Data+ (V2) exam objectives summary

Data concepts and environments (20%)

• Explain data concepts: Database types, data structures, file extensions, and data types.
• Identify data sources: Databases, APIs, website data, files, logs and repositories. 
• Recognize infrastructure concepts: Cloud, on-premise, storage, and containerization.
• Identify data tools: Coding environments, BI software, and analysis platforms. 
• Understand AI concepts: Identify AI models, natural language processing, and robotic automation. 

Data acquisition and preparation (22%)

• Use data acquisition methods: Data integration and queries to gather and combine data. 
• Perform data exploration: Find missing values, duplication, redundancy, or outliers. 
• Apply data transformation: Cleansing, merging, parsing, and formatting data. 

Data analysis (24%)

• Communicate analysis results: Select methods for different audiences. 
• Select statistical methods: Apply basic statistical techniques to data. 
• Troubleshoot analysis issues: Use tools and resources to resolve problems. 

Visualization and reporting (20%) 

• Create effective visuals: Use charts, maps, tables, and design elements. 
• Deliver reports: Provide dashboards or summaries using appropriate methods. 
• Validate reporting accuracy: Apply validation and review to solve reporting issues

Data governance (14%)

• Explain data management practices: Documentation, versioning, and data lineage. 
• Summarize compliance requirements: Retention, audits, and regulations. 
• Compare privacy and protection strategies: Access control, encryption, and masking. 
• Implement quality assurance: Profiling, monitoring, and testing for data quality.