AWS Data Engineer Data Ingestion and Transformation Quiz

1. What does the Glue Data Catalog enable you to do?

Write and execute Spark SQL directly

Create a unified metadata repository across AWS services

Monitor streaming ingestion rates in real-time

Encrypt data before writing to S3

2. In data transformation, what does normalization primarily accomplish?

Removes duplicate records from datasets

Organizes data into consistent, related tables to minimize redundancy

Encrypts sensitive information

Compresses file sizes

3. Which AWS Glue feature allows you to handle schema evolution automatically?

Schema Registry

Dynamic Frames

Job Bookmarks

Partition Projection

4. What is data lineage in the context of ETL pipelines?

The ability to track data origin, transformations, and destinations

The speed of data processing

The compression ratio of stored files

The encryption method used

5. Which approach ensures idempotency in data ingestion pipelines?

Using timestamps to track processed records

Running jobs without error handling

Storing raw data without transformation

Using random file names

6. In AWS Glue, what does a trigger do?

Automatically initiates Glue jobs based on events or schedules

Encrypts data in transit

Monitors S3 bucket access logs

Compresses Parquet files

7. What is the primary benefit of using Parquet format for data lake storage?

It is human-readable in text editors

It provides columnar compression and efficient query performance

It requires no schema definition

It supports unlimited row sizes

8. Which transformation technique removes duplicate records from a dataset?

Filtering

Deduplication

Aggregation

Normalization

9. In data transformation, what is the purpose of schema validation?

To ensure data conforms to expected structure and types

To compress files before storage

To encrypt sensitive columns

To create redundant copies of data

10. Which AWS service is best for continuous data replication from on-premises databases to S3?

AWS Glue Streaming ETL

AWS DMS with continuous replication

Amazon Kinesis Firehose

AWS Lambda scheduled functions

11. Which AWS service is best suited for serverless ETL jobs that extract, transform, and load data from multiple sources?

AWS Glue

Amazon EMR

AWS Lambda

Amazon Kinesis

12. Which partitioning strategy is most efficient for large S3 datasets queried by date range?

Random partitioning

Year/Month/Day directory structure

Single flat directory

UUID-based partitioning

13. In AWS Glue, which component stores metadata about your data sources and schemas?

Glue Jobs

Glue Triggers

Glue Data Catalog

Glue Connections

14. What is the primary purpose of AWS Database Migration Service (DMS)?

To replicate and migrate databases to AWS with minimal downtime

To analyze query performance in production

To compress large datasets

To create backups of relational databases

15. Which AWS service allows you to transform data in transit using SQL queries without provisioning servers?

Amazon Kinesis Data Analytics

AWS DMS

Amazon Redshift

AWS Data Exchange

16. In Kinesis Data Streams, what is a shard?

A partition of the stream with a guaranteed throughput

A copy of your data stored in S3

A transformation rule applied to records

A security group for stream access

17. Amazon Kinesis Data Streams is optimized for which type of data ingestion pattern?

Batch processing of large files

Real-time streaming data collection

Scheduled nightly imports

One-time bulk uploads

18. What is the primary data format supported by AWS Glue for schema inference?

JSON and Parquet

CSV, JSON, Parquet, and ORC

XML only

Binary files exclusively

19. Which of the following best describes Apache Spark's role in AWS Glue jobs?

It provides the distributed processing engine for transformations

It manages S3 bucket permissions

It stores metadata in the Glue Data Catalog

It handles real-time streaming ingestion

20. In AWS Glue, what is the primary purpose of a crawler?

To execute Spark jobs in parallel

To automatically discover and catalog data in your data lake

To encrypt data at rest

To monitor job performance metrics