Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 221:

  You construct a machine learning experiment via Azure Machine Learning Studio.

  You would like to split data into two separate datasets.

  Which of the following actions should you take?

  A. You should make use of the Split Data module.

  B. You should make use of the Group Categorical Values module.

  C. You should make use of the Clip Values module.

  D. You should make use of the Group Data into Bins module.

  Correct Answer: D

  The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

• Question 222:

  You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.

  You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).

  Which of the following actions should you take?

  A. You should consider including Rattle.

  B. You should consider including TensorFlow.

  C. You should consider including Theano.

  D. You should consider including Chainer.

  Correct Answer: B

• Question 223:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Data Science Virtual Machine (DSVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes

  B. No

  Correct Answer: A

  In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs).

  PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for macOS (OS X) version 10.6 and newer – Windows (with installers available for 64-bit version; tested on latest versions and back to Windows 2012 R2.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 224:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Deep Learning Virtual Machine (DLVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes

  B. No

  Correct Answer: B

  DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 225:

  You have a Python script that executes a pipeline. The script includes the following code:

  from azureml.core import Experiment

  pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

  You want to test the pipeline before deploying the script.

  You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

  Which code segment should you add to the test script?

  A. pipeline_run.get.metrics()

  B. pipeline_run.wait_for_completion(show_output=True)

  C. pipeline_param = PipelineParameter(name="stdout",default_value="console")

  D. pipeline_run.get_status()

  Correct Answer: B

  wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.

  Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)

  Parameter: show_output

  Indicates whether to show the run output on sys.stdout.

• Question 226:

  You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework. Which of the following DSVM should you create?

  A. Windows Server 2012 DSVM

  B. Windows Server 2016 DSVM

  C. Ubuntu 16.04 DSVM

  D. CentOS 7.4 DSVM

  Correct Answer: C

  Caffe2 is supported by Data Science Virtual Machine for Linux.

  Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4.

  However, only the DSVM on Ubuntu is preconfigured for Caffe2.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 227:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes

  B. No

  Correct Answer: B

  The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 228:

  You use the Azure Machine Learning designer to create and run a training pipeline. You then create a real-time inference pipeline.

  You must deploy the real-time inference pipeline as a web service.

  What must you do before you deploy the real-time inference pipeline?

  A. Run the real-time inference pipeline.

  B. Create a batch inference pipeline.

  C. Clone the training pipeline.

  D. Create an Azure Machine Learning compute cluster.

  Correct Answer: D

  You need to create an inferencing cluster.

  Deploy the real-time endpoint

  After your AKS service has finished provisioning, return to the real-time inferencing pipeline to complete deployment.

  1.

  Select Deploy above the canvas.

  2.

  Select Deploy new real-time endpoint.

  3.

  Select the AKS cluster you created.

  4.

  Select Deploy.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

• Question 229:

  You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.

  You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.

  You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.

  What should you do?

  A. From DB-cluster, configure the Advanced Logging option.

  B. From DB-workspace, configure the Link Azure ML workspace option.

  C. From ML-workspace, create an attached compute.

  D. From ML-workspace, create a compute cluster.

  Correct Answer: B

  Connect your Azure Databricks and Azure Machine Learning workspaces:

  Linking your ADB workspace to your Azure Machine Learning workspace enables you to track your experiment data in the Azure Machine Learning workspace.

  To link your ADB workspace to a new or existing Azure Machine Learning workspace

  1.

  Sign in to Azure portal.

  2.

  Navigate to your ADB workspace's Overview page.

  3.

  Select the Link Azure Machine Learning workspace button on the bottom right.

  

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks

• Question 230:

  You are planning to register a trained model in an Azure Machine Learning workspace.

  You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.

  You need to register the model.

  Which parameter should you use?

  A. description

  B. model_framework

  C. tags

  D. properties

  Correct Answer: D

  azureml.core.Model.properties:

  Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model