Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 261:

  HOTSPOT

  You manage an Azure Machine Learning workspace by using the Python SDK v2.

  You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format.

  You must configure an autoscaling compute target and a data asset for the job.

  You need to configure the resources for the job.

  Which resource configuration should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

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

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets

• Question 262:

  You need to select a feature extraction method.

  Which method should you use?

  A. Mutual information
  B. Mood's median test
  C. Kendall correlation
  D. Permutation Feature Importance
  C. Kendall correlation

  ---

  Explanation

  In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter ), is a statistic used to measure the ordinal association between two measured quantities.

  It is a supported method of the Azure Machine Learning Feature selection.

  Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.

  References:

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

• Question 263:

  You create an Azure Machine Learning workspace named workspace1. You create a Python SDK v2 notebook to perform custom model training in workspace1.

  You need to run the notebook from Azure Machine Learning Studio in workspace1.

  What should you provision first?

  A. default storage account
  B. real-time endpoint
  C. Azure Machine Learning compute cluster
  D. Azure Machine Learning compute instance
  D. Azure Machine Learning compute instance

  ---

  Explanation

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/tutorial-1st-experiment-hello-world

• Question 264:

  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:

  <pdf2txt-u>age,city,income,home_owner</pdf2txt-u> <pdf2txt-u>21,Chicago,50000,0</pdf2txt-u> <pdf2txt-u>35,Seattle,120000,1</pdf2txt-u> <pdf2txt-u>23,Seattle,65000,0</pdf2txt-u> <pdf2txt-u>45,Seattle,130000,1</pdf2txt-u> <pdf2txt-u>18,Chicago,48000,0</pdf2txt-u>

  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:

  

  

  ---

  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 265:

  You use an Azure Machine Learning workspace.

  You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.

  What should you do?

  A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true
  B. Deploy the model to Azure Container Instances. During deployment, set the auth_enabled parameter of the target configuration object to true
  C. Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabled parameter of the target configuration object to true
  D. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabled parameter of the target configuration object to true
  A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

  ---

  Explanation

  To control token authentication, use the token_auth_enabled parameter when you create or update a deployment

  Token authentication is disabled by default when you deploy to Azure Kubernetes Service.

  Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods: key-based: A static key is used to authenticate to the web service.

  token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.

  Incorrect Answers:

  C: Token authentication isn't supported when you deploy to Azure Container Instances.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service

• Question 266:

  HOTSPOT

  You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.

  Original and scaled data is shown in the following image.

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: StandardScaler

  The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.

  Example:

  

  All features are now on the same scale relative to one another.

  Box 2: Min Max Scaler

  

  Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.

  Box 3: Normalizer

  References:

  http://benalexkeen.com/feature-scaling-with-scikit-learn/

• Question 267:

  You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access.

  Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students.

  You need to ensure that students can run Python-based data visualization code.

  Which Azure tool should you use?

  A. Anaconda Data Science Platform
  B. Azure BatchAl
  C. Azure Notebooks
  D. Azure Machine Learning Service
  C. Azure Notebooks

  ---

  Explanation

  References:

  https://notebooks.azure.com/

• Question 268:

  HOTSPOT

  You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment:

  

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: No

  max_total_runs (50 here)

  The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.

  Box 2: Yes

  Policy EarlyTerminationPolicy

  The early termination policy to use. If None - the default, no early termination policy will be used.

  Box 3: No

  Discrete hyperparameters are specified as a choice among discrete values. choice can be:

  1. one or more comma-separated values

  2. a range object

  3. any arbitrary list object

  https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 269:

  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 train and register a machine learning model.

  You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.

  You need to deploy the web service.

  Solution: Create an AciWebservice instance.

  Set the value of the ssl_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use only auth_enabled = TRUE

  Note: Key-based authentication.

  Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.

  deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE)

  https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

• Question 270:

  You write a Python script that processes data in a comma-separated values (CSV) file.

  You plan to run this script as an Azure Machine Learning experiment.

  The script loads the data and determines the number of rows it contains using the following code:

  

  You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes.

  Which code should you use?

  A. run.upload_file(`row_count', `./data.csv')
  B. run.log(`row_count', rows)
  C. run.tag(`row_count', rows)
  D. run.log_table(`row_count', rows)
  E. run.log_row(`row_count', rows)
  B. run.log(`row_count', rows)

  ---

  Explanation

  Log a numerical or string value to the run with the given name using log(name, value, description=''). 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)

  Incorrect Answers:

  E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.

  Example: run.log_row("Y over X", x=1, y=0.4)

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run