AWS Data Engineer Interview Prep: 500+ Most asked Questions

About AWS Data Engineer Interview Prep: 500+ Most asked Questions

Prepare for your AWS Data Engineer interview with this comprehensive course, covering 500+ most asked interview questions and answers. This course is designed for candidates who want to strengthen their skills in AWS core services, data ingestion, processing, storage, analytics, security, and best practices. Each topic is carefully curated to help you master AWS services and understand their real-world applications. The course is structured in a way that covers all critical areas, from fundamental concepts to advanced implementations.

Course Topics Covered:

1. AWS Core Services for Data Engineering

Amazon S3 (Simple Storage Service)

Object storage fundamentals and versioning

Data encryption, IAM roles, and bucket policies

S3 Event Notifications and performance optimization

Amazon EC2 (Elastic Compute Cloud)

EC2 instance types, pricing models, and autoscaling

Load balancing, network configurations, and security groups

AWS IAM (Identity and Access Management)

Roles, policies, federated access, and MFA

Fine-grained data access control

Amazon VPC (Virtual Private Cloud)

Subnets, route tables, NACLs, and security groups

VPN, Direct Connect, and VPC Peering

2. Data Ingestion and Streaming

AWS Glue

Data Cataloging, Crawler configuration, and ETL Jobs

Integration with S3, RDS, and Redshift

Amazon Kinesis

Kinesis Streams vs. Kinesis Firehose

Real-time processing with Kinesis Data Analytics

Integrations with AWS Lambda and S3

Amazon MSK (Managed Streaming for Apache Kafka)

Kafka vs Kinesis: Understanding use cases

Kafka partitioning, replication, and MSK scaling

3. Data Processing

AWS Lambda

Event-driven serverless execution and integrations with AWS services

Monitoring and scaling Lambda functions

Amazon EMR (Elastic MapReduce)

Apache Hadoop, Spark, HBase, and Presto on EMR

Cluster setup, auto-scaling, and Spot Instances

AWS Glue

Data transformations, Glue Data Catalog, and querying with Athena

Amazon Athena

Serverless SQL queries on S3 data

Schema on read and partitioning techniques for optimization

4. Data Storage

Amazon Redshift

Redshift architecture, columnar storage, and compression

Performance tuning and querying data with Redshift Spectrum

Amazon RDS (Relational Database Service)

Backup, scaling, read replicas, and IAM authentication

Supported engines: MySQL, PostgreSQL, Oracle, SQL Server

Amazon DynamoDB

NoSQL concepts, indexing, and auto-scaling

5. Data Analytics and Visualization

Amazon Redshift

Data warehousing, performance optimization, and Spectrum for querying S3

Amazon QuickSight

BI tool for data visualization, dashboard creation, and ML insights

Amazon Elasticsearch Service

Full-text search and integration with Logstash and Kibana

6. Data Security and Compliance

AWS KMS (Key Management Service)

Data encryption, key rotation, and policies

AWS CloudTrail

Logging, auditing, and integrating with S3 and CloudWatch

AWS Secrets Manager

Secure storage and rotation of credentials and API keys

Amazon Macie

Data security and privacy in S3, identifying Personally Identifiable Information (PII)

7. Monitoring and Optimization

Amazon CloudWatch

Monitoring AWS resources, custom metrics, alarms, and logs

AWS Cost Explorer

Cost optimization for services like S3, Redshift, Glue, and EMR

AWS Trusted Advisor

Recommendations for performance, cost optimization, and security

8. Machine Learning & Data Pipelines

Amazon SageMaker

Building and deploying ML models, integration with S3 and Redshift

Amazon Glue for ML

Applying ML transformations and anomaly detection in Glue jobs

Kinesis Data Analytics for Machine Learning

Real-time data analytics and inference

9. ETL (Extract, Transform, Load)

AWS Data Pipeline

Data workflow orchestration and monitoring

AWS Step Functions

Serverless orchestration with Lambda, Glue, and Batch

AWS Batch

Running batch jobs, job queues, and dependencies

10. Architecting and Best Practices

Data Lake Architecture on AWS

Best practices for creating data lakes with S3, Glue, and Athena

Event-Driven Architecture

Real-time event processing with Lambda, S3, and Kinesis

AWS Well-Architected Framework

Principles for cost optimization, performance, security, and reliability

Serverless vs Server-based Data Pipelines

Comparing Lambda, Glue, Batch vs EMR, EC2 for data pipelines

11. Big Data Tools and Integrations

AWS Glue with Apache Spark

Writing and optimizing Spark jobs in Glue

Amazon Redshift with Apache Hudi, Delta Lake

Efficient updates to Redshift tables using Hudi and Delta Lake

AWS Glue and Kafka/MSK Integration

Building near real-time data pipelines with Kafka/MSK

This course is ideal for professionals seeking to master AWS Data Engineering services and confidently prepare for interviews. With over 500 practice questions, you’ll cover each key service in-depth and gain a solid understanding of how to integrate them for building scalable, efficient data pipelines and architecture