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Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure is a certification exam designed for data professionals who want to demonstrate their skills in designing and implementing data science solutions on Azure. It is intended for individuals who work with data engineers, data scientists, and other stakeholders in the organization to create and deploy end-to-end data science solutions.

Microsoft DP-100 certification exam is an essential tool for professionals who want to enhance their skills in data science and Azure data science solutions. DP-100 Exam covers a wide range of topics related to data science solutions on Azure and is designed to test the candidate’s ability to design and implement data science solutions using Azure services. The DP-100 certification exam is an excellent tool for professionals who want to advance their careers in data science and demonstrate their expertise in Azure data science solutions.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q310-Q315):

NEW QUESTION # 310
You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to
10.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation

In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
References:
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/

NEW QUESTION # 311
You perform hyper parameter tuning with Azure Machine Learning.
You create the following Python code:

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

Answer:

Explanation:

NEW QUESTION # 312
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2. You configure the regression job by using the following script:

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

Answer:

Explanation:

NEW QUESTION # 313
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

1 - from azureml . train.hyperdrive
2 - import TruncationSelectionPolicy
3 - early_termination_policy =....
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

NEW QUESTION # 314
You manage an Azure Machine Learning workspace named workspaces
You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only):

You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with 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 in the correct order.

Answer:

Explanation:

NEW QUESTION # 315
......

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Chris Bell Chris Bell

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