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13 Microsoft Azure DP-100 practices questions
Best Quality Microsoft Azure AZ-120 Exam Questions [2020] Free
QUESTION 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply a Quantiles binning mode with a PQuantile normalization.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Use the Entropy MDL binning mode which has a target column.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
QUESTION 2
HOTSPOT
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 option-, m the answer area;
NOTE: Each correct selection is worth one point.
Hot Area
Correct Answer:
QUESTION 3
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module
may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer: 
Box 1: Clean Missing Data
Box 2: SMOTE Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented
cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than
simply duplicating existing cases.
Box 3: Convert to Indicator Values Use the Convert to Indicator Values module in Azure Machine Learning Studio. The
purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns
that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values
QUESTION 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature
set.
You need to analyze a full dataset to include all values.
Solution: Remove the entire column that contains the missing data point.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Use the Multiple Imputation by Chained Equations (MICE) method.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
QUESTION 5
You are analyzing a dataset by using Azure Machine Learning Studio.
You need to generate a statistical summary that contains the p-value and the unique count for each feature column.
Which two modules can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Computer Linear Correlation
B. Export Count Table
C. Execute Python Script
D. Convert to Indicator Values
E. Summarize Data
Correct Answer: BE
The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table
(deprecated) and Count Featurizer (deprecated) modules.
E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For
example, you might need to know:
How many missing values are there in each column?
How many unique values are there in a feature column?
What is the mean and standard deviation for each column?
The module calculates the important scores for each column, and returns a row of summary statistics for each variable
(data column) provided as input.
Incorrect Answers:
A: The Compute Linear Correlation module in Azure Machine Learning Studio is used to compute a set of Pearson
correlation coefficients for each possible pair of variables in the input dataset.
C: With Python, you can perform tasks that aren\\’t currently supported by existing Studio modules such as: Visualizing
data using matplotlib Using Python libraries to enumerate datasets and models in your workspace Reading, loading,
and manipulating data from sources not supported by the Import Data module
D: The purpose of the Convert to Indicator Values module is to convert columns that contain categorical values into a
series of binary indicator columns that can more easily be used as features in a machine learning model.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data
QUESTION 6
DRAG DROP
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.
Select and Place
Correct Answer:
QUESTION 7
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The
C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in
the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
QUESTION 8
DRAG DROP
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are
removed.
Which three Azure Machine Learning Studio 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.
Select and Place:
Correct Answer:
QUESTION 9
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You need to analyze a full dataset to include all values.
Solution: Use the Last Observation Carried Forward (LOCF) method to impute the missing data points.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Instead use the Multiple Imputation by Chained Equations (MICE) method.
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method
described in the statistical literature as “Multivariate Imputation using Chained Equations” or “Multiple Imputation by
Chained Equations”. With a multiple imputation method, each variable with missing data is modeled conditionally using
the other variables in the data before filling in the missing values. Note: Last observation carried forward (LOCF) is a
method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all
subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused
by the attrition of participants in a study.
References: https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
QUESTION 10
You are evaluating a completed binary classification machine learning model.
You need to use the precision as the evaluation metric.
Which visualization should you use?
A. Violin pilot
B. Gradient descent
C. Box pilot
D. Binary classification confusion matrix
Correct Answer: D
Incorrect Answers:
A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.
B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local
minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or
approximate gradient) of the function at the current point.
C: A box plot lets you see basic distribution information about your data, such as median, mean, range and quartiles but
doesn\\’t show you how your data looks throughout its range.
References: https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/
QUESTION 11
HOTSPOT
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options 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:
Box 2: Unchanged Note: Select the Categorical option to specify that the values in the selected columns should be
treated as categories. For example, you might have a column that contains the numbers 0,1 and 2, but know that the
numbers actually mean “Smoker”, “Non smoker” and “Unknown”. In that case, by flagging the column as categorical
you can ensure that the
values are not used in numeric calculations, only to group data.
QUESTION 12
You need to select an environment that will meet the business and data requirements. Which environment should you
use?
A. Azure HDInsight with Spark MLlib
B. Azure Cognitive Services
C. Azure Machine Learning Studio
D. Microsoft Machine Learning Server
Correct Answer: D
QUESTION 13
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Stratified split for the sampling mode.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning.
SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
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