Design Cost-Optimized Architectures: free practice questions

VPC gateway endpoints for S3 and DynamoDB are free and route traffic through the AWS private network, completely bypassing the NAT gateway. Since NAT gateway charges include both hourly rates and per-GB data processing fees, removing S3 and DynamoDB traffic from the NAT gateway path directly reduces costs. A NAT instance on a t3.small reduces hourly costs but still incurs data processing charges for all traffic including S3/DynamoDB flows, and introduces management overhead without fully solving the problem. VPC Flow Logs are a monitoring tool; they identify traffic patterns but do not reduce data processing charges. Moving instances to public subnets is a significant security regression and violates best practices for multi-tenant production environments, making it an unacceptable cost-optimization approach.

Correct – A: Athena charges $5 per TB of data scanned. Converting to Parquet (a columnar format) allows Athena to skip columns not needed by a query, dramatically reducing scanned bytes. Partitioning by transaction_date means queries filtering on that column only scan the relevant partitions rather than the entire table, further reducing scanned data. Merging small files also reduces per-request overhead. This typically reduces scan costs by 60–90%. Correct – B: Athena's query result reuse feature caches query results for a configurable period. When the same query with identical parameters runs again within that window, Athena returns the cached result instantly with no S3 scan and no charge. For recurring reports running dozens of times daily, this can eliminate a large portion of query charges. Wrong – C: While gzip compression reduces file size and thus scanned bytes, gzip is a splittable compression format only when using certain tools; more importantly, gzip-compressed CSV is still a row-based format — Athena still must read all columns for each row. It offers modest savings compared to columnar Parquet with partitioning. Wrong – D: Migrating to Redshift Spectrum still charges per TB scanned from S3 for external tables, and adds Redshift cluster costs. This does not inherently reduce query costs and adds significant complexity and expense. Wrong – E: Increasing scan limits is an administrative action that prevents queries from being blocked, but it does nothing to reduce the amount of data scanned or the resulting costs. Wrong – F: S3 Intelligent-Tiering affects storage costs only. Athena query charges are based on the amount of data scanned, not the storage class tier of the objects. Intelligent-Tiering has no effect on Athena billing.

Correct – C: EFS supports lifecycle management policies that automatically transition files unused for a configurable period (including 90 days) to the EFS Infrequent Access (IA) storage class. EFS IA costs approximately 85% less than EFS Standard. When a file is accessed, EFS transparently retrieves it back — no application changes are required, which meets the requirement. Wrong – A: This describes 'EFS Intelligent-Tiering,' which is indeed a valid EFS feature, but the description is slightly off — EFS Intelligent-Tiering transitions to IA after the configured inactivity period (not a fixed 30 days; the policy is configurable). More importantly, answer C is more precise and directly names the correct mechanism (lifecycle policy with 90-day threshold), matching the scenario's finding. Both A and C are close, but C is the more accurate and specific answer that aligns with configuring the threshold to match the 90-day access pattern identified. Wrong – B: Copying files to S3 and deleting from EFS would break the NFS-based mount that EC2 instances use. The application would lose access to files via the file system path, violating the 'no application changes' requirement. Wrong – D: Migrating to S3 and using the AWS SDK would require significant application changes to replace POSIX file system calls with S3 API calls, violating the 'no application changes' requirement.

CloudFront caches S3 content at edge locations worldwide, dramatically reducing data transfer OUT charges from S3 (CloudFront-to-origin requests are much cheaper than direct S3 egress) while improving global performance. S3 Transfer Acceleration speeds uploads to S3 but does not cache content or reduce outbound transfer costs — it is also more expensive per GB. Moving to EC2 in multiple regions would massively increase costs and operational complexity. S3 Intelligent-Tiering manages storage costs for objects with unpredictable access patterns but has no effect on data transfer OUT charges.

Athena charges $5 per TB of data scanned. Converting CSV files to Parquet reduces data scanned because Parquet is a columnar format — Athena only reads the columns referenced in the query and compression ratios are much higher than CSV, typically reducing scanned data by 60–90%. Partitioning the data by date and filtering on the partition key allows Athena to skip entire S3 prefixes that don't match the query's date range, dramatically reducing the amount of data scanned. Together, these two changes are the standard AWS-recommended approach to minimizing Athena costs. S3 Transfer Acceleration speeds up uploads to S3 from the internet; it has no effect on Athena query performance or cost. S3 Versioning maintains multiple versions of objects; it does not reduce query costs and would actually increase storage costs. Moving data to Glacier Instant Retrieval is incompatible with direct Athena queries — Athena requires data in S3 standard storage classes and cannot query Glacier-archived objects directly.