Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 131:

  DRAG DROP

  You have an Azure Machine Learning workspace. You are running an experiment on your local computer.

  You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.

  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

• Question 132:

  DRAG DROP

  You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.

  When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.

  You need to debug the service on the local workstation before deploying the service to production.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Step 1: Install Docker on the workstation

  Prerequisites include having a working Docker installation on your local system.

  Build or download the dockerfile to the compute node.

  Step 2: Create an AksWebservice deployment configuration and deploy the model to it

  To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.

  # If deploying to a cluster configured for dev/test, ensure that it was created with enough

  # cores and memory to handle this deployment configuration. Note that memory is also used by

  # things such as dependencies and AML components.

  deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)

  service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target)

  service.wait_for_deployment(show_output = True)

  print(service.state)

  print(service.get_logs())

  Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it

  To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.

  Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.

  During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for

  the service, and then sends data to it.

  Incorrect Answers:

  AciWebservice: The types of web services that can be deployed are LocalWebservice, which will deploy a model locally, and AciWebservice and AksWebservice, which will deploy a model to Azure Container Instances (ACI) and Azure

  Kubernetes Service (AKS), respectively.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local

• Question 133:

  You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).

  The remaining 1,000 rows represent class 1 (10 percent).

  The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

  You need to configure the module.

  Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: 300

  You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).

  Box 2: 5

  We should use 5 data rows.

  Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The

  distance between any two cases is measured by combining the weighted vectors of all features.

  By increasing the number of nearest neighbors, you get features from more cases.

  By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

• Question 134:

  DRAG DROP

  You create an Azure Machine Learning workspace.

  You need to use the shared file system of the workspace to store a clone of a private Git repository.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

• Question 135:

  DRAG DROP

  You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.

  All new versions of the model registered in the workspace must automatically be deployed to the target environment.

  You need to configure Azure Pipelines to deploy the model.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Step 1: Create an Azure DevOps project

  Step 2: Create a release pipeline

  1.

  Sign in to your Azure DevOps organization and navigate to your project.

  2.

  Go to Pipelines, and then select New pipeline.

  Step 3: Install the Machine Learning extension for Azure Pipelines You must install and configure the Azure CLI and ML extension.

  Step 4: Create a service connection How to set up your service connection Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.

  

  

  Note: How to enable model triggering in a release pipeline

  1.

  Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.

  2.

  Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.

  Reference: https://marketplace.visualstudio.com/items?itemName=ms-air-aiagility.vss-services-azureml https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning

• Question 136:

  DRAG DROP

  You are using a Git repository to track work in an Azure Machine Learning workspace.

  You need to authenticate a Git account by using SSH.

  Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Authenticate your Git Account with SSH: Step 1: Generating a public/private key pair Generate a new SSH key

  1.

  Open the terminal window in the Azure Machine Learning Notebook Tab.

  2.

  Paste the text below, substituting in your email address.

  ssh-keygen -t rsa -b 4096 -C "[email protected]"

  This creates a new ssh key, using the provided email as a label.

  > Generating public/private rsa key pair.

  Step 2: Add the public key to the Git Account

  In your terminal window, copy the contents of your public key file. Step 3: Clone the Git repository by using an SSH repository URL

  1.

  Copy the SSH Git clone URL from the Git repo.

  2.

  Paste the url into the git clone command below, to use your SSH Git repo URL. This will look something like:

  git clone [email protected]:GitUser/azureml-example.git Cloning into 'azureml-example'.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration

• Question 137:

  DRAG DROP

  You build a binary classification model using the Azure Machine Learning Studio Two-Class Neural Network module.

  You are preparing to configure the Tune Model Hyperparameters module for the purpose of tuning accuracy for the model.

  Which of the following are valid parameters for the Two-Class Neural Network module? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  Correct Answer:

  

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network

• Question 138:

  DRAG DROP

  You have been tasked with evaluating the performance of a binary classification model that you created.

  You need to choose evaluation metrics to achieve your goal.

  Which of the following are the metrics you would choose? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  Correct Answer:

  

  The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

  Note: A very natural question is: ‘Out of the individuals whom the model, how many were classified correctly (TP)?’

  This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

• Question 139:

  DRAG DROP

  You are in the process of constructing a regression model.

  You would like to make it a Poisson regression model. To achieve your goal, the feature values need to meet certain conditions.

  Which of the following are relevant conditions with regards to the label data? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  Correct Answer:

  

  Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the

  following conditions:

  The response variable has a Poisson distribution.

  Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

  A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression

• Question 140:

  DRAG DROP

  You have been tasked with moving data into Azure Blob Storage for the purpose of supporting Azure Machine Learning.

  Which of the following can be used to complete your task? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  Correct Answer:

  

  You can move data to and from Azure Blob storage using different technologies: Azure Storage-Explorer AzCopy Python SSIS

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob