services workspace by using the following code:
Datastore.register_azure_blob_container(workspace=ws,

datastore_name=‘demo_datastore’,
container_name=‘demo_datacontainer’,
account_name=’demo_account’,
account_key=’0A0A0A-0A00A0A-0A0A0A0A0A0’
create_if_not_exists=True)

You need to write code to access the datastore from a notebook. How should you complete the code segment?

import azureml.core
from azureml.core import Workspace, Datastore

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

• DataStore, ws, demo_datastore
• Run, experiment, demo_datastore
• Experiment, run, demo_account
• Run, ws, demo_datastore

 

Question 13)
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 need to configure compute resources for model inferencing. Which compute type should you use?

• Azure Container Instance
• Field Programmable Gate Array
• Machine Learning Compute
• Azure Kubernetes Service

Question 14)
An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.
The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed.
You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort. What should you advise the IT Operations team to do?

• 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.
• Increase the VM size of nodes in the compute cluster where the web service is deployed.
• 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 15)
You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.
When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead. You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

• Restart the compute cluster where the pipeline experiment is configured to run.
• Set the outputs property of each step in the pipeline to True.
• Use a PipelineData object that references a datastore other than the default datastore.
• Set the regenerate_outputs property of the pipeline to True.
• Set the allow_reuse property of each step in the pipeline to False.

Question 16)
You register a model that you plan to use in a batch inference pipeline.

The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step and the runs must process six input files each time the inferencing function is called.
You need to configure the pipeline. Which configuration setting should you specify in the ParallelRunConfig object for the ParallelRunStep step?

• process_count_per_node= “6”
• node_count= “6”
• error_threshold= “6”
• mini_batch_size= “6”

 

Question 17)
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
You need to reconfigure the compute resource. What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

• Run the refresh_state() method of the BatchCompute class in the Python SDK.
• Use the Azure Machine Learning designer.
• Use the Azure Machine Learning studio.
• Run the update method of the AmlCompute class in the Python SDK.
• Use the Azure portal.

Question 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.

Machine Learning compute
You need to determine which compute environment to use for the following scenarios:

1. Run an Azure Machine Learning Designer training pipeline.
2. Deploying a web service from the Azure Machine Learning Designer.

Which compute types should you use?

• 1 nb_server, 2 aks_cluster
• 1 mlc_cluster, 2 aks_cluster
• 1 nb_server, 2 mlc_cluster
• 1 mlc_cluster, 2 nb_server

Question 19)
A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file is stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

/sales
/01-2019
/sales.csv
/02-2019
/sales.csv
/03-2019
/sales.csv
…

At the end of each month, a new folder with that month’s sales file is added to the sales folder. You plan to use the sales data to train a machine learning model based on the following requirements:
– You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
– You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
– You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace. What should you do?

• 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 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.
• 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.