Scenario: Your company wants to implement a data warehouse to analyze financial data. However, the finance team frequently updates the account hierarchy structure. How would you handle this scenario using Dimensional Modeling techniques?
- Type 1 Slowly Changing Dimension (SCD)
- Type 2 Slowly Changing Dimension (SCD)
- Type 3 Slowly Changing Dimension (SCD)
- Type 4 Slowly Changing Dimension (SCD)
Using a Type 3 Slowly Changing Dimension (SCD) would allow for tracking changes to the account hierarchy structure in a data warehouse, preserving historical data while accommodating updates made by the finance team.
What does ACID stand for in the context of RDBMS?
- Accuracy, Control, Isolation, Durability
- Association, Coordination, Integration, Distribution
- Atomicity, Consistency, Isolation, Durability
- Authentication, Configuration, Installation, Deployment
ACID stands for Atomicity, Consistency, Isolation, and Durability. It is a set of properties that ensure that database transactions are processed reliably. Atomicity ensures that either all the operations within a transaction are successfully completed or none of them are. Consistency ensures that the database remains in a consistent state before and after the transaction. Isolation ensures that the transactions are isolated from each other. Durability ensures that once a transaction is committed, its changes are permanently stored in the database even in the event of system failures.
Which component of Kafka is responsible for storing the published messages?
- Kafka Broker
- Kafka Consumer
- Kafka Producer
- ZooKeeper
The Kafka Broker is responsible for storing the published messages. It manages the storage and distribution of data across topics in Kafka.
Which of the following best describes the primary purpose of a data warehouse?
- Providing real-time analytics
- Storing historical data for analysis
- Storing raw data for operational processes
- Supporting online transaction processing (OLTP)
The primary purpose of a data warehouse is to store historical data for analysis, enabling organizations to make informed decisions based on trends and patterns over time.
The Kafka ________ is responsible for managing the metadata of topics, partitions, and replicas.
- Broker
- Consumer
- Producer
- Zookeeper
The Kafka Zookeeper is responsible for managing the metadata of topics, partitions, and replicas. It maintains information about the structure and configuration of the Kafka cluster.
A(n) ________ relationship in an ERD indicates that each instance of one entity can be associated with multiple instances of another entity.
- Many-to-Many
- Many-to-One
- One-to-Many
- One-to-One
In an ERD, a Many-to-Many relationship signifies that each instance of one entity can be related to multiple instances of another entity, and vice versa. This relationship is common in database modeling scenarios.
What is the primary goal of data cleansing in the context of data management?
- Enhancing data visualization techniques
- Ensuring data accuracy and consistency
- Facilitating data transmission speed
- Maximizing data storage capacity
The primary goal of data cleansing is to ensure data accuracy and consistency. It involves detecting and correcting errors, inconsistencies, and discrepancies in data to improve its quality and reliability for analysis, decision-making, and other data-driven processes. By removing or rectifying inaccuracies, data cleansing enhances the usability and trustworthiness of the data.
What are the key components of a successful data governance framework?
- Data analytics tools, Data visualization techniques, Data storage solutions, Data security protocols
- Data governance committee, Data governance strategy, Data governance roadmap, Data governance metrics
- Data modeling techniques, Data integration platforms, Data architecture standards, Data access controls
- Data policies, Data stewardship, Data quality management, Data privacy controls
A successful data governance framework comprises several key components that work together to ensure effective management and utilization of data assets. These components include clearly defined data policies outlining how data should be handled, data stewardship roles and responsibilities for overseeing data assets, mechanisms for managing and improving data quality, and controls for safeguarding data privacy. By integrating these components into a cohesive framework, organizations can establish a culture of data governance and drive data-driven decision-making processes.
Which of the following is an example of sensitive data?
- Grocery shopping list
- Public news articles
- Social Security Number (SSN)
- Weather forecasts
An example of sensitive data is a Social Security Number (SSN), which is personally identifiable information (PII) uniquely identifying individuals and often used for official purposes. Sensitive data typically includes any information that, if disclosed or compromised, could lead to financial loss, identity theft, or privacy violations.
Scenario: You are working on a project where data integrity is crucial. A new table is being designed to store employee information. Which constraint would you use to ensure that the "EmployeeID" column in this table always contains unique values?
- Check Constraint
- Foreign Key Constraint
- Primary Key Constraint
- Unique Constraint
A Unique Constraint ensures that the values in the specified column or set of columns are unique across all rows in the table. It is commonly used to enforce uniqueness but does not necessarily imply a primary key or foreign key relationship.