AWS Data Engineer Interview Prep: 500+ Most asked Questions

Put together in your AWS Information Engineer interview with this complete course, overlaying 500+ most requested interview questions and solutions. This course is designed for candidates who wish to strengthen their abilities in AWS core companies, knowledge ingestion, processing, storage, analytics, safety, and greatest practices. Every matter is rigorously curated that will help you grasp AWS companies and perceive their real-world purposes. The course is structured in a manner that covers all crucial areas, from elementary ideas to superior implementations.

Course Subjects Lined:

1. AWS Core Companies for Information Engineering

• Amazon S3 (Easy Storage Service)
  • Object storage fundamentals and versioning
  • Information encryption, IAM roles, and bucket insurance policies
  • S3 Occasion Notifications and efficiency optimization
• Amazon EC2 (Elastic Compute Cloud)
  • EC2 occasion sorts, pricing fashions, and autoscaling
  • Load balancing, community configurations, and safety teams
• AWS IAM (Id and Entry Administration)
  • Roles, insurance policies, federated entry, and MFA
  • Advantageous-grained knowledge entry management
• Amazon VPC (Digital Personal Cloud)
  • Subnets, route tables, NACLs, and safety teams
  • VPN, Direct Join, and VPC Peering

2. Information Ingestion and Streaming

• AWS Glue
  • Information Cataloging, Crawler configuration, and ETL Jobs
  • Integration with S3, RDS, and Redshift
• Amazon Kinesis
  • Kinesis Streams vs. Kinesis Firehose
  • Actual-time processing with Kinesis Information Analytics
  • Integrations with AWS Lambda and S3
• Amazon MSK (Managed Streaming for Apache Kafka)
  • Kafka vs Kinesis: Understanding use instances
  • Kafka partitioning, replication, and MSK scaling

3. Information Processing

• AWS Lambda
  • Occasion-driven serverless execution and integrations with AWS companies
  • Monitoring and scaling Lambda features
• Amazon EMR (Elastic MapReduce)
  • Apache Hadoop, Spark, HBase, and Presto on EMR
  • Cluster setup, auto-scaling, and Spot Situations
• AWS Glue
  • Information transformations, Glue Information Catalog, and querying with Athena
• Amazon Athena
  • Serverless SQL queries on S3 knowledge
  • Schema on learn and partitioning strategies for optimization

4. Information Storage

• Amazon Redshift
  • Redshift structure, columnar storage, and compression
  • Efficiency tuning and querying knowledge with Redshift Spectrum
• Amazon RDS (Relational Database Service)
  • Backup, scaling, learn replicas, and IAM authentication
  • Supported engines: MySQL, PostgreSQL, Oracle, SQL Server
• Amazon DynamoDB
  • NoSQL ideas, indexing, and auto-scaling

5. Information Analytics and Visualization

• Amazon Redshift
  • Information warehousing, efficiency optimization, and Spectrum for querying S3
• Amazon QuickSight
  • BI software for knowledge visualization, dashboard creation, and ML insights
• Amazon Elasticsearch Service
  • Full-text search and integration with Logstash and Kibana

6. Information Safety and Compliance

• AWS KMS (Key Administration Service)
  • Information encryption, key rotation, and insurance policies
• AWS CloudTrail
  • Logging, auditing, and integrating with S3 and CloudWatch
• AWS Secrets and techniques Supervisor
  • Safe storage and rotation of credentials and API keys
• Amazon Macie
  • Information safety and privateness in S3, figuring out Personally Identifiable Data (PII)

7. Monitoring and Optimization

• Amazon CloudWatch
  • Monitoring AWS assets, customized metrics, alarms, and logs
• AWS Value Explorer
  • Value optimization for companies like S3, Redshift, Glue, and EMR
• AWS Trusted Advisor
  • Suggestions for efficiency, price optimization, and safety

8. Machine Studying & Information Pipelines

• Amazon SageMaker
  • Constructing and deploying ML fashions, integration with S3 and Redshift
• Amazon Glue for ML
  • Making use of ML transformations and anomaly detection in Glue jobs
• Kinesis Information Analytics for Machine Studying
  • Actual-time knowledge analytics and inference

9. ETL (Extract, Rework, Load)

• AWS Information Pipeline
  • Information workflow orchestration and monitoring
• AWS Step Capabilities
  • Serverless orchestration with Lambda, Glue, and Batch
• AWS Batch
  • Operating batch jobs, job queues, and dependencies

10. Architecting and Greatest Practices

• Information Lake Structure on AWS
  • Greatest practices for creating knowledge lakes with S3, Glue, and Athena
• Occasion-Pushed Structure
  • Actual-time occasion processing with Lambda, S3, and Kinesis
• AWS Effectively-Architected Framework
  • Ideas for price optimization, efficiency, safety, and reliability
• Serverless vs Server-based Information Pipelines
  • Evaluating Lambda, Glue, Batch vs EMR, EC2 for knowledge pipelines

11. Massive Information Instruments and Integrations

• AWS Glue with Apache Spark
  • Writing and optimizing Spark jobs in Glue
• Amazon Redshift with Apache Hudi, Delta Lake
  • Environment friendly updates to Redshift tables utilizing Hudi and Delta Lake
• AWS Glue and Kafka/MSK Integration
  • Constructing close to real-time knowledge pipelines with Kafka/MSK

This course is good for professionals searching for to grasp AWS Information Engineering companies and confidently put together for interviews. With over 500 observe questions, you’ll cowl every key service in-depth and achieve a strong understanding of methods to combine them for constructing scalable, environment friendly knowledge pipelines and structure