Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 71:

  HOTSPOT

  Complete the sentence by selecting the correct option in the answer area.

  Hot Area:

  

  Correct Answer:

  

  Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.

  The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entities and their attributes, and is contained in a single text file. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff

• Question 72:

  HOTSPOT

  A biomedical research company plans to enroll people in an experimental medical treatment trial.

  You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.

  The model returns different performance metrics for people from different ethnic groups.

  You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.

  Which technique and constraint should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Grid Search

  Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms: ExponentiatedGradient, GridSearch, and ThresholdOptimizer.

  Note: The Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms types:

  1.

  Reduction: These algorithms take a standard black-box machine learning estimator (e.g., a LightGBM model) and generate a set of retrained models using a sequence of re-weighted training datasets.

  2.

  Post-processing: These algorithms take an existing classifier and the sensitive feature as input.

  Box 2: Demographic parity

  The Fairlearn open-source package supports the following types of parity constraints: Demographic parity, Equalized odds, Equal opportunity, and Bounded group loss.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

• Question 73:

  HOTSPOT

  You create an Azure Machine Learning workspace.

  You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.

  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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: create_from_datasets

  The create_from_datasets method creates a new DataDriftDetector object from a baseline tabular dataset and a target time series dataset.

  Box 2: backfill

  The backfill method runs a backfill job over a given specified start and end date.

  Syntax: backfill(start_date, end_date, compute_target=None, create_compute_target=False)

  Incorrect Answers:

  List and update do not have datetime parameters.

  Reference:

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

• Question 74:

  HOTSPOT

  You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.

  You have the following Python code segment in the Azure Machine Learning workspace:

  import mlflowimport mlflow.azuremlimport azureml.mlflowimport azureml.core

  from azureml.core import Workspace

  subscription_id = 'subscription_id'resourse_group = 'resource_group_name'workspace_name = 'workspace_name'

  ws = Workspace.get(name=workspace_name,subscription_id=subscription_id,resource_group=resource_group)

  experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}"mlflow.set_experiment(experimentName)

  uri = ws.get_mlflow_tracking_uri()mlflow.set_tracking_uri(uri)

  Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: No

  The Workspace.get method loads an existing workspace without using configuration files.

  ws = Workspace.get(name="myworkspace", subscription_id='', resource_group='myresourcegroup')

  Box 2: Yes

  MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.

  The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.

  Box 3: Yes

  Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

• Question 75:

  HOTSPOT

  You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage

  account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.

  You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.

  You need to configure store1.

  What should you do? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Regenerate the keys of account1.

  Azure Blob Storage support authentication through Account key or SAS token.

  To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal

  Box 2: Update the authentication for store1.

  For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks,

  use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.

  Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.

  The following operations are disabled by default in a virtual network:

  Preview data in the studio.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

• Question 76:

  HOTSPOT

  You create a script for training a machine learning model in Azure Machine Learning service.

  You create an estimator by running the following 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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Yes

  Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.

  Box 2: Yes

  script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.

  Box 3: No

  Box 4: Yes

  The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.

• Question 77:

  HOTSPOT

  You are running Python code interactively in a Conda environment. The environment includes all required Azure Machine Learning SDK and MLflow packages.

  You must use MLflow to log metrics in an Azure Machine Learning experiment named mlflow-experiment.

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

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  In the following code, the get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.

  mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  Box 2: mlflow.set_experiment(experiment_name)

  Set the MLflow experiment name with set_experiment() and start your training run with start_run().

  Box 3: mlflow.start_run()

  Box 4: mlflow.log_metric

  Then use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

• Question 78:

  HOTSPOT

  You train a classification model by using a decision tree algorithm.

  You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.

  from interpret.ext.blackbox import TabularExplainer

  

  You need to explain the predictions made by the model for all classes by determining the importance of all features.

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Yes

  TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).

  Box 2: Yes

  To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.

  Box 3: No

  TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml

• Question 79:

  HOTSPOT

  You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.

  You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

  You run the following 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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: No

  FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.

  Box 2: Yes

  to_path() gets a list of file paths for each file stream defined by the dataset.

  Box 3: Yes

  TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.

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

  Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are

  essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.

  Reference:

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

• Question 80:

  HOTSPOT

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

  

  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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: forcasting

  Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.

  Box 2: temperature

  The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

  Box 3: observation_time

  time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated.

  Please use forecasting_parameters instead.

  Box 4: 7

  "predicts temperature over the next seven days"

  max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.

  Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.

  Box 5: 50

  "For the initial round of training, you want to train a maximum of 50 different models."

  Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig