# [May-2020-New]High Quality Braindump2go DP-100 Exam VCE and PDF Dumps 129Q Free Share(71-84)

May/2020 New Braindump2go DP-100 Exam Dumps with PDF and VCE Free Updated Today! Following are some new DP-100 Exam Questions!

QUESTION 71
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 contain 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 (IOCF) method to impute the missing data points.
Does the solution meet the goal?

A. Yes
B. No

Explanation:
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 72
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 Scale and Reduce sampling mode.
Does the solution meet the goal?

A. Yes
B. No

Explanation:
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 73
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 Learning learning Studio. One class has a much smaller number of observations than the other classes in the training You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Does the solution meet the goal?

A. Yes
B. No

Explanation:
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 74
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 tin- 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

QUESTION 75
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 normalization with a QuantileIndex normalization.
Does the solution meet the GOAL?

A. Yes
B. No

Explanation:
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 76
You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements.
You need to select the appropriate method to conduct feature engineering on the data.
Which method should you use?

A. Exponential Smoothing (ETS) function.
B. One Class Support Vector Machine module
C. Time Series Anomaly Detection module
D. Finite Impulse Response (FIR) Filter module.

QUESTION 77
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?

A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Teachnique (SMOTE)
D. Permutation Feature Importance

Explanation:
Use the SMOTE module in Azure Machine Learning Studio (classic) 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.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

QUESTION 78
You are performing a filter based feature selection for a dataset 10 build a multi class classifies 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. Chi-squared
B. Spearman correlation
C. Kendall correlation
D. Person correlation

Explanation:
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.
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 79
You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano.
You need to select a pre configured DSVM to support the framework.
What should you create?

A. Data Science Virtual Machine for Linux (CentOS)
B. Data Science Virtual Machine for Windows 2012
C. Data Science Virtual Machine for Windows 2016
D. Geo AI Data Science Virtual Machine with ArcGIS
E. Data Science Virtual Machine for Linux (Ubuntu)

Explanation:
Caffe2 and PyTorch is supported by Data Science Virtual Machine for Linux.
Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4.
Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

QUESTION 80
You plan to use .a Deep learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the IXVM to support CUOA.
What should you implement?

A. Intel Software Guard Extensions (Intel SGX) technology
B. Solid State Drives (SSD)
C. Graphic Processing Unit (GPU)
D. Computer Processing Unit (CPU) speed increase by using overcloking
E. High Random Access Memory (RAM) configuration

Explanation:
A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.
References:

QUESTION 81
You need to select a pre built development environment for a series of data science experiments. You must use the R language for the experiments.
Which three environments can you use? Each correct answer presents a complete solution. NOTE:
Each correct selection is worth one point.

A. MI.NET Library on a local environment
B. Azure Machine Learning Studio
C. Data Science Virtual Machine (OSVM)
D. Azure Data bricks
E. Azure Cognitive Services

QUESTION 82
You plan to create a speech recognition deep learning model.
The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?

A. Apache Drill
B. Tensorflow
C. Rattle
D. Weka

Explanation:
TensorFlow is an open source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.
C: Weka is used for visual data mining and machine learning software in Java.
D: Scikit-learn is one of the most useful library for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of effiecient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Reference:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

QUESTION 83
You are developing a data science workspace that uses an Azure Machine Learning service.
You need to select a compote target to deploy the workspace.
What should you use?

A. Azure Data Lake Analytics
B. Azure Databrick .
C. Apache Spark for HDInsight.
D. Azure Container Service

Explanation:
Azure Container Instances can be used as compute target for testing or development. Use for low-scale CPU-based workloads that require less than 48 GB of RAM.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where

QUESTION 84
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 module to handle the missing data.
You need to select a data cleaning method.
Which method should you use?

A. Synthetic Minority Oversampling Technique (SMOTE)
B. Replace using MICE
C. Replace using; Probabilistic PCA
D. Normalization