Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 431:

  DRAG DROP

  You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute1 by using a terminal window from workspace1. You create a file named "requirements.txt" containing Python dependencies to include Jupyter.

  You need to add a new Jupyter kernel to compute1.

  Which four commands should you use? 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:

  

  

  ---

  Explanation:

  Step 1: conda create --n python_env

  Add new kernels

  To add a new Jupyter kernel to the compute instance:

  1. Use the terminal window to create a new environment. For example, the code below creates newenv:

  conda create --name newenv

  Step 2: conda activate python_env 2. Activate the environment. For example, after creating newenv:

  conda activate newenv Step 3: conda install -r "requirements.txt"

  3. Install pip and ipykernel package to the new environment and create a kernel for that conda env

  conda install pip

  conda install ipykernel python -m ipykernel install --user --name newenv --display-name "Python (newenv)"

  Step 4: ipython kernel install --user --name-"python_env"

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal

• Question 432:

  You are solving a classification task.

  You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.

  You need to configure the k parameter for the cross-validation.

  Which value should you use?

  A. k=0.5
  B. k=10
  C. k=5
  D. k=1
  C. k=5

• Question 433:

  HOTSPOT

  You plan to implement an Azure Machine Learning solution.

  You have the following requirements:

  1. Run a Jupyter notebook to interactively train a machine learning model.

  2. Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.

  You need to select a development technique for each requirement.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 434:

  You are performing a filter-based feature selection for a dataset to build a multi-class classifier by using Azure Machine Learning Studio.

  The dataset contains categorical features that are highly correlated to the output label column.

  You need to select the appropriate feature scoring statistical method to identify the key predictors.

  Which method should you use?

  A. Kendall correlation
  B. Spearman correlation
  C. Chi-squared
  D. Pearson correlation
  C. Chi-squared

• Question 435:

  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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run

  import pandas as pd

  run = Run.get_context()

  data = pd.read_csv('data.csv')

  label_vals = data['label'].unique()

  # Add code to record metrics here

  run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  run.log_table('Label Values', label_vals)

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the run_log function to log the contents in label_vals:

  for label_val in label_vals:

  run.log('Label Values', label_val)

  https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 436:

  HOTSPOT

  You manage an Azure subscription that contains the following resources:

  

  You plan to implement a solution that will automatically trigger the retraining of the model implemented by MLPipeline1. The trigger must be invoked if data drift is detected in Dataset1.

  You need to select the components to invoke and run the solution. The solution must minimize coding implementation and maintenance efforts.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 437:

  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?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Use the Multiple Imputation by Chained Equations (MICE) method.

  References:

  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

• Question 438:

  HOTSPOT

  You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.

  You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training.

  The script will accept a parameter designating the dataset

  You need to complete the 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 439:

  You run a script as an experiment in Azure Machine Learning.

  You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run.

  You need to download the log files to a local folder for review.

  Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. run.get_details()
  B. run.get_file_names()
  C. run.get_metrics()
  D. run.download_files(output_directory='./runfiles')
  E. run.get_all_logs(destination='./runlogs')
  D. run.download_files(output_directory='./runfiles')
  E. run.get_all_logs(destination='./runlogs')

  ---

  Explanation

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

• Question 440:

  You manage an Azure Machine Learning workspace.

  The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.

  A Synapse Spark Compute is currently attached and uses system-assigned identity.

  You need to use Python code to update the Synapse Spark Compute to use a user- assigned identity.

  Solution: Pass the UserAssignedldentity class object to the SynapseSparkCompute class.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes