Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 151:

  DRAG DROP

  You are building an experiment using the Azure Machine Learning designer.

  You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.

  You need to determine the Area Under the Curve (AUC) of the model.

  Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Step 1: Train Model Two-Class Boosted Decision Tree First, set up the boosted decision tree model.

  1.

  Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.

  2.

  Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module. The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.

  3.

  Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).

  This portion of the experiment now looks something like this:

  

  Step 2: Score Model Score and evaluate the models You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.

  Add the Score Model modules

  1.

  Find the Score Model module and drag it onto the canvas.

  2.

  Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.

  3.

  Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

  

  Step 3: Evaluate Model

  To evaluate the two scoring results and compare them, you use an Evaluate Model module.

  1.

  Find the Evaluate Model module and drag it onto the canvas.

  2.

  Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.

  3.

  Connect the other Score Model module to the right input port.

  

• Question 152:

  DRAG DROP

  You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format:

  age,city,income,home_owner21,Chicago,50000,035,Seattle,120000,123,Seattle,65000,045,Seattle,130000,118,Chicago,48000,0

  You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:

  1.

  the number of observations in the dataset

  2.

  a box plot of income by home_owner

  3.

  a dictionary containing the city names and the average income for each city You need to use the appropriate logging methods of the experiment's run object to log the required information. How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment 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: log

  The number of observations in the dataset.

  run.log(name, value, description='')

  Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result

  being considered a vector of that metric.

  Example: run.log("accuracy", 0.95)

  Box 2: log_image

  A box plot of income by home_owner.

  log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.

  Example: run.log_image("ROC", plot=plt)

  Box 3: log_table

  A dictionary containing the city names and the average income for each city.

  log_table: Log a dictionary object to the run with the given name.

• Question 153:

  DRAG DROP

  You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.

  You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.

  Which three pipeline steps should you run 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: Configure a DataTransferStep() to fetch new image data…

  Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.

  Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.

  The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.

  Reference:

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

• Question 154:

  DRAG DROP

  An organization uses Azure Machine Learning service and wants to expand their use of machine learning.

  You have the following compute environments. The organization does not want to create another compute environment.

  

  You need to determine which compute environment to use for the following scenarios.

  Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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: nb_server

  

  Box 2: mlc_cluster With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.

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

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

• Question 155:

  You are building an intelligent solution using machine learning models.

  The environment must support the following requirements:

  Data scientists must build notebooks in a cloud environment

  Data scientists must use automatic feature engineering and model building in machine learning pipelines. Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation. Notebooks must be exportable to be version controlled locally.

  You need to create the environment.

  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: Create an Azure HDInsight cluster to include the Apache Spark Mlib library

  Step 2: Install Microsot Machine Learning for Apache Spark

  You install AzureML on your Azure HDInsight cluster.

  Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit

  (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.

  Step 3: Create and execute the Zeppelin notebooks on the cluster

  Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.

  Notebooks must be exportable to be version controlled locally.

  References:

  https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook

  https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

• Question 156:

  DRAG DROP

  You configure a Deep Learning Virtual Machine for Windows.

  You need to recommend tools and frameworks to perform the following:

  1.

  Build deep neural network (DNN) models

  2.

  Perform interactive data exploration and visualization

  Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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: Vowpal Wabbit

  Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.

  Box 2: PowerBI Desktop

  Power BI Desktop is a powerful visual data exploration and interactive reporting tool

  BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration

• Question 157:

  You have a model with a large difference between the training and validation error values.

  You must create a new model and perform cross-validation.

  You need to identify a parameter set for the new model using Azure Machine Learning Studio.

  Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or 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: Split data Box 2: Partition and Sample Box 3: Two-Class Boosted Decision Tree Box 4: Tune Model Hyperparameters

  Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.

  We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

• Question 158:

  DRAG DROP

  You are producing a multiple linear regression model in Azure Machine Learning Studio.

  Several independent variables are highly correlated.

  You need to select appropriate methods for conducting effective feature engineering on all the data.

  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: Use the Filter Based Feature Selection module

  Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.

  The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.

  Step 2: Build a counting transform

  A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.

  Step 3: Test the hypothesis using t-Test

  References:

  https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform

• Question 159:

  DRAG DROP

  You are producing a multiple linear regression model in Azure Machine learning Studio.

  Several independent variables are highly correlated.

  You need to select appropriate methods for conducting elective feature engineering on all the data;

  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 160:

  DRAG DROP

  You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.

  You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.

  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.

  Step 2: Add a dataset to the experiment

  Step 3: Add a Split Data module to create training and test dataset.

  To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.

  Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.

  Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance