Interview Questions for Data Engineer Role

Here are 50 multiple-choice interview questions for a Data Engineer position.
These questions can serve as a preparation kit or a quick refresher before you appear in a Data Engineer interview.

(Refer the answer key at the end)

1. What is the primary role of a Data Engineer?
   a) Data analysis
   b) Data visualization
   c) Data storage and processing
   d) Data presentation
2. Which of the following best describes ETL in the context of data engineering?
   a) Extract, Transfer, Load
   b) Extract, Transform, Load
   c) Export, Transform, Load
   d) Extract, Transfer, Log
3. In a data engineering context, what does “Normalization” refer to?
   a) Removing duplicate records from a dataset
   b) Scaling data to a common range
   c) Structuring data to reduce redundancy
   d) Converting data to a different format
4. What is the primary purpose of a data warehouse in data engineering?
   a) Real-time data processing
   b) Long-term data storage and analysis
   c) Data cleansing and preparation
   d) Data visualization
5. What is the difference between a data lake and a data warehouse?
   a) Data lakes store structured data, while data warehouses store unstructured data.
   b) Data lakes store data in its raw form, while data warehouses store processed data.
   c) Data lakes are used for data analysis, while data warehouses are used for data storage.
   d) Data lakes and data warehouses are interchangeable terms.
6. Which data modeling technique is commonly used in data engineering to represent relationships between entities?
   a) Entity-Attribute-Value (EAV) model
   b) Star schema
   c) Hierarchical model
   d) NoSQL model
7. What is the purpose of data partitioning in data engineering?
   a) To remove duplicate data
   b) To organize data into subsets for efficient querying
   c) To create data visualizations
   d) To encrypt data at rest
8. What is the primary role of Apache Spark in data engineering?
   a) Data storage
   b) Data transformation and processing
   c) Data visualization
   d) Data modeling
9. In data engineering, what does “OLAP” stand for?
   a) Online Logistics and Processing
   b) Online Analytical Processing
   c) Offline Application Programming
   d) Online Language for Application Programming
10. What is the purpose of data serialization in data engineering?
    a) To visualize data
    b) To transform data into a binary format for storage or transmission
    c) To remove missing values from data
    d) To convert unstructured data into structured data
11. What is a key characteristic of a NoSQL database in data engineering?
    a) Strict schema and fixed structure
    b) Horizontal scalability
    c) ACID transactions
    d) Limited data storage capacity
12. Which tool or language is commonly used for real-time data stream processing in data engineering?
    a) Apache Hadoop
    b) SQL
    c) Apache Kafka
    d) Python
13. What is the primary goal of data quality assessment in data engineering?
    a) To ensure data is free from any errors or inconsistencies
    b) To increase data storage capacity
    c) To reduce data processing speed
    d) To visualize data
14. What does “CDC” stand for in data engineering?
    a) Center for Data Control
    b) Change Data Capture
    c) Central Data Cleaning
    d) Continuous Data Collection
15. In data engineering, what is the purpose of data denormalization?
    a) To remove duplicates from a dataset
    b) To transform data into a binary format
    c) To increase data storage efficiency
    d) To improve query performance
16. What is the role of “ETL” tools in data engineering?
    a) Data visualization
    b) Data storage
    c) Data extraction, transformation, and loading
    d) Data modeling
17. Which type of database is suitable for complex querying and data analysis in data engineering?
    a) NoSQL database
    b) Columnar database
    c) Key-Value database
    d) Document database
18. What is the primary purpose of data indexing in data engineering?
    a) To encrypt data at rest
    b) To organize data into partitions
    c) To optimize data retrieval speed
    d) To remove duplicate data
19. In data engineering, what does “Hive” refer to?
    a) A type of data visualization tool
    b) A distributed data warehousing system built on Hadoop
    c) A data cleansing technique
    d) A data modeling language
20. Which data format is commonly used for large-scale data storage and processing in data engineering?
    a) JSON
    b) XML
    c) Avro
    d) HTML
21. What is the primary purpose of data lineage in data engineering?
    a) To remove duplicate data
    b) To track the origins and transformations of data
    c) To store data in a binary format
    d) To improve data modeling
22. In data engineering, what is the role of a data catalog?
    a) To visually represent data
    b) To manage metadata and data discovery
    c) To store data in a compressed format
    d) To remove data duplicates
23. What is the primary goal of data governance in data engineering?
    a) To remove sensitive data from a dataset
    b) To ensure data quality and compliance with regulations
    c) To visualize data
    d) To process data in real-time
24. Which query language is commonly used for querying and manipulating data in Hadoop’s ecosystem in data engineering?
    a) SQL
    b) Python
    c) HQL (Hive Query Language)
    d) NoSQL
25. In data engineering, what does “Kafka” refer to?
    a) A data modeling language
