Pass Your Amazon AWS Certified Solutions Architect - Associate SAA-C03 Exam Easy!

Preallocating speculative capacity increases current storage charges; eligible RDS storage autoscaling can grow up to a defined maximum.

Backup retention governs recovery-history storage, not allocated database volume size. A large live RDS allocation remains billable until an actual supported migration/resizing action changes it; autoscaling avoids future over-allocation at provisioning time.

Changing EBS-backed storage type can affect IOPS and throughput economics, but it does not reclaim previously allocated GiB. RDS storage autoscaling grows when needed and does not automatically shrink an oversized existing volume.

gp3 may lower cost for suitable I/O, but extreme performance or latency needs require an explicit supported-capability comparison.

gp2 performance is more coupled to allocated capacity and burst behavior than gp3; blindly buying additional GiB is not a sound price/performance comparison for a modest, predictable I/O workload.

Provisioning more io2 IOPS generally increases storage performance charges and does not by itself reduce the cost of data kept on the volume. Compare supported gp3 performance, price, and latency requirements.

A low-duty-cycle query workload may benefit from Redshift Serverless pricing rather than dedicated always-running nodes.

Concurrency scaling addresses additional query load, not the underlying continuous provisioned cluster charge. Redshift Serverless meters workgroup capacity differently and warrants evaluation for intermittent workloads.

More nodes may reduce execution time but raise hourly provisioned compute cost until the cluster is paused or resized. It does not provide serverless demand-based RPU metering for mostly idle months.

RA3 managed storage can grow separately from node compute; unnecessary nodes add cost without proportional query benefit.

Dense-storage node types couple the local disk footprint to the cluster node fleet. RA3 managed storage is the option that decouples stored historical data from compute node sizing.

Concurrency scaling adds temporary query compute capacity to absorb eligible peak read demand; it is not how persistent managed storage is allocated. Redshift RA3 already decouples storage growth from provisioned nodes.

Athena costs depend on bytes scanned; columnar Parquet and useful partitions can prune input files and read only needed columns.

Compression can reduce bytes read, but row-oriented CSV lacks Parquet column skipping and unpartitioned data still requires scanning irrelevant date ranges. A layout with useful partitions and columnar files is better for selective repeated queries.

Athena partition pruning depends on predicates compatible with actual partition keys. An unrelated identifier will not restrict date ranges scanned; choose partitions aligned with queried filters.

Broad projections increase physical index storage and write charges; design should match the actual query fields and access patterns.

Index sparsity and changes to indexed fields can materially affect both charges and workload behavior.

GSI keys and projections occupy physical storage and have separate capacity usage.

Snapshot cleanup must respect regulatory recovery requirements and incremental block references before any deletion.

Manual DB snapshots persist until explicitly deleted, independently of the automated backup-retention window. Changing that setting alone does not stop billing for manually retained copies.

Manual snapshots survive deletion of their source DB instance unless explicitly removed. Inventory snapshot dependencies and retention policy before selective deletion.

Discrete daily backups do not provide the requested continuous seconds-level point-in-time recovery history.

On-demand backups represent discrete restore points at their capture times. Even hourly snapshots cannot restore every intermediate second; point-in-time recovery provides continuous recovery points within the enabled window.

Streams offer a change data capture feed, not a managed full-table point-in-time restore. Recovery requires a backup/PITR feature, and stream retention alone cannot reconstruct an arbitrary historical table state.

RDS license modes depend on supported engine, edition and licensing terms; not all BYOL assumptions are valid.

RDS for SQL Server BYOM requires qualifying License Mobility rights and active Software Assurance, with supported editions, versions, and Regions. Possessing a license alone does not establish eligibility.

For RDS for SQL Server, eligible Database Savings Plan discounts apply to instance charges, not the separately billed Windows and SQL Server license fees. Software license eligibility must be evaluated independently.

Provisioned mode reserves throughput and can incur charges for unused provisioned capacity, unlike request-based on-demand mode.

Auto scaling cannot scale below the configured minimum read/write capacity, so setting its floor to a peak value leaves the idle-cost problem in place. Compare a realistic baseline and demand curve.

A provisioned table retains its configured read/write capacity until changed; any auto scaling policy also has configured minimum and response behavior.

On-demand bills per request, and stable high throughput can justify efficiently utilized provisioned capacity.

On-demand request pricing and provisioned RCU/WCU pricing are different models. High, predictable demand can favor effectively utilized provisioned capacity.

Provisioned plus auto scaling can remain economical for stable demand if the chosen baseline and peak utilization justify it. Compare real workload rates.