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100% SASInstitute A00-220 Guaranteed Success With Testing Engine

Exam Code: A00-220

Exam Name: SAS Big Data Preparation, Statistics, and Visual Exploration

Updated: Sep 02, 2026

Number: 0 Q&As with Testing Engine

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SASInstitute A00-220 Exam Overview:

Certification Vendor:SAS Institute
Exam Name:SAS Big Data Preparation, Statistics, and Visual Exploration
Exam Number:A00-220
Passing Score:67%
Real Exam Qty:55-60
Exam Format:Short Answer, Interactive Task-based, Multiple Choice
Exam Duration:110 minutes
Certificate Validity Period:No expiration (retired credential)
Exam Price:$180 USD
Available Languages:English
Related Certifications:SAS Certified Big Data Professional Using SAS 9
Recommended Training:SAS Big Data Preparation Training
Exam Registration:Pearson VUE Registration
SAS Certification Registration
Exam Way:Proctored onsite or online via Pearson VUE
Pre Condition:None required; recommended hands-on experience with SAS 9.4, SAS/STAT, SAS Visual Analytics, and DataFlux
Official Syllabus URL:https://www.sas.com/en_us/certification/exam-content-guides/big-data-preparation.html

SASInstitute A00-220 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Statistical Analysis30%- Analysis of Variance (ANOVA)
  • 1. Perform one-way and two-way ANOVA
  • 2. Run post-hoc tests
  • 3. Interpret results and assumptions
- Linear Regression
  • 1. Model selection and diagnostics
  • 2. Residual analysis and validation
  • 3. Build multiple linear regression models
  • 4. Use REG and GLM procedures
- Logistic Regression
  • 1. Assess model fit and performance
  • 2. Perform binary logistic regression using LOGISTIC
  • 3. Interpret coefficients and odds ratios
Topic 2: Visual Exploration20%- SAS Visual Analytics
  • 1. Select and connect to data sources
  • 2. Use interactive filters and drill-downs
  • 3. Interpret and share visual results
  • 4. Create and customize visualizations
  • 5. Apply analytics to visual outputs
Topic 3: Data Preparation50%- Data Management Studio Interface
  • 1. Create and manage data jobs
  • 2. Handle missing values and outliers
  • 3. Profile and assess data quality
  • 4. Cleanse, standardize, and transform data
- Access and integrate data sources
  • 1. Manage metadata and data lineage
  • 2. Work with Hadoop and distributed data
  • 3. Connect to structured and unstructured data

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