DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

You create a binary classification model.

You need to evaluate the model performance.

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

NOTE: Each correct selection is worth one point.

You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.

You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset

You need to complete the script.

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

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace named workspace1 and a Data Science Virtual Machine (DSVM) named DSMV1.

You must an experiment in DSMV1 by using a Jupiter notebook and Python SDK v2 code. You must store metrics and artifacts in workspace 1 You start by creating Python SCK v2 code to import ail required packages.

You need to implement the Python SOK v2 code to store metrics and article in workspace1.

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 the correctly order.

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?

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:

An authenticated connection must not be required for testing.

The deployed model must perform with low latency during inferencing.

The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.

You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.

Which compute resources should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.

The model will be retrained each month as new data is available.

You must register the model for use in a batch inference pipeline.

You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.

You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

You need to perform the task that must be completed before you can add the new kernel.

Solution: Create an environment.

Does the solution meet the goal?

You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).

The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

You need to configure the module.

Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.