Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 261:

  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 Principal Components Analysis (PCA) 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.

  Incorrect Answers:

  The Principal Component Analysis module in Azure Machine Learning Studio (classic) is used to reduce the dimensionality of your training data. The module analyzes your data and creates a reduced feature set that captures all the

  information contained in the dataset, but in a smaller number of features.

  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/principal-component- analysis

• Question 262:

  You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column.

  You plan to use the Clean Missing Data.

  You need to select a data cleaning method.

  Which method should you use?

  A. Replace using Probabilistic PCA

  B. Normalization

  C. Synthetic Minority Oversampling Technique (SMOTE)

  D. Replace using MICE

  Correct Answer: A

  Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.

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

• Question 263:

  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 264:

  You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access. Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students. You need to ensure that students can run Python-based data visualization code.

  Which Azure tool should you use?

  A. Anaconda Data Science Platform

  B. Azure BatchAl

  C. Azure Notebooks

  D. Azure Machine Learning Service

  Correct Answer: C

  References: https://notebooks.azure.com/

• Question 265:

  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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  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: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.

  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 266:

  You are performing a filter-based feature selection for a dataset to build a multi-class classifier by using Azure Machine Learning Studio.

  The dataset contains categorical features that are highly correlated to the output label column.

  You need to select the appropriate feature scoring statistical method to identify the key predictors.

  Which method should you use?

  A. Kendall correlation

  B. Spearman correlation

  C. Chi-squared

  D. Pearson correlation

  Correct Answer: D

  Pearson's correlation statistic, or Pearson's correlation coefficient, is also known in statistical models as the r value. For any two variables, it returns a value that indicates the strength of the correlation

  Pearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest

  because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.

  Incorrect Answers:

  C: The two-way chi-squared test is a statistical method that measures how close expected values are to actual results.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature- selection https://www.statisticssolutions.com/pearsons-correlation-coefficient/

• Question 267:

  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: 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 268:

  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 plot

  B. Gradient descent

  C. Box plot

  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 269:

  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

• Question 270:

  You are creating a machine learning model.

  You need to identify outliers in the data.

  Which two visualizations can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Venn diagram

  B. Box plot

  C. ROC curve

  D. Random forest diagram

  E. Scatter plot

  Correct Answer: BE

  The box-plot algorithm can be used to display outliers.

  One other way to quickly identify Outliers visually is to create scatter plots.

  References:

  https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/