    b) A real-time data streaming platform
    c) A data visualization tool
    d) A data storage format
26. What is the primary role of a data engineer in data governance?
    a) Defining business strategies
    b) Ensuring compliance with data regulations and standards
    c) Creating data visualizations
    d) Performing data analysis
27. What is the purpose of data encryption in data engineering?
    a) To increase data storage capacity
    b) To make data accessible to everyone
    c) To secure data and protect it from unauthorized access
    d) To remove duplicate data
28. What does “ELT” stand for in data engineering?
    a) Efficient Loading Technique
    b) Extract, Load, Transfer
    c) Extract, Load, Transform
    d) Extract, Load, Test
29. Which type of database is optimized for high-speed read and write operations in data engineering?
    a) Data warehouse
    b) Data lake
    c) In-memory database
    d) NoSQL database
30. In data engineering, what is the role of a data pipeline?
    a) Creating data visualizations
    b) Managing data storage
    c) Moving and processing data from source to destination
    d) Performing data analysis
31. What is the purpose of data sharding in data engineering?
    a) To remove duplicates from data
    b) To improve data security
    c) To split data into smaller, manageable partitions
    d) To convert data to a different format
32. In data engineering, what is the primary goal of data lineage tracking?
    a) To create data visualizations
    b) To track the transformations applied to data
    c) To encrypt data at rest
    d) To remove sensitive information from data
33. What is the role of a data steward in data governance?
    a) Ensuring compliance with data regulations
    b) Building data pipelines
    c) Performing data analysis
    d) Creating data visualizations
34. In data engineering, what does “DAG” stand for in the context of workflow scheduling?
    a) Data Analysis Graph
    b) Directed Acyclic Graph
    c) Data Access Gateway
    d) Data Aggregation
35. What is the primary role of a data warehouse architect in data engineering?
    a) Writing SQL queries
    b) Designing and optimizing data storage and retrieval systems
    c) Ensuring data quality
    d) Data visualization
36. Which technology is commonly used for large-scale batch data processing in data engineering?
    a) Apache Kafka
    b) Spark Streaming
    c) Apache Flink
    d) Hadoop MapReduce
37. In data engineering, what does “data skew” refer to?
    a) Data with sharp spikes
    b) Biased data distribution in a dataset
    c) Data with missing values
    d) The smallest unit of data
38. What is the purpose of “schema-on-read” in data engineering?
    a) To design data schemas in advance
    b) To delay schema creation until data is read
    c) To optimize data storage
    d) To encrypt data at rest
39. In data engineering, what does “ETT” stand for in the ETT process?
    a) Extract, Transfer, Transform
    b) Extract, Transform, Transfer
    c) Explore, Transform, Transfer
    d) Extract, Transport, Transmit
40. What is the primary role of a data pipeline orchestrator in data engineering?
    a) Data storage and retrieval
    b) Coordinating data workflows and dependencies
    c) Data modeling
    d) Data visualization
41. What does “ACID” stand for in the context of database transactions in data engineering?
    a) Acidic Cleaning and Disinfection
    b) Atomic, Consistent, Isolated, Durable
    c) Advanced Computing and Integration Design
    d) Accelerated Data Processing
42. In data engineering, what is the primary purpose of data compression techniques?
    a) To increase storage capacity
    b) To reduce data security
    c) To improve data storage efficiency
    d) To visualize data
43. What is the primary goal of “data profiling” in data engineering?
    a) To eliminate data duplication
    b) To perform data analysis
    c) To assess the quality and structure of data
    d) To extract data from external sources
44. What is the role of “columnar storage” in data engineering?
    a) To store data in a tabular format
    b) To store data in a single column
    c) To remove data duplicates
    d) To encrypt data at rest
45. In data engineering, what is the primary purpose of a “data dictionary”?
    a) To visualize data
    b) To manage data storage
    c) To store metadata and data definitions
    d) To track data lineage
46. Which technology is commonly used for stream data processing and real-time analytics in data engineering?
    a) Apache Hadoop
    b) Apache Cassandra
    c) Apache Storm
    d) Apache Spark
47. What is the primary purpose of a “data lake architecture” in data engineering?
    a) To centralize data storage
    b) To organize data into tables
    c) To store data in its raw, unprocessed form
    d) To encrypt data at rest
48. In data engineering, what is “data deduplication”?
    a) A technique to reduce data complexity
    b) A process to remove duplicate data records
    c) The conversion of structured data into unstructured data
    d) A data visualization approach
49. What does “ELK” stand for in the context of log analysis and data engineering?
    a) Extensible Log Kit
    b) Elastic Log Kinesis
    c) Elasticsearch, Logstash, Kibana
    d) Event Log Keeper
50. In data engineering, what does “CDC” stand for in the context of databases?
    a) Centralized Data Control
    b) Continuous Data Capture
    c) Complex Data Cleansing
    d) Cached Data Conversion

