AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A large enterprise uses AWS Organizations to manage dozens of member accounts. The finance team has reported a significant, unexpected increase in costs, but the high-level views in AWS Cost Explorer are insufficient for identifying the root cause. The company has configured AWS Cost and Usage Reports (CUR) to be delivered hourly in Apache Parquet format to an Amazon S3 bucket in the management account.
A solutions architect needs to implement a scalable and cost-effective solution to perform complex, ad-hoc SQL queries on this CUR data. The goal is to identify specific resources and API operations contributing to the cost increase across the entire organization.
Which approach will achieve this with the LEAST operational overhead?
Create an Amazon EMR cluster configured with Apache Spark. Develop Spark SQL jobs to load the Parquet files from Amazon S3 into data frames and run queries from a Zeppelin notebook attached to the cluster.
Use Amazon S3 Select to query individual CUR Parquet files directly in the S3 bucket. Develop a script that iterates through all CUR files for the desired time range, executes S3 Select queries on each, and aggregates the results in the client application.
Set up an AWS Glue crawler to run on the S3 bucket containing the CUR data. Configure the crawler to populate the AWS Glue Data Catalog. Use Amazon Athena to run standard SQL queries against the table created by the crawler.
Develop an AWS Lambda function triggered by Amazon S3 events when new CUR files are delivered. The function will parse the Parquet files and load the data into a provisioned Amazon RDS for PostgreSQL database for querying.
The correct answer is to use AWS Glue and Amazon Athena. This combination is the AWS-recommended, serverless, and most operationally efficient method for querying AWS Cost and Usage Reports (CUR). When CUR is configured for Athena integration, AWS delivers the data in the optimal Apache Parquet format and provides a CloudFormation template to automatically create the necessary AWS Glue crawler and Data Catalog table. The Glue crawler automatically discovers the schema and partitions, making the data available for querying via Athena with standard SQL. This approach requires minimal setup and no infrastructure to manage, directly addressing the requirement for the least operational overhead.
Using Amazon EMR is incorrect because it introduces significant operational overhead. While EMR is a powerful big data platform, it requires provisioning and managing a cluster of EC2 instances, which is more complex and costly than the serverless Athena model for this use case.
Using AWS Lambda to load data into an Amazon RDS database is incorrect because it involves substantial development and maintenance effort. The architect would need to write and maintain code for parsing, data transformation, and loading. Furthermore, RDS is a transactional database and less cost-effective for the large-scale analytical queries typically run against CUR data compared to Athena.
Using Amazon S3 Select is incorrect because it is designed to query data within a single S3 object. While useful for simple filtering, it is not suitable for running complex, ad-hoc analytical queries that span the thousands of files that make up a complete CUR dataset. This approach would require a complex client-side application to orchestrate queries and aggregate results, creating high operational overhead.
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