Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

 AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:

AWS Glue

[AWS Glue Data Catalog]

[AWS Glue Developer Guide]

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

In AWS Glue, table partitions are managed as metadata objects within the AWS Glue Data Catalog. To add a new partition to an existing table, the correct and supported approach is to use the AWS Glue Data Catalog API, such as the CreatePartition operation, or equivalent console or SDK actions.

Updating the table schema alone does not create partitions or inform Glue about new partition values. Editing metadata files directly in Amazon S3 is unsupported and can corrupt the Data Catalog. Manually modifying the S3 bucket structure without registering partitions in Glue will result in Athena and other query engines being unable to recognize the partitions.

By adding partitions through the Glue Data Catalog API, query engines such as Amazon Athena and Amazon Redshift Spectrum can perform partition pruning, which significantly improves query performance by scanning only relevant data.

This method aligns with AWS best practices, ensures metadata consistency, and avoids unnecessary operational risk. Therefore, Option C is the correct solution.

Option A is the most direct and operationally efficient way to trigger the existing Lambda-based load into Amazon Redshift whenever a new CSV file is uploaded to Amazon S3. The key requirement is event-driven automation on “new object created” events, and the study material explicitly describes using Amazon EventBridge to react to S3 uploads by creating a rule on the default event bus and routing matching events to a target application.

Compared with queue-based designs (Options B and D), Option A reduces components: there is no need to manage an SQS queue, batching/visibility timeout behavior, or retry semantics at the consumer layer. This is especially appropriate because the files are small (≤ 5 MB) and the Lambda function is already implemented to perform the copy/load step. Using DMS (Option C) is not designed for “S3 object arrival → Lambda → Redshift” event triggering; it introduces unnecessary services and operational work for a simple file-ingestion trigger.

Therefore, integrating S3 event notifications with EventBridge and targeting the Lambda function best meets the requirement with the least added complexity.