Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 351:

  You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored. You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

  

  You have the following script.

  

  You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore. Which code should you use to configure the estimator?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  E. Option E

  Correct Answer: B

  Besides passing the dataset through the inputs parameter in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your

  training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform.

  Example:

  from azureml.train.sklearn import SKLearn

  script_params = {

  # mount the dataset on the remote compute and pass the mounted path as an argument to the training script

  '--data-folder': mnist_ds.as_named_input('mnist').as_mount(), '--regularization': 0.5

  }

  est = SKLearn(source_directory=script_folder,

  script_params=script_params,

  compute_target=compute_target,

  environment_definition=env,

  entry_script='train_mnist.py')

  # Run the experiment

  run = experiment.submit(est)

  run.wait_for_completion(show_output=True)

  Incorrect Answers:

  A: Pandas DataFrame not used.

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

• Question 352:

  You write five Python scripts that must be processed in the order specified in Exhibit A ?which allows the same modules to run in parallel, but will wait for modules with dependencies.

  You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

  

  You need to create the pipeline shown. Assume all relevant imports have been done. Which Python code segment should you use?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  Correct Answer: A

  The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step

• Question 353:

  You use the following code to run a script as an experiment in Azure Machine Learning:

  

  You must identify the output files that are generated by the experiment run.

  You need to add code to retrieve the output file names.

  Which code segment should you add to the script?

  A. files = run.get_properties()

  B. files= run.get_file_names()

  C. files = run.get_details_with_logs()

  D. files = run.get_metrics()

  E. files = run.get_details()

  Correct Answer: B

  You can list all of the files that are associated with this run record by called run.get_file_names()

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

• Question 354:

  A set of CSV files contains sales records. All the CSV files have the same data schema.

  Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

  

  At the end of each month, a new folder with that month's sales file is added to the sales folder.

  You plan to use the sales data to train a machine learning model based on the following requirements:

  1.

  You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.

  2.

  You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.

  3.

  You must register the minimum number of datasets possible.

  You need to register the sales data as a dataset in Azure Machine Learning service workspace.

  What should you do?

  A. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.

  B. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

  C. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm- yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month- specific dataset for experiments.

  D. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.

  Correct Answer: B

  Specify the path.

  Example:

  The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.

  from azureml.core import Workspace, Datastore, Dataset

  datastore_name = 'your datastore name'

  # get existing workspace

  workspace = Workspace.from_config()

  # retrieve an existing datastore in the workspace by name datastore = Datastore.get(workspace, datastore_name)

  # create a TabularDataset from 3 file paths in datastore datastore_paths = [(datastore, 'weather/2018/11.csv'),

  (datastore, 'weather/2018/12.csv'),

  (datastore, 'weather/2019/*.csv')]

  weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)

• Question 355:

  You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

  You need to identify the tasks you will be able to perform in the workspace.

  Which three tasks will you be able to perform? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Create a Compute Instance and use it to run code in Jupyter notebooks.

  B. Create an Azure Kubernetes Service (AKS) inference cluster.

  C. Use the designer to train a model by dragging and dropping pre-defined modules.

  D. Create a tabular dataset that supports versioning.

  E. Use the Automated Machine Learning user interface to train a model.

  Correct Answer: ABD

  Incorrect Answers:

  C, E: The UI is included the Enterprise edition only.

  Reference:

  https://azure.microsoft.com/en-us/pricing/details/machine-learning/

• Question 356:

  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

  Correct Answer: B

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

  Note: Python printing/logging example:

  logging.info(message)

  Destination: Driver logs, Azure Machine Learning designer

  Reference:

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

• Question 357:

  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

  Correct Answer: B

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

  Note: Python printing/logging example:

  logging.info(message)

  Destination: Driver logs, Azure Machine Learning designer

  Reference:

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

• Question 358:

  You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.

  You must predict the fare of a taxi trip.

  You need to select performance metrics to correctly evaluate the regression model.

  Which two metrics can you use? Each correct answer presents a complete solution?

  NOTE: Each correct selection is worth one point.

  A. a Root Mean Square Error value that is low

  B. an R-Squared value close to 0

  C. an F1 score that is low

  D. an R-Squared value close to 1

  E. an F1 score that is high

  F. a Root Mean Square Error value that is high

  Correct Answer: AD

  RMSE and R2 are both metrics for regression models.

  A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  Incorrect Answers:

  C, E: F-score is used for classification models, not for regression models.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

• Question 359:

  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

  Correct Answer: A

  Python printing/logging example: logging.info(message)

  Destination: Driver logs, Azure Machine Learning designer

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

• Question 360:

  You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.

  You need to select a pre-configured DSVM to support the frameworks.

  What should you create?

  A. Data Science Virtual Machine for Windows 2012

  B. Data Science Virtual Machine for Linux (CentOS)

  C. Geo AI Data Science Virtual Machine with ArcGIS

  D. Data Science Virtual Machine for Windows 2016

  E. Data Science Virtual Machine for Linux (Ubuntu)

  Correct Answer: E

  Caffe2 and PyTorch is supported by Data Science Virtual Machine for Linux. Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4. Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch. Incorrect Answers:

  D: Caffe2 and PytOCH are only supported in the Data Science Virtual Machine for Linux.

  References: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview