Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

Allow inbound from the application security group at the database port would help if app instances require TCP database access without opening all VPC CIDRs. Group references scope private tier access. That is not the symptom described here: a service with overlapping partner CIDR requires bounded private access. The matching response is Publish a PrivateLink endpoint service instead of trying to route entire peer networks, because service-specific private endpoints avoid network-level CIDR overlap.

For the alternative failure a direct S3 gateway endpoint path is missing from one private subnet route table, the control Associate that route table with the endpoint is relevant: subnet route associations determine private service reachability. Here the evidence instead is a service with overlapping partner CIDR requires bounded private access. The selected technical mechanism is Publish a PrivateLink endpoint service instead of trying to route entire peer networks: service-specific private endpoints avoid network-level CIDR overlap.

Use a multi-AZ ALB with redundant healthy targets solves a different operational problem where public services in two AZs are reached through only one static host. Managed load balancing removes single-host ingress. It does not directly address a service with overlapping partner CIDR requires bounded private access. The case calls for Publish a PrivateLink endpoint service instead of trying to route entire peer networks, since service-specific private endpoints avoid network-level CIDR overlap.

Use Amazon Data Firehose with an S3 destination and buffer settings would help if streaming data must be delivered automatically to S3 with minimal custom code. Firehose handles managed stream delivery into destinations. That is not the symptom described here: millions of records per minute require ordered stream ingestion with replay. The matching response is Use Kinesis Data Streams with suitable capacity and retention, because streams retain ordered records per shard for multiple consumers.

For the alternative failure dashboards need visualization of curated warehouse metrics, the control Use Amazon QuickSuite or other supported BI tooling is relevant: business intelligence services present curated data to consumers. Here the evidence instead is millions of records per minute require ordered stream ingestion with replay. The selected technical mechanism is Use Kinesis Data Streams with suitable capacity and retention: streams retain ordered records per shard for multiple consumers.

Use AWS Glue Spark jobs and data catalog metadata solves a different operational problem where a scheduled batch job must transform CSV objects into partitioned Parquet. Glue handles managed ETL transformations. It does not directly address millions of records per minute require ordered stream ingestion with replay. The case calls for Use Kinesis Data Streams with suitable capacity and retention, since streams retain ordered records per shard for multiple consumers.

For the alternative failure dashboards need visualization of curated warehouse metrics, the control Use Amazon QuickSuite or other supported BI tooling is relevant: business intelligence services present curated data to consumers. Here the evidence instead is streaming data must be delivered automatically to S3 with minimal custom code. The selected technical mechanism is Use Amazon Data Firehose with an S3 destination and buffer settings: firehose handles managed stream delivery into destinations.

The competing option Use AWS Glue Spark jobs and data catalog metadata would be useful when a scheduled batch job must transform CSV objects into partitioned Parquet. Glue handles managed ETL transformations. The present issue is streaming data must be delivered automatically to S3 with minimal custom code; it is more directly addressed by Use Amazon Data Firehose with an S3 destination and buffer settings. Firehose handles managed stream delivery into destinations.

The option Use Kinesis Data Streams with suitable capacity and retention addresses a separate condition: millions of records per minute require ordered stream ingestion with replay. Streams retain ordered records per shard for multiple consumers. The actual requirement is streaming data must be delivered automatically to S3 with minimal custom code. Recommended corrective action: Use Amazon Data Firehose with an S3 destination and buffer settings. Firehose handles managed stream delivery into destinations.

Use Amazon Athena with Glue catalog tables solves a different operational problem where analysts need ad-hoc SQL over S3 without provisioning a database server. Athena queries S3 data serverlessly. It does not directly address a scheduled batch job must transform CSV objects into partitioned Parquet. The case calls for Use AWS Glue Spark jobs and data catalog metadata, since glue handles managed ETL transformations.

The option Use Amazon Data Firehose with an S3 destination and buffer settings addresses a separate condition: streaming data must be delivered automatically to S3 with minimal custom code. Firehose handles managed stream delivery into destinations. The actual requirement is a scheduled batch job must transform CSV objects into partitioned Parquet. Recommended corrective action: Use AWS Glue Spark jobs and data catalog metadata. Glue handles managed ETL transformations.

