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.

Which storage solution in the Hadoop ecosystem is designed for handling small files and is used as a complementary storage layer alongside HDFS? ________

  • HBase
  • Hadoop Archives (HAR)
  • Hive
  • Kudu
Kudu is a storage solution in the Hadoop ecosystem specifically designed for handling small files efficiently. It serves as a complementary storage layer alongside Hadoop Distributed File System (HDFS) and is optimized for workloads involving random access to data, such as time-series data or small analytical queries.

How does Data Lake architecture facilitate data exploration and analysis?

  • Centralized data storage, Schema-on-read approach, Scalability, Flexibility
  • Data duplication, Data redundancy, Data isolation, Data normalization
  • Schema-on-write approach, Predefined schemas, Data silos, Tight integration with BI tools
  • Transactional processing, ACID compliance, Real-time analytics, High consistency
Data Lake architecture facilitates data exploration and analysis through centralized storage, a schema-on-read approach, scalability, and flexibility. This allows users to analyze diverse data sets without predefined schemas, promoting agility and innovation.