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Question 1. Which type of data source is characterized by continuously generated data with high velocity? A) Batch data source B) Streaming data source C) Structured data source D) Unstructured data source Answer: B Explanation: Streaming data sources produce data continuously and require real-time or near-real-time processing, unlike batch sources. Question 2. What is the primary advantage of using a message queue for data ingestion? A) Reduces storage costs
B) Decouples producers and consumers C) Increases data redundancy D) Simplifies data modeling Answer: B Explanation: Message queues buffer data and decouple data producers from consumers, increasing scalability and reliability. Question 3. Which ingestion method is most suitable for processing large, historical datasets? A) Real-time streaming B) Micro-batching C) Batch processing D) Change data capture
Question 5. What is the main purpose of a data lake in a data pipeline? A) Store only structured data B) Store raw, unprocessed data C) Perform real-time analytics D) Enforce data quality Answer: B Explanation: A data lake is designed for storing raw, unprocessed data in various formats for later processing. Question 6. Which of the following is NOT a common data ingestion challenge? A) Handling schema evolution B) High data velocity
C) Data deduplication D) Data visualization Answer: D Explanation: Data visualization is part of analytics, not ingestion. Ingestion challenges relate to schema, velocity, and duplication. Question 7. Which tool is best suited for ingesting streaming data into a cloud data platform? A) Apache Beam B) Apache Kafka C) Apache Airflow D) dbt Answer: B
A) To enforce strict data validation B) To maximize storage efficiency C) To support diverse and evolving data formats D) To improve query speed Answer: C Explanation: Schema-on-read allows for flexible data ingestion, supporting multiple formats and evolving schemas. Question 10. Which cloud service is specifically designed for batch and stream data ingestion in Google Cloud? A) Cloud Pub/Sub B) Cloud Spanner C) BigQuery ML
D) Cloud Vision API Answer: A Explanation: Cloud Pub/Sub enables ingestion of both batch and streaming data in Google Cloud. Question 11. Which of the following best describes an ETL pipeline? A) Extract, Transform, Load B) Evaluate, Transfer, Learn C) Extract, Transfer, Load D) Extract, Transform, Listen Answer: A Explanation: ETL stands for Extract, Transform, Load—key steps in preparing data for analysis.
B) Apache Hive C) Apache Storm D) Apache Sqoop Answer: A Explanation: Apache Beam provides a unified model for both batch and stream data processing. Question 14. For real-time analytics with low latency requirements, which engine is preferred? A) Apache Spark Streaming B) Hadoop MapReduce C) Apache Pig D) Google Dataflow
Answer: A Explanation: Apache Spark Streaming supports real-time analytics with low latency. Question 15. What is the main benefit of using windowing in streaming data processing? A) Reduces storage costs B) Enables aggregation over time intervals C) Increases pipeline complexity D) Simplifies data ingestion Answer: B Explanation: Windowing allows for aggregation and analysis over specific time intervals in streaming data.
D) To optimize query speed Answer: B Explanation: Data lineage tools help track how and where data moves and is transformed in a pipeline. Question 18. In a distributed data processing system, what is a common challenge to ensure data consistency? A) Data deduplication B) Eventual consistency C) Data compression D) Data sharding Answer: B Explanation: Distributed systems often provide eventual consistency, requiring additional mechanisms to ensure data consistency.
Question 19. Which operation is NOT part of typical data transformation? A) Aggregation B) Filtering C) Tokenization D) Visualization Answer: D Explanation: Data transformation includes aggregation, filtering, and tokenization, but not visualization. Question 20. What is a primary use case for data enrichment during transformation? A) Enhance data with additional context
Answer: B Explanation: Data lakes are designed to store large volumes of unstructured and semi-structured data. Question 22. What makes columnar storage formats like Parquet efficient for analytics workloads? A) Row-based access B) Minimized disk usage for single columns C) High write throughput D) Uncompressed storage Answer: B Explanation: Columnar formats enable efficient storage and access for analytical queries that focus on specific columns.
Question 23. In which scenario should you use a NoSQL database? A) Strict schema requirements B) High transaction consistency C) Flexible and evolving data models D) Only structured data Answer: C Explanation: NoSQL databases are ideal for use cases with flexible, schema-less, or evolving data models. Question 24. What is the primary benefit of data partitioning in storage? A) Reduces data redundancy B) Improves query performance by limiting data scanned
Explanation: Clustering organizes data within partitions to speed up filtering and improve query performance. Question 26. What is the main goal of database indexing? A) Reduce storage costs B) Improve data compression C) Enable faster data retrieval D) Simplify backup strategies Answer: C Explanation: Indexes improve the speed and efficiency of data retrieval operations. Question 27. Which of the following is a data warehouse characteristic?
A) Optimized for transactional processing B) Designed for analytical queries and reporting C) Stores only unstructured data D) Schema-less design Answer: B Explanation: Data warehouses are designed for analytical workloads and reporting, not transactions. Question 28. What is a star schema in data modeling? A) Hierarchical structure for storing logs B) Fact table connected to multiple dimension tables C) Denormalized structure with one table D) Table with only primary keys
