2022 Latest Free Microsoft DP-100 Sample Questions & Answers

2022 Latest Free Microsoft DP-100 Sample Questions & Answers

Now you can get the most update DP-100 questions and answers to practice for your Designing and Implementing a Data Science Solution on Azure exam. You’ve come to the correct place if you’re preparing for the DP-100 test and want to get ahead. Our experienced IT lecturers produced DP-100 exam dumps, which includes exam questions and answers and contains the most recent Microsoft DP-100 exam questions which ensure you prepare the DP-100 exam easily.

To assess your level of preparation, take this DP-100 practice test first!

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1. You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.

You create a variable that references the dataset using the following code:

training_ds = workspace.datasets.get("training_data")

You define an estimator to run the script.

You need to set the correct property of the estimator to ensure that your script can access the training.data dataset

Which property should you set?

2. You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.

The training dataset that you are using is highly unbalanced.

You need to evaluate the classification model.

Which primary metric should you use?


You need to define an evaluation strategy for the crowd sentiment models.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

4. You are building a machine learning model for translating English language textual content into French

language textual content.

You need to build and train the machine learning model to learn the sequence of the textual content.

Which type of neural network should you use?

5. You retrain an existing model.

You need to register the new version of a model while keeping the current version of the model in the registry.

What should you do?


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()


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.

7. 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?

8. You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.

You need to provide application developers with the information they need to consume the endpoint.

Which two values should you provide to application developers? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

9. You are building a binary classification model by using a supplied training set.

The training set is imbalanced between two classes.

You need to resolve the data imbalance.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.


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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