Core Data Concepts (DP-900) Flashcards
Microsoft Azure Data Fundamentals DP-900 Flashcards
| Front | Back |
| Give an example of a non-relational database | MongoDB or Cassandra |
| Give an example of a relational database | MySQL or PostgreSQL |
| Give an example of semi-structured data | JSON or XML files |
| Give an example of structured data | A database table |
| Give an example of unstructured data | Emails or social media posts |
| What are the 4 Vs of big data | Volume, Velocity, Variety, and Veracity |
| What are the two main types of data | Quantitative data and qualitative data |
| What is a data lake | A storage solution that holds enormous amounts of raw data in its native format |
| What is a data schema | A blueprint that defines how data is structured and stored |
| What is a data warehouse | A centralized repository designed for analytical query and reporting |
| What is a non-relational database | A database not based on the relational model, often optimized for unstructured or semi-structured data |
| What is a relational database | A database structured to recognize relationships among stored data |
| What is big data | Extremely large datasets that are difficult to process using traditional tools |
| What is data mining | The process of extracting useful patterns or insights from large data sets |
| What is data redundancy | Duplicating data at multiple locations for backup and fault tolerance purposes |
| What is data storage | A method of saving data for future access and use |
| What is metadata | Data that describes other data such as file size, file type, or creation date |
| What is qualitative data | Non-numerical data that describes qualities or characteristics |
| What is quantitative data | Data that is measurable and expressed in numerical terms |
| What is semi-structured data | Data that has some organizational structure but does not adhere to strict schema rules |
| What is structured data | Data that is organized into rows and columns with clear labels and formats |
| What is the function of data indexing | Facilitates faster retrieval of data by creating a structured access path |
| What is the key advantage of data lakes over data warehouses | Data lakes allow raw data storage for broader processing possibilities |
| What is the main difference between structured and unstructured data | Structured data is organized and easily searchable while unstructured data lacks organization |
| What is the role of ETL in data management | Extract, Transform, and Load processes for data movement between systems |
| What is unstructured data | Data that does not have a predefined format such as text or multimedia content |
About the Flashcards
Flashcards for the Microsoft Azure Data Fundamentals exam guide you through essential data-management principles, from differentiating structured, semi-structured, and unstructured data to recognizing how schemas and metadata organize information. You will review examples like database tables, JSON files, and social media posts while learning why indexing, redundancy, and ETL streamline access and movement of data.
The deck also clarifies the roles of relational versus non-relational databases, data warehouses, and data lakes, then expands into big data concepts such as the Four Vs and data mining techniques. Concise definitions and comparisons help you master key terminology, boost recall, and apply concepts confidently on test day.
Topics covered in this flashcard deck:
- Data types & formats
- Database models
- Storage solutions
- ETL & indexing
- Big data fundamentals