DP-100問題集が集中する問題と実際のMicrosoft DP-100試験内容の同期しています。

DP-100問題集中のすべての問題は、すべて実際の試験の中からコピーして得られた試験のすべての知識点をカバーする。

DP-100問題集は、毎週実際の試験内容と一度チェックし、試験をカバーするすべての真実の問題を確保する。

DP-100問題集中提供の答えは、正確率は96.75%に達する。

DP-100問題集の中問題の答えは、すべてこの分野で認証された複数の専門家によって提供され、高い正確性を保証する。

毎回DP-100問題集は、一部の問題の答えは知識点の出所を付け加えて、答えの正確性を理解して検証するのに便利です。

DP-100 問題集は毎週更新を維持し、試験問題の最新版を確保する。

毎週実際の試験内容を完全に整合し、変化のある内容を発見して、直ちにDP-100 問題集の中に更新する。

実際にMicrosoft DP-100試験に参加する前に、killtestに最新の問題を求めて、よく考えて、試験に挑戦するのはもっと自信がある。

DP-100問題集の内容は様々な試験題目を含んでおり、問題数もMicrosoft DP-100 問題集の全集合である。

DP-100問題集，単選問題、多選問題及び問答問題を含む。

DP-100問題集，含まれる問題の数量は実際の試験で、試験問題のすべての集合である。

DP-100模擬練習の役割，自分が試験に挑戦する能力を客観的に評価することにある。

killtest提供する練習問題（102題），現在のMicrosoft DP-100試験の60%最新の真題を含む。

普段は欠片化時間で随所に訪れ、模擬練習を始め、試験の準備時間を短縮することができます。

模擬練習の場合、自分の知識レベルをリアルに反映して、試験に参加するかどうかを評価基準とします。

模擬練習を通じて、DP-100問題集の真実性、信頼性を十分に理解し、承認した後に、再購入の最新の真題を考慮します。

Question No :1

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.

You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.

You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

What should you do?

 A.Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

 B.Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

 C.Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

 D.Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.

 E.Always run the experiment with an Estimator by using the default packages.

正解: C

Explanation:

If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.

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

Question No : 2

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?

 A.a new Machine Learning Compute resource

 B.Azure Kubernetes Services

 C.HDInsight

 D.the existing Machine Learning Compute resource

 E.Azure Databricks

正解: B

Explanation:

Azure Kubernetes Service (AKS) can be used real-time inference.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

Question No : 3

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?

 A.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.

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

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

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

正解: B

Explanation:

The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes

Question No : 4

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. NOTE: Each correct selection is worth one point.

 A.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.

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

 C.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.

 D.Navigate to Azure Machine Learning studio and create a workspace.

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

正解: ABCD

Explanation:

B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning. Example:

{"type": "Microsoft.MachineLearningServices/workspaces",

…

C: You can create a workspace for Azure Machine Learning with Azure CLI

Install the machine learning extension.

Create a resource group: az group create --name <resource-group-name> --location <location>

To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g <resource-group-name>

D: You can create and manage Azure Machine Learning workspaces in the Azure portal.

Question No : 5

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. NOTE: Each correct selection is worth one point.

 A.Use the Azure Machine Learning studio.

 B.Run the update method of the AmlCompute class in the Python SD

 C.Use the Azure portal.

 D.Use the Azure Machine Learning designer.

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

正解: AB

Explanation:

A: You can manage assets and resources in the Azure Machine Learning studio.

B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.

C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.

Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)

Question No : 6

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?

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

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

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

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

正解: B

Explanation:

Configure logging with Azure Machine Learning studio

You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights: