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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.

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The world is becoming increasingly data-driven, and organizations must adapt to compete. The Microsoft DP-100 certification exam is designed to equip data professionals with the skills they need to design and implement data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification exam is an excellent way to prove your expertise in the field and advance your career.

The Microsoft DP-100 Exam covers a wide range of topics, including data exploration and preparation, modeling, deployment, and management of a data science solution on Azure. It also covers topics such as data ingestion, feature engineering, and machine learning algorithms. DP-100 exam is designed to test the candidate's understanding of these topics and their ability to apply them to real-world scenarios.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q279-Q284):

NEW QUESTION # 279
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 options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
rence:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample

NEW QUESTION # 280
You create a multi-class image classification deep learning model.
You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?

A. Save the model locally as a file, and deploy the model as a local web service.
B. Register the model, specifying the model_framework and model_framework_version properties.
C. Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.
D. Register the model with a .pt property.

Answer: B

Explanation:
Explanation
framework_version: The PyTorch version to be used for executing training code.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py

NEW QUESTION # 281
You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
References:
https://www.edureka.co/blog/classification-algorithms/

NEW QUESTION # 282
You manage an Azure Machine Learning workspace named workspace 1 with a compute instance named computet.
You must remove a kernel named kernel 1 from computet1. You connect to compute 1 by using noa terminal window from workspace 1.
You need to enter a command in the terminal window to remove kernel 1.
Which command should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection it worth one point.

Answer:

Explanation:

NEW QUESTION # 283
You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. a Root Mean Square Error value that is high
B. a Root Mean Square Error value that is tow
C. an R-Squared value close to 0
D. an R Squared value dose to 1
E. an F1 score that is high
F. an F 1 score that is low.

Answer: A,F

NEW QUESTION # 284
......

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