Which of the following is an example of a data quality metric?
- Data accuracy
- Data diversity
- Data quantity
- Data velocity
Data accuracy is an example of a data quality metric. It measures the extent to which data values correctly represent the real-world objects or events they are intended to describe. High data accuracy indicates that the information in the dataset is reliable and free from errors, while low accuracy suggests inaccuracies or discrepancies that may impact decision-making and analysis. Assessing and maintaining data accuracy is essential for ensuring the credibility and trustworthiness of organizational data assets.
What is a stored procedure in the context of RDBMS?
- A precompiled set of SQL statements that can be executed
- A schema that defines the structure of a database
- A temporary table used for intermediate processing
- A virtual table representing the result of a SELECT query
A stored procedure in the context of RDBMS is a precompiled set of SQL statements that can be executed as a single unit. It allows for modularizing and reusing code, enhancing performance, and improving security by controlling access to database operations.
In the context of database performance, what role does indexing play?
- Enhancing data integrity by enforcing constraints
- Facilitating data manipulation through SQL queries
- Improving data retrieval speed by enabling faster lookup
- Minimizing data redundancy by organizing data efficiently
Indexing plays a crucial role in enhancing database performance by improving data retrieval speed. It involves creating data structures (indexes) that enable faster lookup of records based on specific columns or expressions commonly used in queries. By efficiently locating relevant data without scanning the entire dataset, indexing reduces query processing time and enhances overall system responsiveness, especially for frequently accessed data.
How does indexing impact write operations (e.g., INSERT, UPDATE) in a database?
- Indexing can slow down write operations due to the overhead of maintaining indexes
- Indexing depends on the type of database engine being used
- Indexing has no impact on write operations
- Indexing speeds up write operations by organizing data efficiently
Indexing can slow down write operations because every INSERT or UPDATE operation requires the index to be updated, which adds overhead. This trade-off between read and write performance should be carefully considered when designing databases.
The process of converting categorical data into numerical values during data transformation is called ________.
- Aggregation
- Deduplication
- Encoding
- Normalization
Encoding is the process of converting categorical data into numerical values, allowing for easier analysis and processing. Common techniques include one-hot encoding and label encoding.
Which data quality metric assesses the degree to which data conforms to predefined rules?
- Accuracy
- Completeness
- Consistency
- Validity
Validity is a data quality metric that evaluates whether data adheres to predefined rules or constraints. It assesses the correctness and appropriateness of data based on established criteria, ensuring that data meets specified standards and requirements. Valid data contributes to the overall reliability and usefulness of information within a dataset.
What is the primary purpose of an ETL (Extract, Transform, Load) tool such as Apache NiFi or Talend?
- Extracting data from various sources and loading it into a destination
- Loading data into a data warehouse
- Monitoring data flow in real-time
- Transforming data from one format to another
The primary purpose of an ETL tool like Apache NiFi or Talend is to extract data from disparate sources, transform it as required, and load it into a target destination, such as a data warehouse or database.
NoSQL databases are often used in scenarios where the volume of data is ________, and the data structure is subject to frequent changes.
- High
- Low
- Moderate
- Variable
NoSQL databases are often used in scenarios where the volume of data is variable, and the data structure is subject to frequent changes, as they provide schema flexibility and horizontal scalability to accommodate changing needs.
How does Kafka ensure fault tolerance and high availability?
- Enforcing strict data retention policies
- Implementing strict message ordering
- Increasing network bandwidth
- Replication of data across multiple brokers
Kafka ensures fault tolerance and high availability by replicating data across multiple brokers. This redundancy ensures that if one broker fails, data can still be retrieved from other replicas, ensuring continuity.
Scenario: A large organization is facing challenges in ensuring data consistency across departments. How can a data governance framework help in addressing this issue?
- By conducting regular data audits and implementing access controls to enforce data integrity.
- By defining standardized data definitions and establishing data stewardship roles to oversee data quality and consistency.
- By deploying real-time data synchronization solutions to maintain consistency across distributed systems.
- By implementing data encryption techniques to prevent unauthorized access and ensure data security.
A data governance framework can help address challenges in ensuring data consistency across departments by defining standardized data definitions, formats, and structures. It involves establishing data governance policies and procedures to ensure consistent data interpretation and usage across the organization. Additionally, assigning data stewardship roles and responsibilities can help oversee data quality and consistency, ensuring that data is accurate, complete, and reliable across departments.