Data Engineering Program Final Exam

Data Engineering Final Exam

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1. What is the primary responsibility of a data engineer in the data ecosystem?

Creating predictive machine learning models

Conducting statistical analysis on datasets

Building and maintaining data pipelines

Designing front-end dashboards for data visualization

2. Which of the following best describes the difference between data engineering and data science?

Data scientists work with relational databases, while data engineers use NoSQL databases

Data engineers write machine learning models, while data scientists build ETL pipelines

Data engineers focus on infrastructure, while data scientists focus on analysis

Data engineering is a subset of data science

3. What is a common tool used for workflow orchestration in data engineering?

Pandas

Excel

Jupyter Notebook

Apache Airflow

4. Which of the following is NOT a primary function of an ETL pipeline?

Extracting data from various sources

Running machine learning algorithms

Loading data into a destination system

Transforming data into a usable format

5. What is a key advantage of using a NoSQL database over a relational database?

Automatic data normalization

Improved query performance for analytical workloads

Strict schema enforcement

Better handling of unstructured data

6. When optimizing SQL queries for performance, what is one common technique used?

Increasing the size of the database

Storing data as JSON blobs

Indexing

Using only SELECT * queries

7. Which of the following is an advantage of database sharding?

Simplified database schema

Automatic schema normalization

Elimination of data redundancy

Improved scalability and parallel query execution

8. What is the primary benefit of using Apache Spark for big data processing?

Built-in visualization tools

Better support for transactional workloads

Distributed in-memory computing for faster processing

Uses only batch processing

9. What is a major difference between Hadoop MapReduce and Spark?

MapReduce is faster for real-time data processing

Spark does not support distributed computing

Spark processes data in-memory, while MapReduce writes intermediate data to disk

MapReduce is only used for structured data

10. Which of the following is a common technique for optimizing big data processing performance?

Running queries without indexing

Using a single node for distributed processing

Using columnar storage formats like Parquet

Storing all data as CSV files

11. What is the purpose of deploying data pipelines with CI/CD?

Eliminating the need for data transformation

Ensuring only batch processing pipelines are used

Automating data pipeline testing and deployment

Replacing manual database queries

12. What is a major benefit of using Apache Kafka in a real-time data pipeline?

Eliminates the need for ETL processing

Converts unstructured data into structured data

Automatic data visualization

High throughput message streaming

13. Which of the following is a key characteristic of cloud-based data solutions?

No support for distributed computing

Strict on-premises data storage requirements

Manual infrastructure setup required

On-demand scalability

14. What is a primary function of Apache Airflow in a data pipeline?

Replacing traditional ETL tools

Running machine learning models

Automating workflow orchestration

Storing large-scale datasets

15. What is an essential aspect of securing data at rest?

Disabling authentication for internal users

Encrypting stored data using AES-256

Storing data in plaintext

Using a single access control method

16. What is a primary challenge in ensuring data governance compliance?

Storing all data in relational databases

Eliminating data encryption

Managing access control across multiple systems

Avoiding real-time data processing

17. What is the role of logging in monitoring data systems?

Preventing data replication

Reducing storage costs by deleting data

Tracking system activity and troubleshooting errors

Improving query performance

18. What is the main goal of GDPR compliance in data engineering?

Restricting data usage to government agencies

Requiring all data to be stored in Europe

Protecting personal data privacy and access rights

Allowing companies to store user data indefinitely

19. What is a key advantage of Change Data Capture (CDC)?

Propagates only changes instead of full data reloads

Reduces data accuracy by summarizing information

Eliminates the need for schema updates

Prevents data from being updated in real-time

20. Why is columnar storage like Parquet preferred in big data processing?

Improves query performance by storing data efficiently

Eliminates the need for indexing

Reduces file readability

Uses more storage space than row-based formats

21. What is a major challenge in scaling a data architecture?

Ensuring high availability and consistency

Avoiding schema updates

Storing only structured data

Reducing data access

22. How does a data lake differ from a data warehouse?

A data warehouse is optimized for real-time streaming

A data lake supports only transactional workloads

A data warehouse does not enforce schema

A data lake stores raw, unstructured data

23. What is a key challenge of implementing database sharding?

Eliminating replication

Reducing storage space

Managing distributed queries efficiently

Avoiding schema design changes

24. What is the benefit of using a distributed message broker like Kafka?

Stores only structured data

Enables real-time data streaming

Eliminates the need for ETL pipelines

Reduces query performance

25. Which BI tool supports real-time data visualization?

Power BI

Tableau

Google Looker

Apache Superset

26. What is the purpose of data partitioning in scalable architectures?

Slows down query execution

Prevents schema evolution

Stores only relational data

Distributes data across multiple nodes for parallel processing

27. What is a best practice for securing cloud-based data pipelines?

Allowing unrestricted API access

Storing encryption keys alongside sensitive data

Enabling multi-factor authentication

Disabling monitoring for cost savings

28. Why is monitoring important in data security?

Detects potential security breaches and anomalies

Eliminates the need for compliance audits

Reduces the frequency of database backups

Prevents encryption from being used

29. What is a benefit of using Apache Flink for real-time analytics?

Data storage optimization

Schema enforcement

High-latency batch processing

Low-latency stream processing

30. What is a common challenge when integrating data with BI tools?

Storing data in NoSQL formats only

Optimizing queries for fast dashboard loading

Reducing data availability

Preventing schema updates

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