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Valid DP-100 Most Reliable Questions & Leading Provider in Qual

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    Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure is a valuable certification for data professionals who want to demonstrate their skills in designing and implementing data science solutions on Azure. It covers a wide range of topics and requires candidates to demonstrate their ability to apply these skills to real-world scenarios. Becoming certified in Microsoft DP-100 can lead to career advancement and increased opportunities in the field of data science.

    Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q300-Q305):

    NEW QUESTION # 300
    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 dat a. 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.
    Reference:
    https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample


    NEW QUESTION # 301
    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 remaining 1,000 rows represent class 1 (10 percent).
    The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 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.

    Answer:

    Explanation:

    Explanation

    Box 1: 300
    You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
    Box 2: 5
    We should use 5 data rows.
    Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
    By increasing the number of nearest neighbors, you get features from more cases.
    By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
    References:
    https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote


    NEW QUESTION # 302
    You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.
    You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.
    Which two hyperparameters should you use? Each correct answer presents part of the solution.
    NOTE: Each correct selection is worth one point.

    • A. Hidden layer specification
    • B. The type of the normalizer
    • C. Number of learning iterations
    • D. Learning Rate
    • E. Number of hidden nodes

    Answer: A,C

    Explanation:
    Explanation
    D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.
    E: For Hidden layer specification, select the type of network architecture to create.
    Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers.
    References:
    https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network


    NEW QUESTION # 303
    You need to define a process for penalty event detection.
    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 # 304
    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 # 305
    ......

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