Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 111:

  DRAG DROP

  You set up a machine learning workflow as an automated process. You have an Owner role in an Azure subscription that contains the Azure Machine Learning workspace.

  You must set up an authentication method that allows an automated process to authenticate to the workspace without requiring user interaction.

  You need to set up the authentication for the Azure Machine Learning workspace.

  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.

  NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

  Select and Place:

  

  

• Question 112:

  You define a datastore named ml-data for an Azure Storage blob container. In the container, you have a folder named train that contains a file named data.csv. You plan to use the file to train a model by using the Azure Machine Learning SDK.

  You plan to train the model by using the Azure Machine Learning SDK to run an experiment on local compute.

  You define a DataReference object by running the following code:

  

  You need to load the training data.

  Which code segment should you use?

  

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

  ---

  Explanation

  Example:

  data_folder = args.data_folder

  # Load Train and Test data train_data = pd.read_csv(os.path.join(data_folder, 'data.csv'))

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

• Question 113:

  You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.

  You have the following data available for model building:

  1. Video recordings of sporting events

  2. Transcripts of radio commentary about events

  3. Logs from related social media feeds captured during sporting events

  You need to select an environment for creating the model.

  Which environment should you use?

  A. Azure Cognitive Services
  B. Azure Data Lake Analytics
  C. Azure HDInsight with Spark MLib
  D. Azure Machine Learning Studio
  A. Azure Cognitive Services

  ---

  Explanation

  Azure Cognitive Services expand on Microsoft's evolving portfolio of machine learning APIs and enable developers to easily add cognitive features ?such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding ?into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/welcome

• Question 114:

  HOTSPOT

  You have a Python data frame named salesData in the following format:

  

  The data frame must be unpivoted to a long data format as follows:

  

  You need to use the pandas.melt() function in Python to perform the transformation.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: dataFrame

  Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source]

  Where frame is a DataFrame

  Box 2: shop

  Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.

  Box 3: ['2017','2018']

  value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

  Example:

  df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, ... 'B': {0: 1, 1: 3, 2: 5}, ... 'C': {0: 2, 1: 4, 2: 6}}) pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])

  A variable value

  0 a B 1

  1 b B 3

  2 c B 5

  3 a C 2

  4 b C 4

  5 c C 6 References:

  https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

• Question 115:

  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 to run an experiment that trains a classification model.

  You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

  

  You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

  You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Use a solution with logging.info(message) instead.

  Note: Python printing/logging example:

  logging.info(message) Destination: Driver logs, Azure Machine Learning designer

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

• Question 116:

  HOTSPOT

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

  You must create a compute cluster in the workspace. The compute cluster must run workloads and property handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.

  The cluster definition includes the following properties and values:

  names="mlcluster"

  size="STANDARD_DS3_v2"

  min_instances=1

  max_instances=4

  tier="dedicated"

  The cost of the compute resources must be minimized when a workload is active or idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.

  You need to modify the cluster properties to minimize the cost of compute resources.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 117:

  You manage an Azure Machine Learning workspace.

  You use Azure Machine Learning Python SDK v2 to configure a trigger to schedule a pipeline job.

  You need to create a time- based schedule with recurrence pattern.

  Which two properties must you use to successfully configure the trigger?

  Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  A. interval
  B. start_time
  C. schedule
  D. time_zone
  E. frequency
  A. interval
  E. frequency

• Question 118:

  HOTSPOT

  You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.

  The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.

  Business managers in your organization want to see the importance of the features in the model.

  You need to print out the model features and their relative importance in an output that looks similar to the following.

  

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: from_run_id from_run_id(workspace, experiment_name, run_id) Create the client with factory method given a run ID.

  Returns an instance of the ExplanationClient.

  Parameters

  Workspace Workspace An object that represents a workspace. experiment_name str The name of an experiment. run_id str A GUID that represents a run.

  Box 2: list_model_explanations

  list_model_explanations returns a dictionary of metadata for all model explanations available.

  Returns

  A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time

  Box 3: explanation

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py

• Question 119:

  You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

  When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.

  You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.

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

  NOTE: Each correct selection is worth one point.

  A. Use a PipelineData object that references a datastore other than the default datastore.
  B. Set the regenerate_outputs property of the pipeline to True.
  C. Set the allow_reuse property of each step in the pipeline to False.
  D. Restart the compute cluster where the pipeline experiment is configured to run.
  E. Set the outputs property of each step in the pipeline to True.
  B. Set the regenerate_outputs property of the pipeline to True.
  C. Set the allow_reuse property of each step in the pipeline to False.

  ---

  Explanation

  B: If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.

  C: Keep the following in mind when working with pipeline steps, input/output data, and step reuse.

  If data used in a step is in a datastore and allow_reuse is True, then changes to the data change won't be detected. If the data is uploaded as part of the snapshot (under the step's source_directory), though this is not recommended, then the hash will change and will trigger a rerun.

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

  https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

• Question 120:

  You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.

  You must update the content of the downstream data source of pipeline1 and run the pipeline again

  You need to ensure the new run of pipeline1 fully processes the updated content.

  Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes