Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Use AWS DataSync over supported network connectivity addresses a separate condition: a data center has a high-bandwidth network path and millions of files to synchronize. DataSync efficiently copies files and metadata. The actual requirement is transfer completeness must be proven after a migration. Recommended corrective action: Run DataSync verification and reconcile object counts/checksums as applicable. Verified transfer reduces silent migration omissions.

For the alternative failure managed SFTP partners must upload only to individually assigned storage prefixes, the control Configure Transfer Family identity mappings and restricted home-directory storage permissions is relevant: sFTP user identity mappings restrict each configured user backend path. Here the evidence instead is transfer completeness must be proven after a migration. The selected technical mechanism is Run DataSync verification and reconcile object counts/checksums as applicable: verified transfer reduces silent migration omissions.

Use AWS Storage Gateway File Gateway solves a different operational problem where existing on-prem apps need a local cache backed by cloud object storage. File Gateway caches file access with S3-backed storage. It does not directly address transfer completeness must be proven after a migration. The case calls for Run DataSync verification and reconcile object counts/checksums as applicable, since verified transfer reduces silent migration omissions.

For the alternative failure a platform processes petabyte-scale custom Spark transformations, the control Use Amazon EMR or AWS Glue Spark according to workload control needs is relevant: managed Spark services scale distributed ETL. Here the evidence instead is an Athena report reads excessive data because its Glue table is not partitioned for its date filters. The selected technical mechanism is Partition S3 Parquet data and update Glue metadata for Athena partition pruning: partition predicates reduce scanned objects and query billing.

Implement Lake Formation grants in supported integrated engines solves a different operational problem where sQL users need governed permissions over multiple shared data lake tables. Lake Formation governs data lake table access. It does not directly address an Athena report reads excessive data because its Glue table is not partitioned for its date filters. The case calls for Partition S3 Parquet data and update Glue metadata for Athena partition pruning, since partition predicates reduce scanned objects and query billing.

The competing option Use DynamoDB rather than Athena as an OLTP store would be useful when a developer needs per-key millisecond record lookups for transactions. DynamoDB optimizes key-value access. The present issue is an Athena report reads excessive data because its Glue table is not partitioned for its date filters; it is more directly addressed by Partition S3 Parquet data and update Glue metadata for Athena partition pruning. Partition predicates reduce scanned objects and query billing.

For the alternative failure a developer needs per-key millisecond record lookups for transactions, the control Use DynamoDB rather than Athena as an OLTP store is relevant: dynamoDB optimizes key-value access. Here the evidence instead is analysts need high-throughput cross-table aggregations over large loaded historical datasets. The selected technical mechanism is Use Redshift warehouse architecture with appropriate sort/distribution: columnar warehouses excel at analytic scans.

The competing option Partition S3 Parquet data and update Glue metadata for Athena partition pruning would be useful when an Athena report reads excessive data because its Glue table is not partitioned for its date filters. Partition predicates reduce scanned objects and query billing. The present issue is analysts need high-throughput cross-table aggregations over large loaded historical datasets; it is more directly addressed by Use Redshift warehouse architecture with appropriate sort/distribution. Columnar warehouses excel at analytic scans.

The option Implement Lake Formation grants in supported integrated engines addresses a separate condition: sQL users need governed permissions over multiple shared data lake tables. Lake Formation governs data lake table access. The actual requirement is analysts need high-throughput cross-table aggregations over large loaded historical datasets. Recommended corrective action: Use Redshift warehouse architecture with appropriate sort/distribution. Columnar warehouses excel at analytic scans.

Partition S3 Parquet data and update Glue metadata for Athena partition pruning solves a different operational problem where an Athena report reads excessive data because its Glue table is not partitioned for its date filters. Partition predicates reduce scanned objects and query billing. It does not directly address a developer needs per-key millisecond record lookups for transactions. The case calls for Use DynamoDB rather than Athena as an OLTP store, since dynamoDB optimizes key-value access.

