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70-773 - Analyzing Big Data with Microsoft R - Dump Information

Vendor	:	Microsoft
Exam Code	:	70-773
Exam Name	:	Analyzing Big Data with Microsoft R
Questions and Answers	:	39 Q & A
Updated On	:	January 1, 2018
PDF Download Mirror	:	70-773 Brain Dump
Get Full Version	:	Pass4sure 70-773 Full Version

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70-773 Questions and Answers

70-773 Microsoft Analyzing Big Data with Microsoft R http://killexams.com/pass4sure/exam-detail/70-773 QUES TION: 34 You plan to read data from an Oracle database table and to store the data in the file system for later processing by dplyrXdf, The size of the data is larger than the memory on the server to used for modelling. You need to ensure that the data can be processed by dplyrXdf in the least amount of time possible. How should you transfer the data from the Oracle database? A . Use the RODBC library, connect to the Oracle database server by using odbcConnect. and then use rxDataStep to export the data to a comma-separated values (CSV) file. B. Define a data source to the Oracle database server by using RxOdbcData, and then use rxlmport to save the data to an XDF file. C. Use the RODBC library, connect to the Oracle database server by using odbcConnect. and then use rxSplit to save the data to multiple comma-separated values (CSV) files. An swer: C QUES TION: 35 Note: This Question is part of a series of Questions that use the same or similar answer choices. An answer choice may be correct 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 Answer: E QUESTION: 36 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, whale 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 use dplyrXdf and you discover that after you exit the session, the output files that were created were deleted. You need to prevent the files from being deleted. Solution: You use dplyrXdf with the outFile parameter and specify a path other than the working directory for dplyrXdf. Does this meet the goal? A. Yes B. No Answer: A QUESTION: 37 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, whale 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 use dplyrXdf. and you discover that after you exit the session, the output files that were created were deleted. You need to prevent the files from being deleted. Solution: You use rxSetComputeContext with the local parameter before performing operations that save results. Does this meet the goal? A. Yes B. No Answer: B QUESTION: 38 You have a dataset. You need to repeatedly split randomly the dataset so that 80 percent of the data is used as a training set and the remaining 20 percent is used as a test set. Which method should you use? A. threshold B. binary classification C. imputation D. cross validation E. pruning Answer: D QUESTION: 39 You have a slow Map Reduce job. You need to optimize the job to control the number of mapper and runner tasks. Which function should you use? A. RxComputeContext B. RxHadoopMR C. rxExec D. RxLocalParallel Answer: B For More exams visit http://killexams.com Kill your exam at First Attempt....Guaranteed!

Microsoft 70-773 Exam (Analyzing Big Data with Microsoft R) Detailed Information

70-773 - Analyzing Big Data with Microsoft R

70-773 Test Objectives

Read and explore big data

Read data with R Server
- Read supported data file formats, such as text files, SAS, and SPSS; convert data to XDF format; identify trade-offs between XDF and flat text files; read data through Open Database Connectivity (ODBC) data sources; read in files from other file systems; use an internal data frame as a data source; process data from sources that cannot be read natively by R Server
Summarize data
- Compute crosstabs and univariate statistics, choose when to use rxCrossTabs versus rxCube, integrate with open source technologies by using packages such as dplyrXdf, use group by functionality, create complex formulas to perform multiple tasks in one pass through the data, extract quantiles by using rxQuantile
Visualize data
- Visualize in-memory data with base plotting functions and ggplot2; create custom visualizations with rxSummary and rxCube; visualize data with rxHistogram and rxLinePlot, including faceted plots

Process big data

Process data with rxDataStep
- Subset rows of data, modify and create columns by using the Transforms argument, choose when to use on-the-fly transformations versus in-data transform trade-offs, handle missing values through filtering or replacement, generate a data frame or an XDF file, process dates (POSIXct, POSIXlt)
Perform complex transforms that use transform functions
- Define a transform function; reshape data by using a transform function; use open source packages, such as lubridate; pass in values by using transformVars and transformEnvir; use internal .rx variables and functions for tasks, including cross-chunk communication
Manage data sets
- Sort data in various orders, such as ascending and descending; use rxSort deduplication to remove duplicate values; merge data sources using rxMerge(); merge options and types; identify when alternatives to rxSort and rxMerge should be used
Process text using RML packages
- Create features using RML functions, such as featurizeText(); create indicator variables and arrays using RML functions, such as categorical() and categoricalHash(); perform feature selection using RML functions

Build predictive models with ScaleR

Estimate linear models
- Use rxLinMod, rxGlm, and rxLogit to estimate linear models; set the family for a generalized linear model by using functions such as rxTweedie; process data on the fly by using the appropriate arguments and functions, such as the F function and Transforms argument; weight observations through frequency or probability weights; choose between different types of automatic variable selections, such as greedy searches, repeated scoring, and byproduct of training; identify the impact of missing values during automatic variable selection
Build and use partitioning models
- Use rxDTree, rxDForest, and rxBTrees to build partitioning models; adjust the weighting of false positives and misses by using loss; select parameters that affect bias and variance, such as pruning, learning rate, and tree depth; use as.rpart to interact with open source ecosystems
Generate predictions and residuals
- Use rxPredict to generate predictions; perform parallel scoring using rxExec; generate different types of predictions, such as link and response scores for GLM, response, prob, and vote for rxDForest; generate different types of residuals, such as Usual, Pearson, and DBM
Evaluate models and tuning parameters
- Summarize estimated models; run arbitrary code out of process, such as parallel parameter tuning by using rxExec; evaluate tree models by using RevoTreeView and rxVarImpPlot; calculate model evaluation metrics by using built-in functions; calculate model evaluation metrics and visualizations by using custom code, such as mean absolute percentage error and precision recall curves
Create additional models using RML packages
- Build and use a One-Class Support Vector Machine, build and use linear and logistic regressions that use L1 and L2 regularization, build and use a decision tree by using FastTree, use FastTree as a recommender with ranking loss (NDCG), build and use a simple three-layer feed-forward neural network

Use R Server in different environments

Use different compute contexts to run R Server effectively
- Change the compute context (rxHadoopMR, rxSpark, rxLocalseq, and rxLocalParallel); identify which compute context to use for different tasks; use different data source objects, depending on the context (RxOdbcData and RxTextData); identify and use appropriate data sources for different data sources and compute contexts (HDFS and SQL Server); debug processes across different compute contexts; identify use cases for RevoPemaR
Optimize tasks by using local compute contexts
- Identify and execute tasks that can be run only in the local compute context, identify tasks that are more efficient to run in the local compute context, choose between rxLocalseq and rxLocalParallel, profile across different compute contexts
Perform in-database analytics by using SQL Server
- Choose when to perform in-database versus out-of-database computations, identify limitations of in-database computations, use in-database versus out-of-database compute contexts appropriately, use stored procedures for data processing steps, serialize objects and write back to binary fields in a table, write tables, configure R to optimize SQL Server ( chunksize, numtasks, and computecontext), effectively communicate performance properties to SQL administrators and architects (SQL Server Profiler)
Implement analysis workflows in the Hadoop ecosystem and Spark
- Use appropriate R Server functions in Spark; integrate with Hive, Pig, and Hadoop MapReduce; integrate with the Spark ecosystem of tools, such as SparklyR and SparkR; profile and tune across different compute contexts; use doRSR for parallelizing code that was written using open source foreach
Deploy predictive models to SQL Server and Azure Machine Learning
- Deploy predictive models to SQL Server as a stored procedure, deploy an arbitrary function to Azure Machine Learning by using the AzureML R package, identify when to use DeployR

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