Data Processing JAMB Past Questions And Answers (Objectives and Theory)
Section A: Multiple Choice Questions
What is the primary objective of data processing?
a) Data storage
b) Data analysis
c) Data manipulation
d) Data security
Answer: b) Data analysis
Which of the following is NOT a stage of data processing?
a) Data collection
b) Data presentation
c) Data transmission
d) Data disposal
Answer: d) Data disposal
What is the purpose of data validation in data processing?
a) To ensure data accuracy and consistency
b) To increase data volume
c) To delete irrelevant data
d) To encrypt sensitive data
Answer: a) To ensure data accuracy and consistency
Which of the following is an example of structured data?
a) Emails
b) Images
c) Spreadsheets
d) Videos
Answer: c) Spreadsheets
What does the term “data mining” refer to in data processing?
a) Extracting useful information from data
b) Deleting irrelevant data
c) Encrypting sensitive data
d) Backing up data
Answer: a) Extracting useful information from data
What is the purpose of data normalization in data processing?
a) To reduce data redundancy
b) To increase data accuracy
c) To speed up data processing
d) To delete duplicate data
Answer: a) To reduce data redundancy
Which of the following is NOT a data processing technique?
a) Batch processing
b) Real-time processing
c) Parallel processing
d) Data compression
Answer: d) Data compression
What does the term “data backup” refer to in data processing?
a) Deleting data permanently
b) Copying data to secondary storage for safekeeping
c) Encrypting data for security
d) Modifying data for analysis
Answer: b) Copying data to secondary storage for safekeeping
Which of the following is NOT a type of data processing system?
a) Transaction processing system
b) Management information system
c) Decision support system
d) Data encryption system
Answer: d) Data encryption system
What is the primary goal of data processing systems?
a) To increase data complexity
b) To ensure data security
c) To provide accurate and timely information
d) To reduce data volume
Answer: c) To provide accurate and timely information
Section B: Theory Questions
Define data processing and discuss its importance in modern organizations.
Answer: Data processing is the conversion of raw data into meaningful information through various operations such as collection, organization, manipulation, analysis, and presentation. It plays a crucial role in modern organizations by providing accurate, timely, and relevant information for decision-making, planning, and operations. Data processing enables organizations to streamline business processes, improve efficiency, identify trends and patterns, enhance customer experiences, and gain insights into market dynamics and competitive landscapes. It supports various functions such as sales, marketing, finance, human resources, and supply chain management, helping organizations achieve their goals and objectives.
Explain the stages of data processing and discuss the activities involved in each stage.
*Answer: The stages of data processing typically include:
Data collection: Gathering raw data from various sources such as sensors, databases, forms, surveys, and transactions.
Data preparation: Cleaning, filtering, and transforming raw data into a format suitable for processing and analysis.
Data input: Entering data into the system using manual or automated methods such as keyboards, scanners, or sensors.
Data processing: Performing operations such as sorting, filtering, aggregating, calculating, and analyzing data to extract useful information.
Data storage: Storing processed data in databases, data warehouses, or other storage systems for future retrieval and reference.
Data output: Presenting processed data in a meaningful format such as reports, charts, graphs, dashboards, or visualizations for decision-making and analysis.
Data interpretation: Interpreting and analyzing the output to draw insights, make predictions, and inform decision-making.*
Discuss the importance of data validation and data normalization in data processing.
*Answer: Data validation and data normalization are essential techniques in data processing for ensuring data accuracy, consistency, and integrity.
Data validation involves checking data for errors, inconsistencies, and inaccuracies to ensure that it meets specified criteria or standards. It helps identify and correct errors in data entry, eliminate duplicate records, and maintain data quality throughout the processing pipeline.
Data normalization involves organizing data into a standardized format to reduce redundancy and improve efficiency in storage, retrieval, and analysis. It eliminates data anomalies such as duplicate entries, update anomalies, and deletion anomalies by breaking down complex data structures into smaller, more manageable units and establishing relationships between them. Data normalization reduces data redundancy, minimizes data inconsistency, and enhances data integrity, making it easier to maintain and update databases and improve overall system performance.*
Explain the concept of batch processing and real-time processing in data processing systems.
*Answer:
Batch processing is a data processing technique where data is collected, processed, and stored in batches or groups at scheduled intervals. It involves executing a series of predefined tasks or jobs on a set of data records without user interaction. Batch processing is suitable for handling large volumes of data that do not require immediate processing and can be processed offline or during non-peak hours. It is commonly used in scenarios such as payroll processing, billing, report generation, and data backups.
Real-time processing, also known as online processing or transaction processing, is a data processing technique where data is processed and analyzed as soon as it becomes available. It involves capturing, processing, and responding to data in near real-time or with minimal delay. Real-time processing is essential for time-sensitive applications such as financial transactions, stock trading, sensor data analysis, and monitoring systems. It enables organizations to make immediate decisions, detect anomalies, and respond to events as they occur, providing a competitive advantage and improving operational efficiency.
