AWS Certified Associate Data-Engineer-Associate Reddit Questions

Option B best meets a highly resilient DR requirement with an RTO under 1 hour because a Multi-AZ deployment is designed to provide rapid recovery through redundancy across Availability Zones, rather than requiring a restore-and-rebuild process after a failure. A snapshot-based strategy (Option C) can be valuable for backup and regional recovery, but restoring a new cluster from snapshots is a heavier operation and is not the most reliable path to consistently achieving sub-hour RTO during real incidents. The study material highlights snapshot/restore as an operational mechanism for creating a new environment from a Redshift cluster’s saved state, which inherently implies a restore process and associated time to bring resources online.

Option D (cluster relocation) is primarily an operational move mechanism and is not the same as maintaining continuously resilient capacity for unplanned AZ-impacting events. Option A is not the best choice because Multi-AZ resiliency is aligned with modern Redshift architectures, and RA3 is the recommended node family for contemporary Redshift deployments where performance and managed storage characteristics are key for enterprise analytics. The study material reinforces Amazon Redshift as the platform for high-performance enterprise analytics, so a resilience-first configuration is appropriate.

Option A is correct because AWS Glue crawlers are the AWS-native service for discovering metadata and updating the AWS Glue Data Catalog. AWS documentation says that crawlers can crawl data stores, infer schema, and upon completion create or update tables in the Data Catalog. For schema-change tracking on an Amazon Redshift table, the crawler can connect through JDBC, inspect the table metadata, and update the Glue Data Catalog on a schedule. This directly satisfies the requirement to implement a cataloging solution that tracks schema changes over time.

Option B is incorrect because AWS DataSync is for data transfer, not metadata cataloging. Option C is also incorrect because AWS SCT is intended for schema conversion and migration assessments, not ongoing catalog synchronization into a Hive metastore for this use case. Option D is not the best answer because the requirement is for an AWS cataloging solution, and the standard managed catalog service here is the AWS Glue Data Catalog, not an external Apache Hive metastore. The daily scheduled crawler against Redshift with Glue Data Catalog updates is the most direct and exam-aligned solution for tracking schema changes.

This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.

The other options are not optimal for the following reasons:

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard. Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.

B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .

D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time. Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .

1: Amazon Managed Streaming for Apache Flink

2: Amazon Timestream

3: Amazon QuickSight

Analyze data in Amazon Timestream using Grafana

Amazon Kinesis Data Firehose

Amazon Aurora

AWS Glue Bookmarks

AWS Glue Job and Crawler Scheduling

 AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. References:

AWS Glue

AWS Glue Data Catalog

AWS Glue Workflows

[AWS Glue Job Bookmarking]

[AWS Glue Crawlers]

[AWS Glue Schema Registry]

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]