Data engineering is a field of computer science and information technology that focuses on the design, development, and management of data pipelines and infrastructure to support data-driven applications and analytics. It involves the process of collecting, transforming, and storing data in a way that enables efficient data processing and analysis.
Data engineering is a crucial aspect of the data lifecycle, as it ensures that data is reliable, accessible, and ready for analysis by data scientists, analysts, and other stakeholders. Data engineers work closely with data scientists, database administrators, and software developers to build and maintain data pipelines, databases, and data warehouses.
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Article overview
- Part 1: OnlineExamMaker – Generate and share data engineering quiz with AI automatically
- Part 2: 30 data engineering quiz questions & answers
- Part 3: Download data engineering questions & answers for free
Part 1: OnlineExamMaker – Generate and share data engineering quiz with AI automatically
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Part 2: 30 data engineering quiz questions & answers
1. What is data engineering?
a) Analyzing data patterns
b) Designing data visualizations
c) Managing and processing data pipelines
d) Predicting future trends based on data
Answer: c) Managing and processing data pipelines
2. What is the primary goal of data engineering?
a) Creating data visualizations
b) Building machine learning models
c) Managing data storage
d) Preparing data for analysis
Answer: d) Preparing data for analysis
3. Which of the following tasks is NOT a part of data engineering?
a) Data collection
b) Data visualization
c) Data transformation
d) Data storage
Answer: b) Data visualization
4. What technology is commonly used for distributed storage and processing of big data in data engineering?
a) Apache Kafka
b) Amazon Redshift
c) Apache Spark
d) Microsoft Excel
Answer: c) Apache Spark
5. What is the process of cleaning, normalizing, and transforming raw data to make it suitable for analysis?
a) Data integration
b) Data warehousing
c) Data preparation
d) Data visualization
Answer: c) Data preparation
6. Which tool is commonly used for data integration in data engineering?
a) Apache NiFi
b) Amazon S3
c) Microsoft Excel
d) Google BigQuery
Answer: a) Apache NiFi
7. What is the purpose of a data warehouse in data engineering?
a) Real-time data processing
b) Data storage for business analytics
c) Data transformation for machine learning
d) Data visualization for stakeholders
Answer: b) Data storage for business analytics
8. What type of data is typically processed in data engineering?
a) Structured data
b) Unstructured data
c) Relational data
d) Customer reviews
Answer: a) Structured data
9. Which cloud service provides scalable infrastructure for data storage and processing in data engineering?
a) Amazon Web Services (AWS)
b) Microsoft Word
c) Apache Hadoop
d) Apache Cassandra
Answer: a) Amazon Web Services (AWS)
10. What is the process of orchestrating complex data pipelines in data engineering?
a) Data transformation
b) Data governance
c) Data pipeline orchestration
d) Data visualization
Answer: c) Data pipeline orchestration
11. What does data engineering help in achieving?
a) Efficient data storage
b) Real-time data visualization
c) Data analysis without any preparation
d) Data-driven decision making
Answer: d) Data-driven decision making
12. Which technology is used for managing and scheduling data processing workflows in data engineering?
a) Apache Spark
b) Amazon Redshift
c) Apache Airflow
d) Google BigQuery
Answer: c) Apache Airflow
13. What is the primary purpose of data engineering in data-driven organizations?
a) To create data visualizations
b) To build machine learning models
c) To manage and process data efficiently
d) To predict future trends based on data
Answer: c) To manage and process data efficiently
14. Which of the following is NOT a component of data engineering?
a) Data collection
b) Data transformation
c) Data visualization
d) Data storage
Answer: c) Data visualization
15. What technology is commonly used for distributed storage and processing of big data in data engineering?
a) Apache Kafka
b) Amazon Redshift
c) Apache Spark
d) Microsoft Excel
Answer: c) Apache Spark
Part 3: Download data engineering questions & answers for free
Download questions & answers for free
16. What is the process of cleaning, normalizing, and transforming raw data to make it suitable for analysis?
a) Data integration
b) Data warehousing
c) Data preparation
d) Data visualization
Answer: c) Data preparation
17. Which tool is commonly used for data integration in data engineering?
a) Apache NiFi
b) Amazon S3
c) Microsoft Excel
d) Google BigQuery
Answer: a) Apache NiFi
18. What is the purpose of a data warehouse in data engineering?
a) Real-time data processing
b) Data storage for business analytics
c) Data transformation for machine learning
d) Data visualization for stakeholders
Answer: b) Data storage for business analytics
19. What type of data is typically processed in data engineering?
a) Structured data
b) Unstructured data
c) Relational data
d) Customer reviews
Answer: a) Structured data
20. Which cloud service provides scalable infrastructure for data storage and processing in data engineering?
a) Amazon Web Services (AWS)
b) Microsoft Word
c) Apache Hadoop
d) Apache Cassandra
Answer: a) Amazon Web Services (AWS)
21. What is the process of orchestrating complex data pipelines in data engineering?
a) Data transformation
b) Data governance
c) Data pipeline orchestration
d) Data visualization
Answer: c) Data pipeline orchestration
22. What does data engineering help in achieving?
a) Efficient data storage
b) Real-time data visualization
c) Data analysis without any preparation
d) Data-driven decision making
Answer: d) Data-driven decision making
23. Which technology is used for managing and scheduling data processing workflows in data engineering?
a) Apache Spark
b) Amazon Redshift
c) Apache Airflow
d) Google BigQuery
Answer: c) Apache Airflow
24. What is the primary purpose of data engineering in data-driven organizations?
a) To create data visualizations
b) To build machine learning models
c) To manage and process data efficiently
d) To predict future trends based on data
Answer: c) To manage and process data efficiently
25. Which of the following is NOT a component of data engineering?
a) Data collection
b) Data transformation
c) Data visualization
d) Data storage
Answer: c) Data visualization
26. What technology is commonly used for distributed storage and processing of big data in data engineering?
a) Apache Kafka
b) Amazon Redshift
c) Apache Spark
d) Microsoft Excel
Answer: c) Apache Spark
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27. What is the process of cleaning, normalizing, and transforming raw data to make it suitable for analysis?
a) Data integration
b) Data warehousing
c) Data preparation
d) Data visualization
Answer: c) Data preparation
28. Which tool is commonly used for data integration in data engineering?
a) Apache NiFi
b) Amazon S3
c) Microsoft Excel
d) Google BigQuery
Answer: a) Apache NiFi
29. What is the purpose of a data warehouse in data engineering?
a) Real-time data processing
b) Data storage for business analytics
c) Data transformation for machine learning
d) Data visualization for stakeholders
Answer: b) Data storage for business analytics
30. What type of data is typically processed in data engineering?
a) Structured data
b) Unstructured data
c) Relational data
d) Customer reviews
Answer: a) Structured data