Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 101:

  HOTSPOT

  You have an Azure Machine Learning workspace

  You plan to use the Azure Machine Learning SDK for Python v1 to submit a job to run a training script.

  You need to complete the script to ensure that it will execute the training script.

  How should you complete the script? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point

  

  

• Question 102:

  You register a file dataset named csv_folder that references a folder. The folder includes multiple comma- separated values (CSV) files in an Azure storage blob container.

  You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

  

  You have the following code:

  

  You need to pass the dataset to ensure that the script can read the files it references.

  Which code segment should you insert to replace the code comment?

  A. inputs=[file_dataset.as_named_input('training_files')],
  B. inputs=[file_dataset.as_named_input('training_files').as_mount()],
  C. inputs=[file_dataset.as_named_input('training_files').to_pandas_dataframe ()],
  D. script_params={'--training_files': file_dataset},
  B. inputs=[file_dataset.as_named_input('training_files').as_mount()],

  ---

  Explanation

  Example:

  from azureml.train.estimator import Estimator

  script_params = {

  # to mount files referenced by mnist dataset

  '--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(), '--regularization': 0.5

  } est = Estimator(source_directory=script_folder, script_params=script_params, compute_target=compute_target, environment_definition=env, entry_script='train.py')

  https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml

• Question 103:

  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 a data scientist using Azure Machine Learning Studio.

  You need to normalize values to produce an output column into bins to predict a target column.

  Solution: Apply an Equal Width with Custom Start and Stop binning mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 104:

  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:

  

  

  ---

  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 105:

  You are developing a hands-on workshop to introduce Docker for Windows to attendees.

  You need to ensure that workshop attendees can install Docker on their devices.

  Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Microsoft Hardware-Assisted Virtualization Detection Tool
  B. Kitematic
  C. BIOS-enabled virtualization
  D. VirtualBox
  E. Windows 10 64-bit Professional
  C. BIOS-enabled virtualization
  E. Windows 10 64-bit Professional

  ---

  Explanation

  C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.

  Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

  

  E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.

  References:

  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/

• Question 106:

  HOTSPOT

  You create an Azure Machine Learning workspace. You train a classification model by using automated machine learning (automated ML) in Azure Machine Learning studio. The training data contains multiple classes that have significantly different numbers of samples.

  You must use a metric type to avoid labeling negative samples as positive and an averaging method that will minimize the class imbalance.

  You need to configure the metric type and the averaging method.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Precision

  Metric type

  Precision is the ability of a model to avoid labeling negative samples as positive.

  Box 2: macro

  Averaging method

  While each averaging method has its benefits, one common consideration when selecting the appropriate method is class imbalance. If classes have different numbers of samples, it might be more informative to use a macro average where minority classes are given equal weighting to majority classes.

  Note: Scikit-learn provides several averaging methods, three of which automated ML exposes: macro, micro, and weighted.

  Macro - Calculate the metric for each class and take the unweighted average

  Micro - Calculate the metric globally by counting the total true positives, false negatives, and false positives (independent of classes).

  Weighted - Calculate the metric for each class and take the weighted average based on the number of samples per class.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml

• Question 107:

  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 using Azure Machine Learning Studio to perform feature engineering on a dataset.

  You need to normalize values to produce a feature column grouped into bins.

  Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 108:

  You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

  The experiment fails.

  You need to troubleshoot the failed experiment.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  A. Use the get_metrics() method of the run object to retrieve the experiment run logs.
  B. Use the get_details_with_logs() method of the run object to display the experiment run logs.
  C. View the log files for the experiment run in the experiment folder.
  D. View the logs for the experiment run in Azure Machine Learning studio.
  E. Use the get_output() method of the run object to retrieve the experiment run logs.
  B. Use the get_details_with_logs() method of the run object to display the experiment run logs.
  D. View the logs for the experiment run in Azure Machine Learning studio.

  ---

  Explanation

  Use get_details_with_logs() to fetch the run details and logs created by the run.

  You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.

  Incorrect Answers:

  A: You can view the metrics of a trained model using run.get_metrics().

  E: get_output() gets the output of the step as PipelineData.

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

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs

• Question 109:

  HOTSPOT

  You create an Azure Machine Learning dataset. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.

  You must upload the script and associated libraries as a script bundle.

  You need to configure the Execute Python Script component.

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

  NOTE Each correct selection is worth one point.

  

  

• Question 110:

  You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.

  You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

  

  You have the following script.

  

  You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.

  Which code should you use to configure the estimator?

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  B. Option B

  ---

  Explanation

  Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.

  Example:

  from azureml.train.sklearn import SKLearn

  script_params = {

  # mount the dataset on the remote compute and pass the mounted path as an argument to the training script

  '--data-folder': mnist_ds.as_named_input('mnist').as_mount(), '--regularization': 0.5

  } est = SKLearn(source_directory=script_folder, script_params=script_params, compute_target=compute_target, environment_definition=env, entry_script='train_mnist.py')

  # Run the experiment

  run = experiment.submit(est)

  run.wait_for_completion(show_output=True)

  Incorrect Answers:

  A: Pandas DataFrame not used.

  https://docs.microsoft.com/es-es/azure/machine-learning/how-to-train-with-datasets