Scenario: A security breach occurs in your Data Lake, resulting in unauthorized access to sensitive data. How would you respond to this incident and what measures would you implement to prevent similar incidents in the future?
- Data Backup Procedures, Data Replication Techniques, Disaster Recovery Plan, Data Masking Techniques
- Data Normalization Techniques, Query Optimization, Data Compression Techniques, Database Monitoring Tools
- Data Validation Techniques, Data Masking Techniques, Data Anonymization, Data Privacy Policies
- Incident Response Plan, Data Encryption, Access Control Policies, Security Auditing
In response to a security breach in a Data Lake, an organization should enact its incident response plan, implement data encryption to protect sensitive data, enforce access control policies to limit unauthorized access, and conduct security auditing to identify vulnerabilities. Preventative measures may include regular data backups, disaster recovery plans, and data masking techniques to obfuscate sensitive information.
In data pipeline monitoring, ________ is the process of identifying and analyzing deviations from expected behavior.
- Anomaly detection
- Data aggregation
- Data transformation
- Data validation
Anomaly detection in data pipeline monitoring involves identifying and analyzing deviations from the expected behavior of the pipeline. This process often employs statistical techniques, machine learning algorithms, or predefined rules to detect unusual patterns or outliers in the data flow, which may indicate errors, bottlenecks, or data quality issues within the pipeline.
________ measures the degree to which data is free from errors.
- Data Accuracy
- Data Completeness
- Data Consistency
- Data Validity
Data Accuracy measures the extent to which data is free from errors, inaccuracies, or mistakes. It evaluates the correctness of data values in relation to the real-world entities they represent. High data accuracy ensures that the data reflects the true state of the system and supports informed decision-making and analysis.
What is a primary feature that distinguishes NoSQL databases from traditional relational databases?
- ACID compliance
- Horizontal scalability
- Schema normalization
- Strong consistency
One of the primary features that distinguish NoSQL databases from traditional relational databases is horizontal scalability, which allows them to efficiently handle large volumes of data by adding more nodes to the database cluster.
Scenario: You are tasked with designing a scalable architecture for an e-commerce platform. How would you approach database design to ensure scalability and performance under high traffic loads?
- Denormalizing the database schema
- Implementing sharding
- Utilizing a single monolithic database
- Vertical scaling by adding more resources to existing servers
Sharding involves partitioning data across multiple database instances, allowing for horizontal scaling and distributing the workload evenly. It enables the system to handle increased traffic by spreading data and queries across multiple servers. This approach enhances scalability and performance by reducing the load on individual database servers.
Which of the following best describes a characteristic of NoSQL databases?
- Fixed schema
- Flexible schema
- Limited scalability
- Strong consistency
NoSQL databases typically offer a flexible schema, allowing for the storage of various types of data without the need to adhere to a rigid structure like in traditional relational databases.
What is the primary purpose of data lineage in metadata management?
- Encrypting sensitive data
- Optimizing database performance
- Storing backup copies of data
- Tracking the origin and transformation of data
Data lineage in metadata management primarily serves the purpose of tracking the origin, transformation, and movement of data throughout its lifecycle. It provides insights into how data is sourced, processed, and utilized across various systems and processes, facilitating data governance, compliance, and decision-making. Understanding data lineage helps organizations ensure data quality, lineage, and regulatory compliance.
The logical data model focuses on defining ________, attributes, and relationships between entities.
- Constraints
- Entities
- Tables
- Transactions
The logical data model focuses on defining entities, attributes, and relationships between entities, providing a structured representation of the data independent of any specific database technology or implementation.
In data transformation, the process of combining data from multiple sources into a single, unified dataset is known as ________.
- Data Aggregation
- Data Cleansing
- Data Integration
- Data Normalization
Data Integration is the process of combining data from different sources into a single, unified dataset. This involves merging, cleaning, and structuring the data to ensure consistency and reliability.
In streaming processing, data is processed ________ as it arrives.
- Continuously
- Intermittently
- Periodically
- Retroactively
In streaming processing, data is processed continuously as it arrives, without the need to wait for the entire dataset to be collected. This enables real-time analysis, monitoring, and decision-making based on fresh data streams. Streaming processing systems are designed to handle high data velocity and provide low-latency insights into rapidly changing data streams, making them suitable for applications like real-time analytics, fraud detection, and IoT (Internet of Things) data processing.