Mobile Menu

[Free] 2019(Nov) EnsurePass Microsoft DP-100 Dumps with VCE and PDF 11-20

by admin, November 7, 2019

Get Full Version of the Exam
http://www.EnsurePass.com/DP-100.html

Question No.11

HOTSPOT

You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.

You need to configure the Preprocess Text module to meet the following requirements:

image

image

Ensure that multiple related words from a single canonical form. Remove pipe characters from text.

image

Remove words to optimize information retrieval.

Which three options should you select? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

image

Correct Answer:

image

Question No.12

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?

  1. Paired t-test with a two-tail option

  2. Unpaired t-test with a two tail option

  3. Paired t-test with a one-tail option

  4. Unpaired t-test with a one-tail option

Correct Answer: D

Question No.13

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

Which two hyperparameters should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

  1. Number of hidden nodes

  2. Learning Rate

  3. The type of the normalizer

  4. Number of learning iterations

  5. Hidden layer specification

Correct Answer: DE

Explanation:

D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.

E: For Hidden layer specification, select the type of network architecture to create.

Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class- neural-network

Question No.14

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?

  1. Data Science Virtual Machine for Linux (CentOS)

  2. Data Science Virtual Machine for Windows 2012

  3. Data Science Virtual Machine for Windows 2016

  4. Geo AI Data Science Virtual Machine with ArcGIS

  5. Data Science Virtual Machine for Linux (Ubuntu)

Correct Answer: A

Question No.15

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?

  1. Exponential Smoothing (ETS) function.

  2. One Class Support Vector Machine module

  3. Time Series Anomaly Detection module

  4. Finite Impulse Response (FIR) Filter module.

Correct Answer: D

Question No.16

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 value count for each feature column.

Which two modules can you users? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  1. Execute Python Script

  2. Export Count Table

  3. Convert to Indicator Values

  4. Summarize Data

  5. Compute linear Correlation

Correct Answer: BE

Question No.17

You are building recurrent neural network to perform a binary classification.

The training loss, validation loss, training accuracy, and validation accuracy of each training

epoch has been provided. You need to identify whether the classification model is over fitted. Which of the following is correct?

  1. The training loss increases while the validation loss decreases when training the model.

  2. The training loss decreases while the validation loss increases when training the model.

  3. The training loss stays constant and the validation loss decreases when training the model.

  4. The training loss .stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

Correct Answer: B

Explanation:

An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.

References:

How to Diagnose Overfitting and Underfitting of LSTM Models

Question No.18

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.

  1. an F1 score that is high

  2. an R Squared value dose to 1

  3. an R-Squared value close to 0

  4. a Root Mean Square Error value that is high

  5. a Root Mean Square Error value that is tow

  6. an F1 score that is low.

Correct Answer: DF

Question No.19

You are creating a machine learning model. You need to identify outliers data.

Which two visualizations can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  1. box plot

  2. scatter

  3. random forest diagram

  4. Venn diagram

  5. ROC curve

Correct Answer: AB

Question No.20

HOTSPOT

You are evaluating a Python NumPy array that contains six data points defined as follows: data = [10, 20, 30, 40, 50, 60]

You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

train: [10 40 50 60], test: [20 30]

train: [20 30 40 60], test: [10 50]

train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.

How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.

image

Correct Answer:

image

Get Full Version of the Exam
DP-100 Dumps
DP-100 VCE and PDF

Categories