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											Q1.You need to use the ScaleR distributed processing in an Apache Hadoop environment.
Which data source should you use?
 - A:   Microsoft SQL Server database
 - B:   XDF data files
 - C:   ODBC data
 - D:   Teradata database

 solution: B

Explanation:
References: https://docs.microsoft.com/en-us/machine-learning-server/r/how-to-revoscaler-hadoop


Q2.You are planning the compute contexts for your environment.
You need to execute rx-function calls in parallel.
What are three possible compute contexts that you can use to achieve this goal? Each correct answer presents
a complete solution.
NOTE: Each correct selection is worth one point.
 - A:   local parallel
 - B:   Spark
 - C:   local sequential
 - D:   Map Reduce
 - E:   SQL

 solution: A, B, D

Explanation:
References:https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-r-server-compute-contexts


Q3.HOTSPOT
Note: This question is part of a series of questions that use the same scenario. For your convenience,
the scenario is repeated in each question. Each question presents a different goal and answer choices,
but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You are developing a Microsoft R Open solution that will leverage the computing power of the database server
for some of your datasets.
You are performing feature engineering and data preparation for the datasets.


              
The following is a sample of the dataset.

[PIC-1]

End of repeated scenario.
You plan to score some data to create data features to address empty rows.
You have the following R code.

[PIC-2]

You need to transform the data and overwrite the current dataset.
Which R code segment should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:


              

[PIC-3]
 - A:   [PIC-4]

 solution: A



Explanation:






Q4.Note: This question is part of a series of questions that use the same or similar answer choices. An
answer choice may be correct for more than one question in the series. Each question is independent
of the other questions in this series. Information and details provided in a question apply only to that
question.
You need to calculate a measure of central tendency and variability for the variables in a dataset that is grouped
by using another categorical variable.
What should you use?
 - A:   the Describe package
 - B:   the rxHistogram function
 - C:   the rxSummary function
 - D:   the rxQuantile function
 - E:   the rxCube function
 - F:   the summary function
 - G:   the rxCrossTabs function
 - H:   the ggplot2 package

 solution: C



Q5.Note: This question is part of a series of questions that use the same or similar answer choices. An
answer choice may be correct for more than one question in the series. Each question is independent
of the other questions in this series. Information and details provided in a question apply only to that
question.
You need to get all of the deciles for a variable in a data frame.
What should you use?
 - A:   the Describe package
 - B:   the rxHistogram function
 - C:   the rxSummary function
 - D:   the rxQuantile function
 - E:   the rxCube function
 - F:   the summary function
 - G:   the rxCrossTabs function
 - H:   the ggplot2 package

 solution: D



Q6.Note: This question is part of a series of questions that use the same or similar answer choices. An
answer choice may be correct for more than one question in the series. Each question is independent
of the other questions in this series. Information and details provided in a question apply only to that
question.
You have a dataset that contains the physical characteristics of people.
You need to visualize a relationship between height and weight for a subset of observations in the dataset.
What should you use?
 - A:   the Describe package
 - B:   the rxHistogram function
 - C:   the rxSummary function
 - D:   the rxQuantile function
 - E:   the rxCube function
 - F:   the summary function
 - G:   the rxCrossTabs function
 - H:   the ggplot2 package

 solution: H



Q7.Note: This question is part of a series of questions that use the same or similar answer choices. An
answer choice may be correct for more than one question in the series. Each question is independent
of the other questions in this series. Information and details provided in a question apply only to that
question.
You have a data source that is larger than memory.
You need to visualize the distribution of the values for a variable in the data source.
What should you use?
 - A:   the Describe package
 - B:   the rxHistogram function
 - C:   the rxSummary function
 - D:   the rxQuantile function
 - E:   the rxCube function
 - F:   the summary function
 - G:   the rxCrossTabs function
 - H:   the ggplot2 package

 solution: B



Q8.You have the following regression forest.

[PIC-5]

Which variable contributes the most to the dependent variable?
 - A:   stack.loss
 - B:   Water.Temp
 - C:   Air.Flow
 - D:   Acid.Conc

 solution: D



Q9.You need to run a large data tree model by using rxDForest. The model must use cross validation.
Which rxDForest option should you use?
 - A:   maxSurrogate
 - B:   maxNumBins
 - C:   maxDepth
 - D:   maxCompete
 - E:   xVal

 solution: E

Explanation:
References: https://docs.microsoft.com/en-us/machine-learning-server/r/how-to-revoscaler-decision-tree


Q10.You perform an analysis that produces the decision tree shown in the exhibit. (Click the Exhibit button.)

[PIC-6]

How many leaf nodes are there on the tree?
 - A:   2
 - B:   3
 - C:   5
 - D:   7

 solution: C

Explanation:
References: https://docs.microsoft.com/en-us/machine-learning-server/r/how-to-revoscaler-decision-tree