Practice Free DP-100 Exam Online Questions

HOTSPOT

You create an Azure Machine Learning workspace.

You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment. The logged image must be available from the images tab in Azure Machine Learning Studio.

You need to complete the script.

Which code segments should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

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.

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Correct Answer:

Explanation:

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:

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.

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

HOTSPOT

You collect data from a nearby weather station.

You have a pandas dataframe named weather_df that 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.

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Correct Answer:

Explanation:

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

HOTSPOT

You need to identify the methods for dividing the data according, to the testing requirements.

Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Sampling

You arc creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.

You need to select a data cleaning method.

Which method should you use?

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HOTSPOT

You are using the Azure Machine Learning designer to transform a dataset by using an Execute Python Script component and custom code.

You need to define the method signature for the Execute Python Script component and return value type.

What should you define? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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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 mlflow

import mlflow.azureml

import azureml.mlflow

import 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.

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Correct Answer:

Explanation:

Box 1: No

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

ws = Workspace.get(name="myworkspace",

subscription_id='<azure-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

HOTSPOT

You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

You are reviewing the results of experiment1 by using the following code segment:

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

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Correct Answer:

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 True.

Deploy the model to the service.

Does the solution meet the goal?

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You create an Azure Machine learning workspace.

You are use the Azure Machine -learning Python SDK v2 to define the search space for concrete hyperparafneters. The hyper parameters must consist of a list of predetermined, comma-separated.

You need to import the class from the azure ai ml. sweep package used to create the list of values.

Which class should you import?

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