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.
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.
What role does data validation play in the data loading process?
- Enhancing data visualization and reporting
- Ensuring data integrity and quality
- Optimizing data storage and retrieval
- Streamlining data transformation and cleansing
Data validation is crucial in ensuring that the loaded data meets quality standards and integrity constraints. It helps prevent errors and inconsistencies, ensuring the reliability of downstream processes.
In Hadoop MapReduce, what is the function of the Map phase?
- Aggregates the output of the Reduce phase
- Converts input into key-value pairs
- Distributes tasks to worker nodes
- Sorts the input data
The Map phase in Hadoop MapReduce takes input data and processes it to generate key-value pairs, which are then passed to the Reduce phase for further processing. It involves tasks like data parsing and filtering.
What is the main purpose of a wide-column store NoSQL database?
- Designed for transactional consistency
- Optimal for storing and querying large amounts of data
- Primarily used for key-value storage
- Suitable for highly interconnected data
A wide-column store NoSQL database is designed for efficiently storing and querying large volumes of data, typically organized in column families, making it optimal for analytical and big data workloads.