Precise Microsoft DP-100 Simulations Online

NEW QUESTION 1
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 Technique (SMOTE)
• D. Permutation Feature Importance

Answer: C

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

NEW QUESTION 2

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

Answer: D

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/

NEW 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.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
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

NEW QUESTION 4

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 DLVM to support CUDA. 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

Answer: C

Explanation:
A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances. References:
https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning

NEW QUESTION 5

You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
Models must be built using Caffe2 or Chainer frameworks.
Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.
Personal devices must support updating machine learning pipelines when connected to a network. You need to select a data science environment.
Which environment should you use?

• A. Azure Machine Learning Service
• B. Azure Machine Learning Studio
• C. Azure Databricks
• D. Azure Kubernetes Service (AKS)

Answer: A

Explanation:
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM.
DSVM integrates with Azure Machine Learning.

NEW QUESTION 6

You are building a binary classification model by using a supplied training set. The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.

• A. Penalize the classification
• B. Resample the data set using under sampling or oversampling
• C. Generate synthetic samples in the minority class.
• D. Use accuracy as the evaluation metric of the model.
• E. Normalize the training feature set.

Answer: BCD

NEW QUESTION 7

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.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 8

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

Answer: D

NEW QUESTION 9

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

Answer: B

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.
References:
https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html

NEW QUESTION 10

You need to select a feature extraction method. Which method should you use?

• A. Mutual information
• B. Mood’s median test
• C. Kendall correlation
• D. Permutation Feature Importance

Answer: C

Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

NEW QUESTION 11

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

Answer: A

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

NEW QUESTION 12

You are developing a hands-on workshop to introduce Docker for Windows to attendees. You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Microsoft Hardware-Assisted Virtualization Detection Tool
• B. Kitematic
• C. BIOS-enabled virtualization
• D. VirtualBox
• E. Windows 10 64-bit Professional

Answer: E

Explanation:
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher. References:
https://docs.docker.com/toolbox/toolbox_install_windows/ https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

NEW QUESTION 13

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.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
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

NEW QUESTION 14

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: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
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

NEW QUESTION 15

You are building a machine learning model for translating English language textual content into French language textual content.
You need to build and train the machine learning model to learn the sequence of the textual content. Which type of neural network should you use?

• A. Multilayer Perceptions (MLPs)
• B. Convolutional Neural Networks (CNNs)
• C. Recurrent Neural Networks (RNNs)
• D. Generative Adversarial Networks (GANs)

Answer: C

Explanation:
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They’re called recurrent because the network’s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

NEW QUESTION 16

You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module. Which splitting mode should you use?

• A. Regular Expression Split
• B. Split Rows with the Randomized split parameter set to true
• C. Relative Expression Split
• D. Recommender Split

Answer: B

Explanation:
Split Rows: Use this 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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

NEW QUESTION 17

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

Answer: B

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/

NEW QUESTION 18

You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must
meet the following requirements:
iterate all possible combinations of hyperparameters
minimize computing resources required to perform the sweep
You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?

• A. Random sweep
• B. Sweep clustering
• C. Entire grid
• D. Random grid
• E. Random seed

Answer: D

Explanation:
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.

NEW QUESTION 19

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

Answer: C

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

NEW QUESTION 20

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.
Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.
You need to produce the distribution.
Which type of distribution should you produce?

• A. Paired t-test with a two-tail option
• B. Unpaired t-test with a two tail option
• C. Paired t-test with a one-tail option
• D. Unpaired t-test with a one-tail option

Answer: A

Explanation:
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test https://en.wikipedia.org/wiki/Student%27s_t-test

NEW QUESTION 21
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