✨Free Sample Questions for Your Microsoft DP-900 Exam Your Success

Study Notes

Data is raw facts or figures, often unorganized, and without inherent meaning.
Data is the foundation of information.
Data types include numerical, textual, dates, and images.
Data can be structured (organized in tables) or unstructured (not organized).
Data quality refers to accuracy, completeness, consistency, and timeliness of data.
Data governance is essential for data management, ensuring policies and procedures are followed.
Metadata describes data and its characteristics.

Databases are organized collections of data, stored electronically in a structured format.
Database management systems (DBMS) provide a way to manage data efficiently.
Relational databases store data in tables related by keys.
NoSQL databases offer flexible schemas and scalability for large datasets.
Cloud storage offers remote storage solutions like Azure Blob Storage and OneDrive.
Data warehouses store integrated data for business analysis.
Data lakes store large volumes of raw data in its native format.

Data processing involves transforming raw data into useful information.
ETL (Extract, Transform, Load) processes data from various sources, transforms it, and loads it into a target system.
Data mining identifies patterns and trends in data.
Data aggregation combines data from multiple sources.
Data cleansing improves data quality by removing inconsistencies and errors.
Data visualization transforms data into graphs and charts to enhance understanding.
Machine learning algorithms are used for pattern analysis and prediction.

Data security protects data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data encryption converts data into an unreadable format to protect it.
Access controls manage who can access data.
Data loss prevention (DLP) systems prevent sensitive information from leaving the organization.
Compliance with regulations (e.g., GDPR, HIPAA) requires data security measures.
Data backups and recovery plans are essential for data protection.
Incident response plans outline procedures for dealing with data breaches.

Data models define how data is organized and related.
Entity-relationship diagrams (ERDs) visually represent data relationships.
Primary keys uniquely identify records in a table.
Foreign keys establish relationships between tables.
Normalization reduces data redundancy and improves data integrity.
Data integrity ensures data accuracy, consistency, and validity.
Data dictionaries document data elements, structures, and relationships.
Data flow diagrams (DFDs) illustrate the movement of data within a system.
Data warehousing involves collecting, storing, and managing data from various sources for business analysis.
Data marts are smaller, focused subsets of data warehouses.
Big data refers to large, complex, and varied datasets that traditional data processing methods struggle to manage.
Data analytics involves using various techniques to extract meaning from data.
Business intelligence (BI) uses data to improve decision-making.