Free Microsoft 070-773 Exam Dumps Questions & Answers

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 ggplot2 package
• B.the rxSummary function
• C.the rxCrossTabs function
• D.the rxCube function
• E.the rxHistogram function
• F.the summary function
• G.the Describe package
• H.the rxQuantile function

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.

End of repeated scenario.
You need to sort the data from the dataset sample and to remove duplicates by using wkswork1.
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.

Correct Answer:

You plan to analyze data on a local computer. To improve performance, you plan to alternate the operation between a Microsoft SQL Server and the local computer.
You need to run complex code on the SQL Server, and then revert to the local compute context.
Which R code segment should you use?

• A.sqlCompute <- RxInSqlServer(connectionString = "Driver=SQL Server;Server = myServer; Database = TestDB; Uid = myID; Pwd = myPwd;")sqlPackagePaths <- RxFindPackage(package = "RevoScaleR", computeContext = sqlServerCompute)
• B.sqlCompute <- RxInSqlServer(connectionstring = sqlConnString, shareDir = sqlShareDir,wait = sqlWait, consoleOutput = sqlConsoleOutput)rxSetComputeContext("local")x <- 1:10rxExec(print, x, elemType =
  "cores", timesToRun = 10)rxSetComputeContext("sqlCompute")
• C.sqlCompute <- RxInSqlServer(connectionstring = sqlConnString, shareDir = sqlShareDir,wait = sqlWait, consoleOutput = sqlConsoleOutput)rxSetComputeContext("local")x <- 1:10rxExec(print, x, elemType =
  "cores", timesToRun = 10)rxSetComputeContext("RxLocalParallel")
• D.sqlCompute <- RxInSqlServer(connectionstring = sqlConnString, shareDir = sqlShareDir,wait = sqlWait, consoleOutput = sqlConsoleOutput)rxSetComputeContext("sqlCompute")x <- 1:10rxExec(print, x, elemType
  = "cores", timesToRun = 10)rxSetComputeContext("local")

You are running a large logistic regression for 1,000 feature variables by using the LoisticRegression() function in the MicrosoftML package. All of the predictor variables are numeric.
Currently, you specify the input variables separately by using the following formula.
Outcome ~ Feature000 + Feature001 + Feature002 + ... + Feature999
You discover that it takes 20 minutes to estimate each model.
You need to reduce the amount of time required to estimate each model without losing any information in the predictors.
What should you do?

• A.Use selectFeatures() to select the features that provide the most information about the outcome variable.
• B.Use princomp() on the correlation matrix of Features, and then use only the first 100 principle components to reduce the number of input variables.
• C.Use stepControl() to perform stepwise regression to limit the number of variables that contribute to the model.
• D.Use concat() to create a single array variable named Features, and then specify a new formula named Outcome ~ Features.

You are running a parallel function that uses the following R code segment. (Line numbers are included for reference only.)
0 1 cp <- 0.01 xval <- 0 maxdepth <- 5
0 2
0 3 (form, data = "segmentationDataBig", maxDepth = maxdepth, cp = cp, xval = xval, blocksPerRead = 250 You need to complete the R code. The solution must support chunking.
Which function should insert at line 02?

• A.rxExec
• B.rxDTree
• C.rxBTrees
• D.rxDForest