Free DP-100: Designing and Implementing a Data Science Solution on Azure (beta) Exam Questions and Answers

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.

Work out the matching from the exhibit, then reveal.

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

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. An IT department creates the following Azure resource groups and resources: The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace. You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed. You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics. Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace. Run the training script as an experiment on the aks-cluster compute target. Does the solution meet the goal?

• AA. Yes
• BB. No

You have a dataset that includes confidential data. You use the dataset to train a model. You must use a differential privacy parameter to keep the data of individuals safe and private. You need to reduce the effect of user data on aggregated results. What should you do?

• AA. Decrease the value of the epsilon parameter to reduce the amount of noise added to the data
• BB. Increase the value of the epsilon parameter to decrease privacy and increase accuracy
• CC. Decrease the value of the epsilon parameter to increase privacy and reduce accuracy
• DD. Set the value of the epsilon parameter to 1 to ensure maximum privacy

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.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form. You start by creating a linear regression model. You need to evaluate the linear regression model. Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC. Does the solution meet the goal?

• AA. Yes
• BB. No

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.

Work out the matching from the exhibit, then reveal.

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

You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training. You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value. You need to modify the inference pipeline to meet the requirement. What should you do?

• AA. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.
• BB. Delete the dataset from the training pipeline and recreate the real-time inference pipeline.
• CC. Delete the Web Service Input module from the inference pipeline.
• DD. Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.

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.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are analyzing a numerical dataset which contains missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Remove the entire column that contains the missing data point. Does the solution meet the goal?

• AA. Yes
• BB. No

You are creating a binary classification by using a two-class logistic regression model. You need to evaluate the model results for imbalance. Which evaluation metric should you use?

• AA. Relative Absolute Error
• BB. AUC Curve
• CC. Mean Absolute Error
• DD. Relative Squared Error