Use Amazon QuickSuite or other supported BI tooling would help if dashboards need visualization of curated warehouse metrics. Business intelligence services present curated data to consumers. That is not the symptom described here: a scheduled batch job must transform CSV objects into partitioned Parquet. The matching response is Use AWS Glue Spark jobs and data catalog metadata, because glue handles managed ETL transformations.

The option Use Kinesis Data Streams with suitable capacity and retention addresses a separate condition: millions of records per minute require ordered stream ingestion with replay. Streams retain ordered records per shard for multiple consumers. The actual requirement is analysts need ad-hoc SQL over S3 without provisioning a database server. Recommended corrective action: Use Amazon Athena with Glue catalog tables. Athena queries S3 data serverlessly.

Use Amazon QuickSuite or other supported BI tooling would help if dashboards need visualization of curated warehouse metrics. Business intelligence services present curated data to consumers. That is not the symptom described here: analysts need ad-hoc SQL over S3 without provisioning a database server. The matching response is Use Amazon Athena with Glue catalog tables, because athena queries S3 data serverlessly.

For the alternative failure streaming data must be delivered automatically to S3 with minimal custom code, the control Use Amazon Data Firehose with an S3 destination and buffer settings is relevant: firehose handles managed stream delivery into destinations. Here the evidence instead is analysts need ad-hoc SQL over S3 without provisioning a database server. The selected technical mechanism is Use Amazon Athena with Glue catalog tables: athena queries S3 data serverlessly.

The option Use Amazon Data Firehose with an S3 destination and buffer settings addresses a separate condition: streaming data must be delivered automatically to S3 with minimal custom code. Firehose handles managed stream delivery into destinations. The actual requirement is dashboards need visualization of curated warehouse metrics. Recommended corrective action: Use Amazon QuickSuite or other supported BI tooling. Business intelligence services present curated data to consumers.

Use Amazon Athena with Glue catalog tables would help if analysts need ad-hoc SQL over S3 without provisioning a database server. Athena queries S3 data serverlessly. That is not the symptom described here: dashboards need visualization of curated warehouse metrics. The matching response is Use Amazon QuickSuite or other supported BI tooling, because business intelligence services present curated data to consumers.

Use Kinesis Data Streams with suitable capacity and retention solves a different operational problem where millions of records per minute require ordered stream ingestion with replay. Streams retain ordered records per shard for multiple consumers. It does not directly address dashboards need visualization of curated warehouse metrics. The case calls for Use Amazon QuickSuite or other supported BI tooling, since business intelligence services present curated data to consumers.

Use AWS DataSync with scheduled incremental transfer would help if a batch file arrives on-prem and should be copied incrementally to S3. DataSync moves file data with transfer verification. That is not the symptom described here: raw records land in S3 but analysts cannot discover table schema. The matching response is Create Glue Data Catalog tables using crawlers or defined schemas, because catalog metadata exposes datasets to query engines.

For the alternative failure a CSV field changes type and Glue jobs fail silently, the control Add schema validation and error quarantine before publishing transformed data is relevant: explicit validation protects downstream table expectations. Here the evidence instead is raw records land in S3 but analysts cannot discover table schema. The selected technical mechanism is Create Glue Data Catalog tables using crawlers or defined schemas: catalog metadata exposes datasets to query engines.

Convert records to compressed Parquet and partition by common filters solves a different operational problem where ingested CSV queries scan enormous bytes for a few required columns. Columnar compression and pruning reduce query I/O. It does not directly address raw records land in S3 but analysts cannot discover table schema. The case calls for Create Glue Data Catalog tables using crawlers or defined schemas, since catalog metadata exposes datasets to query engines.

For the alternative failure raw records land in S3 but analysts cannot discover table schema, the control Create Glue Data Catalog tables using crawlers or defined schemas is relevant: catalog metadata exposes datasets to query engines. Here the evidence instead is a batch file arrives on-prem and should be copied incrementally to S3. The selected technical mechanism is Use AWS DataSync with scheduled incremental transfer: dataSync moves file data with transfer verification.