Answer Key:

1. c) Data storage and processing
2. b) Extract, Transform, Load
3. c) Structuring data to reduce redundancy
4. b) Long-term data storage and analysis
5. b) Data lakes store data in its raw form, while data warehouses store processed data.
6. b) Star schema
7. b) To organize data into subsets for efficient querying
8. b) Data transformation and processing
9. b) Online Analytical Processing
10. b) To transform data into a binary format for storage or transmission
11. b) Horizontal scalability
12. c) Apache Kafka
13. a) To ensure data is free from any errors or inconsistencies
14. b) Change Data Capture
15. d) To improve query performance
16. c) Data extraction, transformation, and loading
17. b) Columnar database
18. c) To optimize data retrieval speed
19. b) A distributed data warehousing system built on Hadoop
20. c) Avro
21. b) To track the origins and transformations of data
22. b) To manage metadata and data discovery
23. b) To ensure data quality and compliance with regulations
24. c) HQL (Hive Query Language)
25. b) A real-time data streaming platform
26. b) Ensuring compliance with data regulations and standards
27. c) To secure data and protect it from unauthorized access
28. c) Extract, Load, Transform
29. c) In-memory database
30. c) Moving and processing data from source to destination
31. c) To split data into smaller, manageable partitions
32. b) To track the transformations applied to data
33. a) Ensuring compliance with data regulations
34. b) Directed Acyclic Graph
35. b) Designing and optimizing data storage and retrieval systems
36. d) Hadoop MapReduce
37. b) Biased data distribution in a dataset
38. b) To delay schema creation until data is read
39. b) Extract, Transform, Transfer
40. b) Coordinating data workflows and dependencies
41. b) Atomic, Consistent, Isolated, Durable
42. c) To improve data storage efficiency
43. c) To assess the quality and structure of data
44. a) To store data in a tabular format
45. c) To store metadata and data definitions
46. c) Apache Storm
47. c) To store data in its raw, unprocessed form
48. b) A process to remove duplicate data records
49. c) Elasticsearch, Logstash, Kibana
50. b) Continuous Data Capture