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.

run.get_all_logs(destination='./runlogs')

run.download_files(output_directory='./runfiles')

run.get_all_logs(destination='./runlogs')

You have the following code. The code prepares an experiment to run a script: The experiment must be run on local computer using the default environment.You need to add code to start the experiment and run the script.Which code segment should you use?

run = script_experiment.submit(config=script_config)

run = script_experiment.start_logging()

run = Run(experiment=script_experiment)

ws.get_run(run_id=experiment.id)

run = script_experiment.submit(config=script_config)

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 PythonScriptStepws = 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.

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

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

experiment = Experiment(workspace=ws,name='pipeline-experiment')run = experiment.submit(config=pipeline_steps)

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

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

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 Runparser = 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 ScriptRunConfig object for the experiment and pass the ds dataset as an argument.Which code segment should you use?

arguments = ['--input-data', ds.to_pandas_dataframe()]

arguments = ['--input-data', ds.as_mount()]

arguments = ['--data-data', ds]

arguments = ['--input-data', ds.as_download()]

You need to implement a feature engineering strategy for the crowd sentiment local models.What should you do?

Apply a linear discriminant analysis.

Apply an analysis of variance (ANOVA).

Apply a Pearson correlation coefficient.

Apply a Spearman correlation coefficient.

Apply a linear discriminant analysis.

You need to implement a model development strategy to determine a user's tendency to respond to an ad.Which technique should you use?

Use a Relative Expression Split module to partition the data based on centroid distance.

Use a Relative Expression Split module to partition the data based on distance travelled to the event.

Use a Split Rows module to partition the data based on distance travelled to the event.

Use a Split Rows module to partition the data based on centroid distance.

You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit.Which technique should you use?

Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.

Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.

Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.

Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.

You need to select a feature extraction method.Which method should you use?

Fisher Linear Discriminant Analysis

You use an Azure Machine Learning workspace.You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.What should you do?

Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

Deploy the model to Azure Container Instances. During deployment, set the auth_enabled parameter of the target configuration object to true

Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabled parameter of the target configuration object to true

Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabled parameter of the target configuration object to true

You have a Jupyter Notebook that contains Python code that is used to train a model.You must create a Python script for the production deployment. The solution must minimize code maintenance.Which two actions should you perform? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Refactor the Jupyter Notebook code into functions

Define a main() function in the Python script

Refactor the Jupyter Notebook code into functions

Save each function to a separate Python file

Define a main() function in the Python script

Remove all comments and functions from the Python script

You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files.You must publish the batch inference pipeline as a service that can be scheduled to run every night.You need to select an appropriate compute target for the inference service.Which compute target should you use?

Azure Machine Learning compute cluster

Azure Machine Learning compute instance

Azure Machine Learning compute cluster

Azure Kubernetes Service (AKS)-based inference cluster

Azure Container Instance (ACI) compute target

Microsoft DP-100 Practice Test 6 - Free Online Exam Prep & Questions

Question 1 / 40

HOTSPOT

You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:

Microsoft DP-100 image Question 92 89192 10022024015825000000

The data is collected every 12 hours: noon and midnight.

You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.

You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.

You need to configure the automated machine learning run.

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

NOTE: Each correct selection is worth one point.

Become a Premium Member for full access

Unlock Premium Member

Microsoft DP-100 Practice Test 6

Question

Microsoft DP-100 Practice Tests

Practice Tests