Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 101:

  HOTSPOT

  The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.

  You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.

  How should you complete the code? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: register_azure_blob_container

  Register an Azure Blob Container to the datastore.

  Box 2: create_if_not_exists = False

  Create the file share if it does not exists, defaults to False.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore

• Question 102:

  You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an

  experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

  You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

  What should you select? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Vocabulary mode: Create

  For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.

  N-Grams size: 3

  For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.

  Weighting function: Leave blank

  The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text

• Question 103:

  HOTSPOT

  You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

  You need to configure the module. You must meet the following requirements:

  1.

  Divide the data into subsets

  2.

  Assign the rows into folds using a round-robin method

  3.

  Allow rows in the dataset to be reused

  How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Use the Split data into partitions 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.

  Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.

  For Partition or sample mode, select Assign to Folds.

  Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

  If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.

  Randomized split: Select this option if you want rows to be randomly assigned to folds.

  If you do not select this option, rows are assigned to folds using the round-robin method.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

• Question 104:

  HOTSPOT

  You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

  Hot Area:

  

  Correct Answer:

  

  Box 1: 4

  Choose the one which has lower training and validation error and also the closest match.

  Minimize variance (difference between validation error and train error).

  Box 2: 5

  Minimize variance (difference between validation error and train error).

  Reference:

  https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

• Question 105:

  HOTSPOT

  You are performing a classification task in Azure Machine Learning Studio.

  You must prepare balanced testing and training samples based on a provided data set.

  You need to split the data with a 0.75:0.25 ratio.

  Which value should you use for each parameter? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Split rows

  Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.

  You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.

  Box 2: 0.75

  If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.

  Box 3: Yes

  To ensure splits are balanced.

  Box 4: No

  If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

• Question 106:

  HOTSPOT

  You are evaluating a Python NumPy array that contains six data points defined as follows:

  data = [10, 20, 30, 40, 50, 60]

  You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

  train: [10 40 50 60], test: [20 30]train: [20 30 40 60], test: [10 50]train: [10 20 30 50], test: [40 60]

  You need to implement a cross-validation to generate the output.

  How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: k-fold

  Box 2: 3

  K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

  The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.

  Box 3: data

  Example: Example:

  >>>

  >>> from sklearn.model_selection import KFold

  >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])

  >>> y = np.array([1, 2, 3, 4])

  >>> kf = KFold(n_splits=2)

  >>> kf.get_n_splits(X)

  >>> print(kf)

  KFold(n_splits=2, random_state=None, shuffle=False)

  >>> for train_index, test_index in kf.split(X):

  ... print("TRAIN:", train_index, "TEST:", test_index)

  ... X_train, X_test = X[train_index], X[test_index]

  ... y_train, y_test = y[train_index], y[test_index]

  TRAIN: [2 3] TEST: [0 1]

  TRAIN: [0 1] TEST: [2 3]

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

• Question 107:

  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.

  Hot Area:

  

  Correct Answer:

  

  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/

• Question 108:

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Boosted Decision Tree Regression

  Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

  Box 2:

  Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

• Question 109:

  You have a feature set containing the following numerical features: X, Y, and Z.

  The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:

  

  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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: 0.859122

  Box 2: a positively linear relationship +1 indicates a strong positive linear relationship -1 indicates a strong negative linear correlation 0 denotes no linear relationship between the two variables. References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

  You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features. Original and scaled data is shown in the following image.

  

• Question 110:

  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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: StandardScaler The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1. Example:

  

  All features are now on the same scale relative to one another. Box 2: Min Max Scaler

  

  Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.

  Box 3: Normalizer

  References:

  http://benalexkeen.com/feature-scaling-with-scikit-learn/