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Questions
30
Answers
1
Which is a data source?
Data Streams
Files
All of the above
Databases
2
Characteristic of a data lake?
Holds raw, unprocessed data
Deletes data automatically
Stores only structured data
Only stores small datasets
3
What is the first stage of data lifecycle?
Storage
Generation
Analysis
Collection
4
Example of structured data?
Social media posts
JSON files
PDF documents
Relational databases
5
Which is NOT a type of data serving?
Data Hiding
Analytics
Business Intelligence
Machine Learning
6
What is data?
Only numerical values
Only text and images
Raw facts and observations
None of the above
7
Why is data engineering essential in AI/ML?
Builds infrastructure for decisions
Deletes training data
Ignores data pipelines
Provides dirty data
8
Key aspect of security in data engineering?
Unlimited access
Ignoring encryption
Sharing passwords openly
Principle of Least Privilege
9
Real-time stream processing tool?
None of the above
Apache Kafka
Microsoft Excel
MySQL
10
What is data consolidation?
Ignoring data formats
Making data inaccessible
Combining data into structure
Deleting all data
11
Focus of data architecture?
Designing systems for data needs
Deleting all data
Making data inaccessible
Ignoring business requirements
12
Which is NOT a method of data collection?
Web APIs
Observation
Data Destruction
Surveys
13
Goal of data transformation?
To slow down processing
To delete all data
To make data inaccessible
To convert and cleanse data
14
What is the ELT pattern?
Extract, Load, Transform
None of the above
Eliminate, Lose, Terminate
Extract, Lose, Transform
15
Role of data engineers?
Deleting data pipelines
Building data infrastructure
Only analyzing data
Ignoring data quality
16
What is a data pipeline?
Ignoring data sources
Processes that move data
Slow down data processing
A tool to delete data
17
What is DataOps?
Enhance data product development
A way to delete data
Slow down data processes
Ignoring data quality
18
What is data quality?
Ignoring data errors
Condition based on accuracy
None of the above
Deleting data randomly
19
Role of data scientist vs data engineer?
Data engineers build infrastructure
Both roles are identical
Data scientists build pipelines
None of the above
20
What is a destructive transformation?
Combining columns
Adding new fields
Standardizing date formats
Deleting outdated records
21
Key development in data engineering in 2000s?
Rise of big data tools
Elimination of all databases
End of data processing
Ignoring cloud technologies
22
Difference between operational and analytical data?
Both are the same
Operational data for business
Analytical data for transactions
Operational data is never stored
23
Which is a pillar of DataOps?
No monitoring
Manual processes
Automation
Ignoring errors
24
Purpose of a data warehouse?
Ignore historical data
Centralize integrated data
Store unstructured data only
Slow down queries
25
What is the 3Vs framework?
Virtual, Vague, Visible
Volume, Velocity, Variety
None of the above
Value, Vision, Victory
26
Common challenge in data engineering?
Data accuracy and quality
Unlimited computational resources
No need for optimization
Ignoring data governance
27
Key concept in orchestration?
Ignoring dependencies
Random task execution
No error handling
Time-based scheduling
28
Responsibility of a data engineer?
Deleting all data
Slowing down pipelines
Combining disparate data
Ignoring data sources
29
Which is NOT a type of data?
Hypothetical Data
Text Data
Numerical Data
Audio Data
30
Purpose of data serving?
To make data accessible
To slow down access
To hide data
To delete data permanently
