AWS Certified Associate Data-Engineer-Associate Book

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

• Using the Amazon Redshift Data API
• Using Amazon EventBridge with Amazon Redshift
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

The STL ALERT EVENT LOG table view records anomalies when the query optimizer identifies conditions that might indicate performance issues. These conditions include skewed data distribution, missing statistics, nested loop joins, and broadcasted data. The STL ALERT EVENT LOG table view can help the data engineer to identify and troubleshoot the root causes of performance issues and optimize the query execution plan. The other table views are not relevant for this requirement. STL USAGE CONTROL records the usage limits and quotas for Amazon Redshift resources. STL QUERY METRICS records the execution time and resource consumption of queries. STL PLAN INFO records the query execution plan and the steps involved in each query. References:

• STL ALERT EVENT LOG
• System Tables and Views
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon Athena Federated Query is a feature that allows you to query data from multiple sources using standard SQL. You can use Athena Federated Query to join data from Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3, as well as other data sources such as MongoDB, Apache HBase, and Apache Kafka1. Athena Federated Query is a serverless and interactive service, meaning you do not need to provision or manage any infrastructure, and you only pay for the amount of data scanned by your queries. Athena Federated Query is the most cost-effective solution for performing a one-time analysis job on data from multiple sources, as it eliminates the need to copy or move data, and allows you to query data directly from the source.

The other options are not as cost-effective as Athena Federated Query, as they involve additional steps or costs. Option A requires you to provision and pay for an Amazon EMR cluster, which can be expensive and time-consuming for a one-time job. Option B requires you to copy or move data from DynamoDB, RDS, and Redshift to S3, which can incur additional costs for data transfer and storage, and also introduce latency and complexity. Option D requires you to have an existing Redshift cluster, which can be costly and may not be necessary for a one-time job. Option D also does not supportquerying data from RDS directly, so you would need to use Redshift Federated Query to access RDS data, which adds another layer of complexity2. References:

• Amazon Athena Federated Query
• Redshift Spectrum vs Federated Query

 AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. References:

• AWS Glue
• FindMatches ML Transform
• AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide