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