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Analyzing Big Data with Microsoft R 70-773 Exam

Analyzing Big Data with Microsoft R 70-773 Exam been designed in order to test expertise and technical knowledge in designing and implementing Microsoft services in order to obtain value from the available data set. It examines the candidate's ability to execute Microsoft Big Data Services according to architecture practices. The Analyzing Big Data with Microsoft R 70-773 Exam is high level exam and it requires in-depth knowledge of data analytics concepts, before you go ahead and write this certification you must require some prerequisites Candidates for this exam are data scientists or analysts who process and analyze data sets larger than memory using R.

Candidates should have experience with R, familiarity with data structures, familiarity with basic programming concepts (such as control flow and scope), and familiarity with writing and debugging R functions. Candidates should be familiar with common statistical methods and data analysis best practices. Candidates should also have a high-level understanding of data platforms, such as the Hadoop ecosystem, SQL Server, and core T-SQL capabilities. We think our Analyzing Big Data with Microsoft R 70-773 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for Microsoft 70-773 Exam

This is the list of the contents in our Analyzing Big Data with Microsoft R 70-773 Practice Test:

  • Data reading with R Server.
  • Hadoop Ecosystem and Spark.
  • Models with ScaleR.
  • Data Processing with RxDataStep.
  • Compute concepts to Run R Server.

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Overview about MICROSOFT 70-773 Exam

  • Format: Multiple choice, multiple answer
  • Length of Examination: 150 minutes
  • Registration Fee: 165 USD
  • Passing Score: 70-80%
  • Number of Questions: 40-60

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Microsoft 070-773 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and Operationalize R Solutions15%- Run R scripts in Hadoop/Spark environments
- Manage scalable analytics workflows
- Integrate R with SQL Server R Services
Build Statistical and Machine Learning Models25%- Use partitioning and parallelized models
- Process text data with RML packages
- Train linear and generalized linear models
Evaluate and Optimize Models20%- Optimize R scripts for scale and speed
- Assess model performance
- Tune model parameters
Manage Data Sets20%- Handle large datasets beyond memory
- Transform and clean data
- Read and write data using Microsoft R Server
Explore and Visualize Data20%- Perform exploratory data analysis
- Summarize and aggregate data
- Create visualizations using R and RevoScaleR

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