Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 231:

  You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.

  You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use

  the service must not be required to submit this value.

  You need to modify the inference pipeline to meet the requirement.

  What should you do?

  A. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.

  B. Delete the dataset from the training pipeline and recreate the real-time inference pipeline.

  C. Delete the Web Service Input module from the inference pipeline.

  D. Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.

  Correct Answer: A

  By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset removing target variable column is connected to the same port as the output of the Web Service Intput module.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

• Question 232:

  You plan to run a Python script as an Azure Machine Learning experiment. The script contains the following code:

  import os, argparse, globfrom 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_folderfile_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 ScriptRunConfigobject 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()]

  Correct Answer: A

  If you have structured data not yet registered as a dataset, create a TabularDataset and use it directly in your training script for your local or remote experiment.

  To load the TabularDataset to pandas DataFrame

  df = dataset.to_pandas_dataframe()

  Note: TabularDataset represents data in a tabular format created by parsing the provided file or list of files.

  Reference:

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

• Question 233:

  You create and register a model in an Azure Machine Learning workspace.

  You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStepto score input data using the model. You must specify a value for the ParallelRunConfig compute_targetsetting

  of the pipeline step.

  You need to create the compute target.

  Which class should you use?

  A. BatchCompute

  B. AdlaCompute

  C. AmlCompute

  D. AksCompute

  Correct Answer: C

  Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace. The compute_target target is of AmlCompute or string.

  Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)

• Question 234:

  You have the following code. The code prepares an experiment to run a script:

  A. run = script_experiment.start_logging()

  B. run = Run(experiment=script_experiment)

  C. ws.get_run(run_id=experiment.id)

  D. run = script_experiment.submit(config=script_config)

  Correct Answer: D

  The experiment class submit method submits an experiment and return the active created run.

  Syntax: submit(config, tags=None, **kwargs)

  Reference:

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

• Question 235:

  You use the following code to define the steps for a pipeline:

  from azureml.core import Workspace, Experiment, Runfrom azureml.pipeline.core import Pipelinefrom azureml.pipeline.steps import PythonScriptStep

  ws = Workspace.from_config()

  . . . step1 = PythonScriptStep(name="step1", ...)step2 = PythonScriptsStep(name="step2", ...)pipeline_steps = [step1, step2]

  You need to add code to run the steps.

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

  NOTE: Each correct selection is worth one point.

  A. experiment = Experiment(workspace=ws,

  name='pipeline-experiment')

  run = experiment.submit(config=pipeline_steps)

  B. run = Run(pipeline_steps)

  C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)experiment = Experiment(workspace=ws,

  name='pipeline-experiment')

  run = experiment.submit(pipeline)

  D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)run = pipeline.submit(experiment_name='pipeline-experiment')

  Correct Answer: CD

  After you define your steps, you build the pipeline by using some or all of those steps.

  # Build the pipeline. Example:

  pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

  # Submit the pipeline to be run

  pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)

  Reference:

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

• Question 236:

  You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python. You must add a statement that retrieves the names of the logs and outputs generated by the script.

  You need to reference a Python class object from the SDK for the statement. Which class object should you use?

  A. Run

  B. ScriptRunConfig

  C. Workspace

  D. Experiment

  Correct Answer: A

  A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

  The run Class get_all_logs method downloads all logs for the run to a directory.

  Incorrect Answers:

  A: A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

  B: A ScriptRunConfig packages together the configuration information needed to submit a run in Azure ML, including the script, compute target, environment, and any distributed job-specific configs.

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

• Question 237:

  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')

  Correct Answer: AE

  The run Class get_all_logs method downloads all logs for the run to a directory.

  The run Class get_details gets the definition, status information, current log files, and other details of the run.

  Incorrect Answers:

  B: The run get_file_names list the files that are stored in association with the run.

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

• Question 238:

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

  The script must read files from a hierarchy of folders. The files will be passed to the script as a dataset argument.

  You must specify an appropriate mode for the dataset argument.

  Which mode can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. to_pandas_dataframe ()

  B. as_download()

  C. as_upload()

  D. as mount ()

  Correct Answer: B

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py

• Question 239:

  You use the Azure Machine Learning Python SDK to define a pipeline to train a model.

  The data used to train the model is read from a folder in a datastore.

  You need to ensure the pipeline runs automatically whenever the data in the folder changes.

  What should you do?

  A. Set the regenerate_outputs property of the pipeline to True

  B. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline

  C. Create a PipelineParameter with a default value that references the location where the training data is stored

  D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property

  Correct Answer: D

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline

• Question 240:

  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 AksWebservice instance.

  Set the value of the auth_enabled property to False.

  Set the value of the token_auth_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  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)

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