DP-100 Learning Practice Questions

You have an Azure Machine Learning workspace that contains a training cluster and an inference cluster. You plan to create a classification model by using the Azure Machine Learning designer. You need to ensure that client applications can submit data as HTTP requests and receive predictions as responses. 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.

Match each item to its target.

Correct answer: 1 = Create a pipeline that trains a classification model and run the pipeline on the compute cluster; 2 = Create a batch inference pipeline and run the pipeline on the compute cluster; 3 = Deploy a service to the inference cluster

You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service. You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment. You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update. What should you do?

• AA. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
• BB. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
• CC. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
• DD. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
• EE. Create a local web service deployment configuration and deploy the model to a local Docker container.

You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices. You must submit an experiment that runs this script in the Azure Machine Learning workspace. The following compute resources are available: a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time. Which resources should the data scientist use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Match each item to its target.

Correct answer: Run code to submit the experiment = the ds-workstation notebook VM; Run the training script = the gpu-compute target

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 in 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: 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?

• AA. 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.
• BB. 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.
• CC. 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.
• DD. 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.