DP-100 | How Many Questions Of DP-100 Practice Question

Your success in Microsoft DP-100 is our sole target and we develop all our DP-100 braindumps in a way that facilitates the attainment of this target. Not only is our DP-100 study material the best you can find, it is also the most detailed and the most updated. DP-100 Practice Exams for Microsoft Microsoft Other Exam DP-100 are written to the highest standards of technical accuracy.

Online Microsoft DP-100 free dumps demo Below:

NEW QUESTION 1

You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
DP-100 dumps exhibit

NEW QUESTION 2

You are performing clustering by using the K-means algorithm. You need to define the possible termination conditions.
Which three conditions can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  • A. A fixed number of iterations is executed.
  • B. The residual sum of squares (RSS) rises above a threshold.
  • C. The sum of distances between centroids reaches a maximum.
  • D. The residual sum of squares (RSS) falls below a threshold.
  • E. Centroids do not change between iterations.

Answer: ADE

Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/k-means-clustering https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html

NEW QUESTION 3

You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
• Reduce the number of training epochs.
• Reduce the size of the neural network.
• Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area. NOTE: Each correct selection is worth one point.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
DP-100 dumps exhibit

NEW QUESTION 4

You need to replace the missing data in the AccessibilityToHighway columns.
How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Replace using MICE
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.
Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Box 2: Propagate
Cols with all missing values indicate if columns of all missing values should be preserved in the output. References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

NEW QUESTION 5

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.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Create Scatterplot Summarize Data Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/ https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values

NEW QUESTION 6

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:
DP-100 dumps exhibit Models must be built using Caffe2 or Chainer frameworks.
DP-100 dumps exhibit 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.
DP-100 dumps exhibit 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 7

You need to implement a model development strategy to determine a user’s tendency to respond to an ad. Which technique should you use?

  • A. Use a Relative Expression Split module to partition the data based on centroid distance.
  • B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
  • C. Use a Split Rows module to partition the data based on distance travelled to the event.
  • D. Use a Split Rows module to partition the data based on centroid distance.

Answer: A

Explanation:
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

NEW QUESTION 8

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 model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.
You start by creating a linear regression model. You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: A

Explanation:
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION 9

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 10

You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B
The variation of Class B is very high. You need to resolve imbalances. Which method should you use?

  • A. Partition and Sample
  • B. Cluster Centroids
  • C. Tomek links
  • D. Synthetic Minority Oversampling Technique (SMOTE)

Answer: D

NEW QUESTION 11

You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?

  • A. Apply an analysis of variance (ANOVA).
  • B. Apply a Pearson correlation coefficient.
  • C. Apply a Spearman correlation coefficient.
  • D. Apply a linear discriminant analysis.

Answer: D

Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data. All shared features for local models are continuous variables.

NEW QUESTION 12

You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared

NEW QUESTION 13

You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit.
Which technique should you use?

  • A. Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.
  • B. Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.
  • C. Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.
  • D. Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.

Answer: A

Explanation:

Scenario:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:
DP-100 dumps exhibit
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 +/- 5%.

NEW QUESTION 14

You are implementing a machine learning model to predict stock prices. The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools. What should you do?

  • A. Create a Data Science Virtual Machine (DSVM) Windows edition.
  • B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
  • C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
  • D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
  • E. Create a Data Science Virtual Machine (DSVM) Linux edition.

Answer: E

NEW QUESTION 15

You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  • A. an F1 score that is high
  • B. an R Squared value dose to 1
  • C. an R-Squared value close to 0
  • D. a Root Mean Square Error value that is high
  • E. a Root Mean Square Error value that is tow
  • F. an F 1 score that is low.

Answer: DF

NEW QUESTION 16

You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
DP-100 dumps exhibit The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
DP-100 dumps exhibit Batch size must be 16, 32 and 64.
DP-100 dumps exhibit Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service. How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling param_sampling = RandomParameterSampling( { "learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

NEW QUESTION 17

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:
DP-100 dumps exhibit iterate all possible combinations of hyperparameters
DP-100 dumps exhibit minimize computing resources required to perform the sweep
DP-100 dumps exhibit 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 18

You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.
Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Step 1: Sweep Clustering
Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.
One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.
Step 2: Train Model Step 3: Evaluate Model
Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
References:
http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

NEW QUESTION 19

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 20

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:
DP-100 dumps exhibit
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.
DP-100 dumps exhibit

  • 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 21

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 22
......

P.S. Easily pass DP-100 Exam with 111 Q&As DumpSolutions.com Dumps & pdf Version, Welcome to Download the Newest DumpSolutions.com DP-100 Dumps: https://www.dumpsolutions.com/DP-100-dumps/ (111 New Questions)