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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:
- Compute concepts to Run R Server.
- Data Processing with RxDataStep.
- Hadoop Ecosystem and Spark.
- Models with ScaleR.
- Data reading with R Server.
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Reference: https://www.microsoft.com/en-us/learning/exam-70-773.aspx
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Microsoft 070-773 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate and Optimize Models | 20% | - Optimize R scripts for scale and speed - Tune model parameters - Assess model performance |
| Build Statistical and Machine Learning Models | 25% | - Train linear and generalized linear models - Process text data with RML packages - Use partitioning and parallelized models |
| Manage Data Sets | 20% | - Transform and clean data - Handle large datasets beyond memory - Read and write data using Microsoft R Server |
| Deploy and Operationalize R Solutions | 15% | - Manage scalable analytics workflows - Integrate R with SQL Server R Services - Run R scripts in Hadoop/Spark environments |
| Explore and Visualize Data | 20% | - Perform exploratory data analysis - Summarize and aggregate data - Create visualizations using R and RevoScaleR |
