SASInstitute A00-220 Exam Information and Actual Questions

  • Exam Code/Number: A00-220
  • Exam Name/Title: SAS Big Data Preparation, Statistics, and Visual Exploration
  • Certification Provider: SASInstitute
  • Corresponding Certification: SASInstitute Certification

A00-220
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SASInstitute
A00-220 Exam
SAS Big Data Preparation, Statistics, and Visual Exploration

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SASInstitute A00-220 certification exam is designed for individuals who are interested in demonstrating their expertise in big data preparation, statistics, and visual exploration using SAS software. SAS Big Data Preparation, Statistics, and Visual Exploration certification is ideal for data analysts, data scientists, and business analysts who work with large datasets and want to enhance their skills in data preparation, analysis, and visualization.

SASInstitute A00-220 Exam Overview:

Certification Vendor:SAS Institute
Exam Name:SAS Big Data Preparation, Statistics, and Visual Exploration
Exam Number:A00-220
Certificate Validity Period:Not specified
Available Languages:English
Passing Score:67%
Exam Format:Multiple Choice, Short Answer, Interactive Questions
Exam Price:$180 USD
Real Exam Qty:55-60
Related Certifications:SAS Certified Big Data Professional
SAS Big Data Programming and Loading (A00-221)
Exam Duration:110 minutes
Exam Way:Pearson VUE testing centers and SAS-authorized exam delivery.
Pre Condition:Recommended hands-on experience with SAS/STAT, SAS Visual Analytics, and SAS DataFlux Data Management Studio. Commonly pursued as part of the SAS Certified Big Data Professional credential path.
Official Syllabus URL:https://www.sas.com/en_in/certification/credentials/data-management/big-data-professional/big-data-preparation-exam.html

SASInstitute A00-220 is a certification exam that focuses on SAS Big Data Preparation, Statistics, and Visual Exploration. It is designed to test the knowledge and skills of individuals in the field of data analysis, specifically in handling big data. A00-220 exam is intended for individuals who wish to validate their expertise and proficiency in using SAS tools and techniques in data preparation, statistical analysis, and visual exploration of big data.

To take the SASInstitute A00-220 exam, candidates are required to have a strong foundation in data analysis, statistics, and programming. Candidates should also have experience in using SAS tools and techniques for data preparation, statistical analysis, and visual exploration. A00-220 exam is suitable for data analysts, statisticians, data scientists, and other professionals who work with big data.

SASInstitute A00-220 exam is a certification test that measures the proficiency of a candidate in utilizing SAS tools for big data preparation, statistics, and visual exploration. A00-220 exam is designed to evaluate the knowledge and practical skills of the candidates in using SAS to perform data preparation, advanced analytics, data exploration, and data visualization. The SASInstitute A00-220 certification is highly regarded in the industry and is recognized globally as a standard for measuring the expertise of a professional in SAS tools.

SASInstitute A00-220 Exam Syllabus Topics:

SectionObjectives
Hadoop and Hive Integration- Big Data Platform Usage
  • 1. Work with Hive
  • 2. Integrate SAS with big data platforms
  • 3. Work with Hadoop
Data Exploration and Visualization- Visual Analytics
  • 1. Explore data visually
  • 2. Create visual reports
  • 3. Identify patterns and trends
SAS Programming and Data Preparation- Data Access and Manipulation
  • 1. Access data from multiple sources
  • 2. Transform and manipulate data
  • 3. Implement critical SAS programming techniques
Big Data Fundamentals- Big Data Concepts
  • 1. Recognize big data challenges
  • 2. Understand big data environments
Statistics- Fundamental Statistical Techniques
  • 1. Interpret statistical results
  • 2. Basic statistical analysis
  • 3. Descriptive statistics
Data Quality Management- Data Quality Improvement
  • 1. Cleanse and standardize data
  • 2. Identify data quality issues
  • 3. Improve data quality for reporting and analytics


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