Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 141:

  DRAG DROP

  You are planning to host practical training to acquaint staff with Docker for Windows.

  Staff devices must support the installation of Docker.

  Which of the following are requirements for this installation? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  Correct Answer:

  

  Reference:

  https://docs.docker.com/toolbox/toolbox_install_windows/

  https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

  https://docs.docker.com/docker-for-windows/install/

• Question 142:

  DRAG DROP You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files. Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named

  predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.

  You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.

  Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source may be used once, more than once, or not at all. You may need to drag the

  split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point. Select and Place:

  

  Correct Answer:

  

  Box 1: training-dataset

  Baseline dataset - usually the training dataset for a model.

  Box 2: predictions-dataset

  Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified. The monitor will compare the baseline and target datasets.

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

• Question 143:

  DRAG DROP

  You have several machine learning models registered in an Azure Machine Learning workspace.

  You must use the Fairlearn dashboard to assess fairness in a selected model.

  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:

  

  Step 1: Select a model feature to be evaluated.

  Step 2: Select a binary classification or regression model.

  Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.

  Example:

  model_dict = {}

  lr_reg_id = register_model("fairness_logistic_regression", lr_predictor)

  model_dict[lr_reg_id] = lr_predictor

  svm_reg_id = register_model("fairness_svm", svm_predictor)

  model_dict[svm_reg_id] = svm_predictor

  Step 3: Select a metric to be measured

  Precompute fairness metrics.

  Create a dashboard dictionary using Fairlearn's metrics package.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

• Question 144:

  DRAG DROP

  You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for three species of penguin in comma-delimited format.

  The model must be optimized for area under the received operating characteristic curve performance metric, averaged for each class.

  You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.

  Which five 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 and select a new dataset by uploading he command-delimited file of penguin data.

  Step 2: Select the Classification task type

  Step 3: Set the Primary metric configuration setting to Accuracy.

  The available metrics you can select is determined by the task type you choose.

  Primary metrics for classification scenarios:

  Post thresholded metrics, like accuracy, average_precision_score_weighted, norm_macro_recall, and precision_score_weighted may not optimize as well for datasets which are very small, have very large class skew (class imbalance), or

  when the expected metric value is very close to 0.0 or 1.0. In those cases, AUC_weighted can be a better choice for the primary metric.

  Step 4: Configure the automated machine learning run by selecting the experiment name, target column, and compute target

  Step 5: Run the automated machine learning experiment and review the results.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train

• Question 145:

  DRAG DROP

  You use Azure Machine Learning to deploy a model as a real-time web service.

  You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.

  Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  Correct Answer:

  

  Box 1: init()

  The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the

  model(s).

  Box 2: run()

  Reference:

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

• Question 146:

  DRAG DROP

  You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.

  You need to use the designer to create a pipeline that includes steps to perform the following tasks:

  1.

  Select the training features using the pandas filter method.

  2.

  Train a model based on the naive_bayes.GaussianNB algorithm.

  3.

  Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1;

  Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  Correct Answer:

  

• Question 147:

  DRAG DROP

  You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a

  classification model.

  You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.

  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:

  

  Step 1: Create and start a Compute Instance

  To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.

  There are four kinds of compute resource you can create:

  Compute Instances: Development workstations that data scientists can use to work with data and models.

  Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.

  Inference Clusters: Deployment targets for predictive services that use your trained models.

  Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.

  Step 2: Create and run a training pipeline..

  After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline

  Step 3: Create and run a real-time inference pipeline

  After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its

  features. This pipeline will form the basis for a predictive service that you can publish for applications to use.

  Reference:

  https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

• Question 148:

  DRAG DROP

  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.

  Select and Place:

  

  Correct Answer:

  

• Question 149:

  DRAG DROP

  You create a multi-class image classification deep learning model.

  The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.

  You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.

  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: Publish the pipeline.

  To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.

  Step 2: Retrieve the pipeline ID.

  Needed for the schedule.

  Step 3: Create a ScheduleRecurrence..

  To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.

  First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:

  recurrence = ScheduleRecurrence(frequency="Minute", interval=15)

  Step 4: Define an Azure Machine Learning pipeline schedule..

  Example, continued:

  recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",

  description="Based on time",

  pipeline_id=pipeline_id,

  experiment_name=experiment_name,

  recurrence=recurrence)

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

• Question 150:

  DRAG DROP

  You create machine learning models by using Azure Machine Learning.

  You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.

  You need to select the appropriate compute types.

  Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or

  scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  Correct Answer:

  

  Box 1: Attached compute

  

  Box 2: Inference cluster Box 3: Training cluster Box 4: Attached compute