AWS Certified Data Analytics Specialty 2021 - Hands-On! - Lambda Integration - Part 2

The video tutorial explains how AWS Lambda can be integrated with various AWS services like Elasticsearch, Data Pipeline, Redshift, and Kinesis. It covers the use of Lambda for real-time data processing, automating data pipelines, and handling streaming data. The tutorial also highlights potential challenges and best practices for using Lambda, such as managing state with DynamoDB and handling errors in streaming data processing.

What is a common use case for integrating AWS Lambda with Elasticsearch?

• Real-time video processing

• Log data processing

• Image recognition

• Machine learning model training

How does AWS Lambda enhance the AWS Data Pipeline?

• By providing a user interface for data visualization

• By automatically scaling resources

• By enabling on-demand data processing

• By reducing data storage costs

What is a challenge when using AWS Lambda with Redshift?

• Redshift's high cost of operation

• Redshift's lack of support for real-time data

• Lambda's stateless nature

• Lambda's inability to handle large data volumes

Which AWS service can be used to maintain state when using Lambda with Redshift?

• Amazon EC2

• Amazon S3

• Amazon DynamoDB

• Amazon RDS

What is a potential issue when processing large batches of streaming data with AWS Lambda?

• Data loss during processing

• Lambda function timeout

• Increased latency in data retrieval

• Increased data storage costs

What is the maximum payload size for a batch of streaming data in AWS Lambda?

• 20 MB

• 10 MB

• 6 MB

• 15 MB

How can you prevent a stalled pipeline when using AWS Lambda with Kinesis?

• By increasing the number of shards

• By increasing the Lambda timeout limit

• By using a different AWS region

• By reducing the batch size