The competing option Convert records to compressed Parquet and partition by common filters would be useful when ingested CSV queries scan enormous bytes for a few required columns. Columnar compression and pruning reduce query I/O. The present issue is a batch file arrives on-prem and should be copied incrementally to S3; it is more directly addressed by Use AWS DataSync with scheduled incremental transfer. DataSync moves file data with transfer verification.

The option Add schema validation and error quarantine before publishing transformed data addresses a separate condition: a CSV field changes type and Glue jobs fail silently. Explicit validation protects downstream table expectations. The actual requirement is a batch file arrives on-prem and should be copied incrementally to S3. Recommended corrective action: Use AWS DataSync with scheduled incremental transfer. DataSync moves file data with transfer verification.

The competing option Configure Lake Formation and S3/IAM permissions for governed access would be useful when different analysts must not access each other's datasets in a shared lake. Data lake governance is an additional authorization layer. The present issue is ingested CSV queries scan enormous bytes for a few required columns; it is more directly addressed by Convert records to compressed Parquet and partition by common filters. Columnar compression and pruning reduce query I/O.

The option Add schema validation and error quarantine before publishing transformed data addresses a separate condition: a CSV field changes type and Glue jobs fail silently. Explicit validation protects downstream table expectations. The actual requirement is ingested CSV queries scan enormous bytes for a few required columns. Recommended corrective action: Convert records to compressed Parquet and partition by common filters. Columnar compression and pruning reduce query I/O.

Use AWS DataSync with scheduled incremental transfer would help if a batch file arrives on-prem and should be copied incrementally to S3. DataSync moves file data with transfer verification. That is not the symptom described here: ingested CSV queries scan enormous bytes for a few required columns. The matching response is Convert records to compressed Parquet and partition by common filters, because columnar compression and pruning reduce query I/O.

The competing option Add schema validation and error quarantine before publishing transformed data would be useful when a CSV field changes type and Glue jobs fail silently. Explicit validation protects downstream table expectations. The present issue is different analysts must not access each other's datasets in a shared lake; it is more directly addressed by Configure Lake Formation and S3/IAM permissions for governed access. Data lake governance is an additional authorization layer.

Use AWS DataSync with scheduled incremental transfer would help if a batch file arrives on-prem and should be copied incrementally to S3. DataSync moves file data with transfer verification. That is not the symptom described here: different analysts must not access each other's datasets in a shared lake. The matching response is Configure Lake Formation and S3/IAM permissions for governed access, because data lake governance is an additional authorization layer.

For the alternative failure ingested CSV queries scan enormous bytes for a few required columns, the control Convert records to compressed Parquet and partition by common filters is relevant: columnar compression and pruning reduce query I/O. Here the evidence instead is different analysts must not access each other's datasets in a shared lake. The selected technical mechanism is Configure Lake Formation and S3/IAM permissions for governed access: data lake governance is an additional authorization layer.

The option Convert records to compressed Parquet and partition by common filters addresses a separate condition: ingested CSV queries scan enormous bytes for a few required columns. Columnar compression and pruning reduce query I/O. The actual requirement is a CSV field changes type and Glue jobs fail silently. Recommended corrective action: Add schema validation and error quarantine before publishing transformed data. Explicit validation protects downstream table expectations.

Configure Lake Formation and S3/IAM permissions for governed access would help if different analysts must not access each other's datasets in a shared lake. Data lake governance is an additional authorization layer. That is not the symptom described here: a CSV field changes type and Glue jobs fail silently. The matching response is Add schema validation and error quarantine before publishing transformed data, because explicit validation protects downstream table expectations.

For the alternative failure raw records land in S3 but analysts cannot discover table schema, the control Create Glue Data Catalog tables using crawlers or defined schemas is relevant: catalog metadata exposes datasets to query engines. Here the evidence instead is a CSV field changes type and Glue jobs fail silently. The selected technical mechanism is Add schema validation and error quarantine before publishing transformed data: explicit validation protects downstream table expectations.