The option Use Redshift warehouse architecture with appropriate sort/distribution addresses a separate condition: analysts need high-throughput cross-table aggregations over large loaded historical datasets. Columnar warehouses excel at analytic scans. The actual requirement is a developer needs per-key millisecond record lookups for transactions. Recommended corrective action: Use DynamoDB rather than Athena as an OLTP store. DynamoDB optimizes key-value access.

Use Amazon EMR or AWS Glue Spark according to workload control needs would help if a platform processes petabyte-scale custom Spark transformations. Managed Spark services scale distributed ETL. That is not the symptom described here: a developer needs per-key millisecond record lookups for transactions. The matching response is Use DynamoDB rather than Athena as an OLTP store, because dynamoDB optimizes key-value access.

The competing option Use Amazon EMR or AWS Glue Spark according to workload control needs would be useful when a platform processes petabyte-scale custom Spark transformations. Managed Spark services scale distributed ETL. The present issue is sQL users need governed permissions over multiple shared data lake tables; it is more directly addressed by Implement Lake Formation grants in supported integrated engines. Lake Formation governs data lake table access.

Partition S3 Parquet data and update Glue metadata for Athena partition pruning would help if an Athena report reads excessive data because its Glue table is not partitioned for its date filters. Partition predicates reduce scanned objects and query billing. That is not the symptom described here: sQL users need governed permissions over multiple shared data lake tables. The matching response is Implement Lake Formation grants in supported integrated engines, because lake Formation governs data lake table access.

For the alternative failure analysts need high-throughput cross-table aggregations over large loaded historical datasets, the control Use Redshift warehouse architecture with appropriate sort/distribution is relevant: columnar warehouses excel at analytic scans. Here the evidence instead is sQL users need governed permissions over multiple shared data lake tables. The selected technical mechanism is Implement Lake Formation grants in supported integrated engines: lake Formation governs data lake table access.

The option Implement Lake Formation grants in supported integrated engines addresses a separate condition: sQL users need governed permissions over multiple shared data lake tables. Lake Formation governs data lake table access. The actual requirement is a platform processes petabyte-scale custom Spark transformations. Recommended corrective action: Use Amazon EMR or AWS Glue Spark according to workload control needs. Managed Spark services scale distributed ETL.

Partition S3 Parquet data and update Glue metadata for Athena partition pruning would help if an Athena report reads excessive data because its Glue table is not partitioned for its date filters. Partition predicates reduce scanned objects and query billing. That is not the symptom described here: a platform processes petabyte-scale custom Spark transformations. The matching response is Use Amazon EMR or AWS Glue Spark according to workload control needs, because managed Spark services scale distributed ETL.

For the alternative failure a developer needs per-key millisecond record lookups for transactions, the control Use DynamoDB rather than Athena as an OLTP store is relevant: dynamoDB optimizes key-value access. Here the evidence instead is a platform processes petabyte-scale custom Spark transformations. The selected technical mechanism is Use Amazon EMR or AWS Glue Spark according to workload control needs: managed Spark services scale distributed ETL.

Use checkpoints/bookmarks and idempotent sink write logic would help if a Glue transformation doubles records when the job is restarted. Incremental ETL needs replay-safe output. That is not the symptom described here: a Firehose buffer interval introduces unacceptable delay before objects appear in S3. The matching response is Tune supported Firehose buffering settings within service limits, because buffering trades delivery latency for batching efficiency.

Use a stream processor with event-time watermarks and late-data handling solves a different operational problem where an analytics dashboard must combine event-time windows despite late arrivals. Watermarks manage out-of-order event arrival. It does not directly address a Firehose buffer interval introduces unacceptable delay before objects appear in S3. The case calls for Tune supported Firehose buffering settings within service limits, since buffering trades delivery latency for batching efficiency.

The competing option Design partition keys to group events requiring ordering on one shard would be useful when kinesis records appear out of order when using different partition keys. Kinesis ordering is within shard rather than global. The present issue is a Firehose buffer interval introduces unacceptable delay before objects appear in S3; it is more directly addressed by Tune supported Firehose buffering settings within service limits. Buffering trades delivery latency for batching efficiency.

