Web-Based Microsoft DP-100 Practice Exam - Get Familiar With Real Exam Environment

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The DP-100 exam is a valuable certification for data scientists who work with Azure, as it demonstrates their proficiency in designing and implementing data science solutions on this platform. Designing and Implementing a Data Science Solution on Azure certification can help data scientists advance their careers and increase their earning potential.

Microsoft DP-100 certification exam is a comprehensive test that covers a wide range of topics related to data science and machine learning. It consists of various modules that test your knowledge of data exploration, data preprocessing, feature engineering, model training, and deployment. DP-100 exam also evaluates your understanding of machine learning algorithms, statistical modeling, and data visualization techniques. By passing the DP-100 Exam, you can demonstrate your proficiency in all these areas and prove your ability to design and implement effective data science solutions on Azure.

Obtaining the DP-100 certification demonstrates the candidate's proficiency in designing and implementing data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification provides a competitive edge in the job market and opens up various career opportunities for the candidate. The DP-100 certification is also a prerequisite for other advanced Azure-based data science certifications, such as the DP-200 (Implementing an Azure Data Solution) and DP-201 (Designing an Azure Data Solution) certifications.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q241-Q246):

NEW QUESTION # 241
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.


NEW QUESTION # 242
You need to modify the inputs for the global penalty event model to address the bias and variance issue.
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 # 243
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.
You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.
You need to complete the following Python code to configure Bayesian sampling.
Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 244
You need to select a feature extraction method.
Which method should you use?

Answer: D

Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules


NEW QUESTION # 245
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:

Explanation:


NEW QUESTION # 246
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