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.

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.

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.

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 variable, and the data structure is subject to frequent changes, as they provide schema flexibility and horizontal scalability to accommodate changing needs.

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.

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.

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 correct answer is Data confidentiality. Data confidentiality is a fundamental principle of data protection that emphasizes restricting access to sensitive information to authorized users only. It involves implementing security measures such as encryption, access controls, and authentication mechanisms to safeguard data from unauthorized access, disclosure, or alteration. By maintaining data confidentiality, organizations can protect sensitive information from unauthorized disclosure, data breaches, and privacy violations, thereby preserving trust and compliance with regulatory requirements.

The concept of authentication factors revolves around verifying the identity of a user before granting access to resources. "Something you need" does not align with the typical authentication factors. The correct factors are: something you know (like a password), something you have (like a security token or smart card), and something you are (biometric identifiers such as fingerprints or facial recognition).