For the alternative failure a Firehose buffer interval introduces unacceptable delay before objects appear in S3, the control Tune supported Firehose buffering settings within service limits is relevant: buffering trades delivery latency for batching efficiency. Here the evidence instead is a Glue transformation doubles records when the job is restarted. The selected technical mechanism is Use checkpoints/bookmarks and idempotent sink write logic: incremental ETL needs replay-safe output.

The competing option Design partition keys to group events requiring ordering on one shard would be useful when kinesis records appear out of order when using different partition keys. Kinesis ordering is within shard rather than global. The present issue is a Glue transformation doubles records when the job is restarted; it is more directly addressed by Use checkpoints/bookmarks and idempotent sink write logic. Incremental ETL needs replay-safe output.

The option Add signed authentication/authorization and scoped input permissions addresses a separate condition: an ingestion API is exposed publicly without authentication. Data ingestion security must precede trusting submitted records. The actual requirement is a Glue transformation doubles records when the job is restarted. Recommended corrective action: Use checkpoints/bookmarks and idempotent sink write logic. Incremental ETL needs replay-safe output.

Add signed authentication/authorization and scoped input permissions solves a different operational problem where an ingestion API is exposed publicly without authentication. Data ingestion security must precede trusting submitted records. It does not directly address kinesis records appear out of order when using different partition keys. The case calls for Design partition keys to group events requiring ordering on one shard, since kinesis ordering is within shard rather than global.

The competing option Tune supported Firehose buffering settings within service limits would be useful when a Firehose buffer interval introduces unacceptable delay before objects appear in S3. Buffering trades delivery latency for batching efficiency. The present issue is kinesis records appear out of order when using different partition keys; it is more directly addressed by Design partition keys to group events requiring ordering on one shard. Kinesis ordering is within shard rather than global.

Use checkpoints/bookmarks and idempotent sink write logic would help if a Glue transformation doubles records when the job is restarted. Incremental ETL needs replay-safe output. That is not the symptom described here: kinesis records appear out of order when using different partition keys. The matching response is Design partition keys to group events requiring ordering on one shard, because kinesis ordering is within shard rather than global.

The competing option Tune supported Firehose buffering settings within service limits would be useful when a Firehose buffer interval introduces unacceptable delay before objects appear in S3. Buffering trades delivery latency for batching efficiency. The present issue is an analytics dashboard must combine event-time windows despite late arrivals; it is more directly addressed by Use a stream processor with event-time watermarks and late-data handling. Watermarks manage out-of-order event arrival.

The option Use checkpoints/bookmarks and idempotent sink write logic addresses a separate condition: a Glue transformation doubles records when the job is restarted. Incremental ETL needs replay-safe output. The actual requirement is an analytics dashboard must combine event-time windows despite late arrivals. Recommended corrective action: Use a stream processor with event-time watermarks and late-data handling. Watermarks manage out-of-order event arrival.

Add signed authentication/authorization and scoped input permissions would help if an ingestion API is exposed publicly without authentication. Data ingestion security must precede trusting submitted records. That is not the symptom described here: an analytics dashboard must combine event-time windows despite late arrivals. The matching response is Use a stream processor with event-time watermarks and late-data handling, because watermarks manage out-of-order event arrival.

The option Use checkpoints/bookmarks and idempotent sink write logic addresses a separate condition: a Glue transformation doubles records when the job is restarted. Incremental ETL needs replay-safe output. The actual requirement is an ingestion API is exposed publicly without authentication. Recommended corrective action: Add signed authentication/authorization and scoped input permissions. Data ingestion security must precede trusting submitted records.

Use a stream processor with event-time watermarks and late-data handling would help if an analytics dashboard must combine event-time windows despite late arrivals. Watermarks manage out-of-order event arrival. That is not the symptom described here: an ingestion API is exposed publicly without authentication. The matching response is Add signed authentication/authorization and scoped input permissions, because data ingestion security must precede trusting submitted records.

Tune supported Firehose buffering settings within service limits solves a different operational problem where a Firehose buffer interval introduces unacceptable delay before objects appear in S3. Buffering trades delivery latency for batching efficiency. It does not directly address an ingestion API is exposed publicly without authentication. The case calls for Add signed authentication/authorization and scoped input permissions, since data ingestion security must precede trusting submitted records.