Data cleansing often involves removing or correcting ________ in a dataset.
- Anomalies
- Correlations
- Errors
- Outliers
Data cleansing typically involves identifying and correcting errors in a dataset, which can include incorrect values, missing values, or inconsistencies. These errors can arise due to various reasons such as data entry mistakes, system errors, or data integration issues. Addressing these errors is crucial for ensuring the accuracy and reliability of the data for analysis and decision-making purposes.
The process of assessing the quality of data and identifying potential issues is known as ________.
- Data governance
- Data profiling
- Data stewardship
- Data validation
Data profiling involves analyzing and examining the characteristics and quality of data to understand its structure, content, and potential issues. It includes tasks such as assessing data completeness, consistency, accuracy, and integrity to identify anomalies, patterns, and outliers. Data profiling helps organizations gain insights into their data assets, prioritize data quality improvements, and make informed decisions regarding data management strategies and processes.
Scenario: You're working on a project where data consistency is critical, and the system needs to handle rapid scaling. How would you address these requirements using NoSQL databases?
- Combine multiple NoSQL databases
- Implement eventual consistency
- Use a database with strong consistency model
- Utilize sharding and replication for scaling
In a project where data consistency is critical and rapid scaling is required, using a NoSQL database with a strong consistency model ensures data integrity. This may involve sacrificing some scalability for consistency.
What are some potential drawbacks of over-indexing a database?
- Enhanced data consistency
- Improved query performance
- Increased storage space and maintenance overhead
- Reduced likelihood of index fragmentation
Over-indexing a database can lead to increased storage space and maintenance overhead. It may also slow down data modification operations and increase the likelihood of index fragmentation, affecting overall performance.
What strategies can be employed to ensure scalability in data modeling projects?
- Consistent use of primary keys
- Implementation of complex queries
- Normalization and denormalization
- Vertical and horizontal partitioning
Strategies such as vertical and horizontal partitioning allow for distributing data across multiple resources, ensuring scalability by accommodating growing data volumes and supporting efficient data retrieval.
The SQL command used to permanently remove a table from the database is ________.
- DELETE TABLE
- DESTROY TABLE
- DROP TABLE
- REMOVE TABLE
The DROP TABLE command is used in SQL to permanently remove a table and all its data from the database. It's important to exercise caution when using this command as it cannot be undone.
Which of the following is an example of a data cleansing tool commonly used to identify and correct inconsistencies in datasets?
- Apache Kafka
- MongoDB
- OpenRefine
- Tableau
OpenRefine is a popular data cleansing tool used to identify and correct inconsistencies in datasets. It provides features for data transformation, cleaning, and reconciliation, allowing users to explore, clean, and preprocess large datasets efficiently. With its intuitive interface and powerful functionalities, OpenRefine is widely used in data preparation workflows across various industries.
Scenario: A financial institution wants to implement real-time fraud detection. Outline the key components and technologies you would recommend for building such a system.
- Apache Beam for data processing, RabbitMQ for message queuing, Neural networks for fraud detection, Redis for caching
- Apache Kafka for data ingestion, Apache Flink for stream processing, Machine learning models for fraud detection, Apache Cassandra for storing transaction data
- Apache NiFi for data ingestion, Apache Storm for stream processing, Decision trees for fraud detection, MongoDB for storing transaction data
- MySQL database for data storage, Apache Spark for batch processing, Rule-based systems for fraud detection, Elasticsearch for search and analytics
Implementing real-time fraud detection in a financial institution requires a robust combination of technologies. Apache Kafka ensures reliable data ingestion, while Apache Flink enables real-time stream processing for immediate fraud detection. Machine learning models trained on historical data can identify fraudulent patterns, with Apache Cassandra providing scalable storage for transaction data.
Scenario: You are tasked with designing a data warehouse for a retail company to analyze sales data. Which Dimensional Modeling technique would you use to represent the relationships between products, customers, and sales transactions most efficiently?
- Bridge Table
- Fact Constellation
- Snowflake Schema
- Star Schema
A Star Schema would be the most efficient Dimensional Modeling technique for representing relationships between products, customers, and sales transactions, as it simplifies queries and optimizes performance.
Hadoop YARN stands for Yet Another Resource ________.
- Navigator
- Negotiating
- Negotiation
- Negotiator
Hadoop YARN stands for Yet Another Resource Negotiating. It is a resource management layer in Hadoop that manages resources and schedules tasks across the cluster, enabling efficient resource utilization.