Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 361:

  You create a workspace by using Azure Machine Learning Studio.

  You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.

  You need to reset the state of the notebook.

  Which three actions should you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Stop the current kernel.
  B. Change the compute.
  C. Reset the compute.
  D. Navigate to another section of the workspace.
  E. Change the current kernel.
  B. Change the compute.
  C. Reset the compute.
  E. Change the current kernel.

  ---

  Explanation

  These actions will reset the notebook state and will reset all variables in the notebook.

  Action, Result

  * (B) Switch compute

  Notebook automatically uses the new compute.

  * (C) Reset compute

  Starts again when you try to run a cell

  * (E) Change the kernel

  Notebook uses new kernel

  * Stop compute

  No cells will run

  * Open notebook in Jupyter or JupyterLab Notebook opened in a new tab.

  Incorrect:

  These actions won't change the notebook state or the values of any variables in the notebook:

  Action, Result

  * (not A) Stop the kernel

  Stops any running cell. Running a cell will automatically restart the kernel.

  * (not D) Navigate to another workspace section Running cells are stopped.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks

• Question 362:

  HOTSPOT

  You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).

  The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

  You need to configure the module.

  Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 363:

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

  The script contains the following code:

  import os, argparse, glob

  from azureml.core import Run

  parser = argparse.ArgumentParser() parser.add_argument('--input-data', type=str, dest='data_folder') args = parser.parse_args() data_path = args.data_folder file_paths = glob.glob(data_path + "/*.jpg")

  You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source.

  You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.

  Which code segment should you use?

  A. arguments = ['--input-data', ds.to_pandas_dataframe()]
  B. arguments = ['--input-data', ds.as_mount()]
  C. arguments = ['--data-data', ds]
  D. arguments = ['--input-data', ds.as_download()]
  B. arguments = ['--input-data', ds.as_mount()]

  ---

  Explanation

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

• Question 364:

  HOTSPOT

  You are analyzing the asymmetry in a statistical distribution.

  The following image contains two density curves that show the probability distribution of two datasets.

  

  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: Positive skew

  Positive skew values means the distribution is skewed to the right.

  Box 2: Negative skew

  Negative skewness values mean the distribution is skewed to the left.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics

• Question 365:

  You use Azure Machine Learning to train a model based on a dataset named dataset1.

  You define a dataset monitor and create a dataset named dataset2 that contains new data.

  You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.

  Which method of the DataDriftDetector class should you use?

  A. run
  B. get
  C. backfill
  D. update
  A. run

  ---

  Explanation

  A backfill run is used to see how data changes over time.

  https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector

• Question 366:

  You are implementing a machine learning model to predict stock prices.

  The model uses a PostgreSQL database and requires GPU processing.

  You need to create a virtual machine that is pre-configured with the required tools.

  What should you do?

  A. Create a Data Science Virtual Machine (DSVM) Windows edition.
  B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
  C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
  D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
  E. Create a Data Science Virtual Machine (DSVM) Linux edition.
  C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.

  ---

  Explanation

  DSVM on Linux as postgreSQL is only supported on Linux VMS.

  https://docs.microsoft.com/en-us/azure/virtual-machines/linux/postgresql-install

• Question 367:

  DRAG DROP

  You use a training pipeline in the Azure Machine Learning designer. You register a datastore named ds1. The datastore contains multiple training data files. You use the Import Data module with the configured datastore.

  You need to retrain a model on a different set of data files.

  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:

  

  

• Question 368:

  You train and register an Azure Machine Learning model.

  You plan to deploy the model to an online endpoint.

  You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

  Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key. Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  Authentication mode: The authentication method for the endpoint. Choose between key-based authentication and Azure Machine Learning token-based authentication. A key doesn't expire, but a token does expire.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints

• Question 369:

  HOTSPOT

  You are using Azure Machine Learning to train machine learning models. You need to compute target on which to remotely run the training script.

  You run the following Python code:

  

  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: Yes

  The compute is created within your workspace region as a resource that can be shared with other users.

  Box 2: Yes

  It is displayed as a compute cluster.

  View compute targets 1. To see all compute targets for your workspace, use the following steps:

  2. Navigate to Azure Machine Learning studio.

  3. Under Manage, select Compute.

  4. Select tabs at the top to show each type of compute target.

  

  Box 3: Yes

  min_nodes is not specified, so it defaults to 0.

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

• Question 370:

  You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.

  You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).

  Which of the following actions should you take?

  A. You should consider including Rattle.
  B. You should consider including TensorFlow.
  C. You should consider including Theano.
  D. You should consider including Chainer.
  B. You should consider including TensorFlow.