(WEB-BASED) DP-100 PRACTICE TEST - FEEL THE ACTUAL TEST ENVIRONMENT

(Web-Based) DP-100 Practice Test - Feel The Actual Test Environment

(Web-Based) DP-100 Practice Test - Feel The Actual Test Environment

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Achieving the Microsoft DP-100 certification is a great way to showcase your data science skills and enhance your career prospects. Designing and Implementing a Data Science Solution on Azure certification validates your ability to design and implement data science solutions on Azure, which is one of the most popular cloud platforms for data science and AI workloads. By passing the DP-100 Exam, you can demonstrate your proficiency in using Azure tools and services to build intelligent solutions that can help organizations make better decisions and improve their business outcomes.

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All the Pass4guide Microsoft DP-100 practice questions are real and based on actual Designing and Implementing a Data Science Solution on Azure (DP-100) exam topics. The web-based Designing and Implementing a Data Science Solution on Azure (DP-100) practice test is compatible with all operating systems like Mac, IOS, Android, and Windows. Because of its browser-based Microsoft DP-100 Practice Exam, it requires no installation to proceed further. Similarly, Chrome, IE, Firefox, Opera, Safari, and all the major browsers support the Designing and Implementing a Data Science Solution on Azure (DP-100) practice test.

Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Exam is a certification exam that tests the candidate's knowledge and skills in designing and implementing data science solutions on the Azure platform. DP-100 exam is designed for data scientists, data engineers, and machine learning engineers who work with large amounts of data and want to leverage the power of Azure to build scalable and efficient data solutions.

The DP-100 Exam is designed to test candidates' knowledge and skills in various areas related to data science, such as data exploration and preparation, modeling, feature engineering, and machine learning. To pass the exam, candidates must demonstrate their ability to design and implement data science solutions using Azure services such as Azure Machine Learning, Azure Databricks, and Azure HDInsight, among others.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q500-Q505):

NEW QUESTION # 500
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?

  • A. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
  • B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
  • C. View the explanations for the registered model in Azure ML studio.
  • D. View the log files generated by the experiment used to train the model.

Answer: B

Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
1. Sign in to the studio at https://ml.azure.com.
2. Go to Models and select the model you want to deploy.
3. Select +Deploy.
4. Populate the Deploy model form.
5. Expand the Advanced menu.
6. Select Enable Application Insights diagnostics and data collection.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights


NEW QUESTION # 501
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector- machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature- importance


NEW QUESTION # 502
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 503
You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment


For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters


NEW QUESTION # 504
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.
You must submit an experiment that runs this script in the Azure Machine Learning workspace.
The following compute resources are available:
a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.
Which resources should the data scientist use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 505
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