Amazon AWS SAA-C03 Data Ingestion, Streaming, Transfer, Transformation, Analytics, and Data Lakes Practice Test 3

 

Topic 16 Practice Test 3 covers Data Ingestion, Streaming, Transfer, Transformation, Analytics, and Data Lakes for AWS SAA-C03. For broader exam preparation, review the AWS SAA-C03 Exam Dumps. Every option includes focused technical reasoning explaining both the AWS architecture concept and its fit to the scenario.

Question 1

The ingestion layer must retain records long enough for replay. What should the team do to reprocess recent streaming events after a consumer bug is fixed? Choose ONE.

1. Kinesis Data Streams
2. Kinesis enhanced fan-out
3. Kinesis partition key design
4. Amazon Data Firehose

Correct Answer(s)

 

A

Rationale

1. Kinesis Data Streams is intended to build a durable real-time stream that applications consume directly. Case: the ingestion layer must retain records long enough for replay; required action: reprocess recent streaming events after a consumer bug is fixed. This aligns cleanly with the requirement.
2. Kinesis enhanced fan-out is designed to isolate high-throughput stream consumers from shared read limits. Requirement: reprocess recent streaming events after a consumer bug is fixed; key constraint: the ingestion layer must retain records long enough for replay. That mismatch makes it a weaker choice.
3. Kinesis partition key design fits situations that require teams to preserve ordering for an entity while distributing unrelated entities across shards. Because the ingestion layer must retain records long enough for replay, the team must reprocess recent streaming events after a consumer bug is fixed. This would address the wrong decision point.
4. Amazon Data Firehose can help a team load streaming data into managed destinations without building a custom consumer fleet. The case hinges on the ingestion layer must retain records long enough for replay. The team must reprocess recent streaming events after a consumer bug is fixed. That capability is not the required control.

 

Question 2

The team needs to reduce propagation delay from shards to a dedicated consumer application, but the consumer needs HTTP/2 push rather than repeated shared polling. Which option is best? Choose ONE.

1. Kinesis enhanced fan-out
2. Kinesis partition key design
3. Kinesis Data Streams
4. Amazon Data Firehose

Correct Answer(s)

 

A

Rationale

1. Kinesis enhanced fan-out addresses the need to isolate high-throughput stream consumers from shared read limits. Here, the consumer needs HTTP/2 push rather than repeated shared polling; design must reduce propagation delay from shards to a dedicated consumer application. That capability directly satisfies the constraint.
2. Kinesis partition key design maps related records to the same shard and therefore controls per-key ordering and distribution. Case: the consumer needs HTTP/2 push rather than repeated shared polling; required action: reduce propagation delay from shards to a dedicated consumer application. That leaves the key constraint unresolved.
3. Kinesis Data Streams primarily captures ordered streaming records in shards for custom consumers and replay within the stream retention period. Decisive fact: the consumer needs HTTP/2 push rather than repeated shared polling; requested action: reduce propagation delay from shards to a dedicated consumer application. That mismatch makes it a weaker choice.
4. Amazon Data Firehose fits situations that require teams to load streaming data into managed destinations without building a custom consumer fleet. Given the consumer needs HTTP/2 push rather than repeated shared polling, the relevant choice must reduce propagation delay from shards to a dedicated consumer application. This would address the wrong decision point.

 

Question 3

Given that the workload needs scoped ordering and parallelism, which approach most directly lets the team preserve transaction order for each customer without serializing every customer through one shard? Choose ONE.

1. Kinesis Data Streams
2. Kinesis partition key design
3. Kinesis enhanced fan-out
4. Amazon Data Firehose

Correct Answer(s)

 

B

Rationale

1. Kinesis Data Streams can help a team build a durable real-time stream that applications consume directly. Because the workload needs scoped ordering and parallelism, the team must preserve transaction order for each customer without serializing every customer through one shard. The scenario calls for another native capability.
2. Kinesis partition key design primarily maps related records to the same shard and therefore controls per-key ordering and distribution. Requirement: preserve transaction order for each customer without serializing every customer through one shard; key constraint: the workload needs scoped ordering and parallelism. That makes this the strongest fit.
3. Kinesis enhanced fan-out supports teams that need to isolate high-throughput stream consumers from shared read limits. Case: the workload needs scoped ordering and parallelism; required action: preserve transaction order for each customer without serializing every customer through one shard. This solves a neighboring problem instead.
4. Amazon Data Firehose is designed to load streaming data into managed destinations without building a custom consumer fleet. Limiting condition: the workload needs scoped ordering and parallelism; solution must preserve transaction order for each customer without serializing every customer through one shard. That leaves the key constraint unresolved.

 

Question 4

Which option best supports this requirement: send a managed stream to OpenSearch or S3 while retaining an optional backup copy? Constraint: the requirement is destination delivery rather than an application-facing event log. Choose ONE.

1. Amazon Data Firehose
2. Kinesis Data Streams
3. Kinesis partition key design
4. Kinesis enhanced fan-out

Correct Answer(s)

 

A

Rationale

1. Amazon Data Firehose fits situations that require teams to load streaming data into managed destinations without building a custom consumer fleet. Decisive fact: the requirement is destination delivery rather than an application-facing event log; requested action: send a managed stream to OpenSearch or S3 while retaining an optional backup copy. That makes this the strongest fit.
2. Kinesis Data Streams is designed to build a durable real-time stream that applications consume directly. Case: the requirement is destination delivery rather than an application-facing event log; required action: send a managed stream to OpenSearch or S3 while retaining an optional backup copy. That leaves the key constraint unresolved.
3. Kinesis partition key design is intended to preserve ordering for an entity while distributing unrelated entities across shards. Here, the requirement is destination delivery rather than an application-facing event log; design must send a managed stream to OpenSearch or S3 while retaining an optional backup copy. That capability is not the required control.
4. Kinesis enhanced fan-out gives registered consumers dedicated read throughput and low-latency push delivery from shards. The case hinges on the requirement is destination delivery rather than an application-facing event log. The team must send a managed stream to OpenSearch or S3 while retaining an optional backup copy. Its primary role differs from this need.

 

Question 5

The change is lightweight and belongs in the delivery path. What should the team do to convert source records into the destination schema during managed stream delivery? Choose ONE.

1. EventBridge Pipes
2. Amazon MSK
3. AWS Glue crawler
4. Firehose Lambda transformation

Correct Answer(s)

 

D

Rationale

1. EventBridge Pipes supports teams that need to build point-to-point event integration with minimal custom glue code. The case hinges on the change is lightweight and belongs in the delivery path. The team must convert source records into the destination schema during managed stream delivery. That capability is not the required control.
2. Amazon MSK provides managed Apache Kafka clusters and Kafka-compatible APIs for streaming workloads. Here, the change is lightweight and belongs in the delivery path; design must convert source records into the destination schema during managed stream delivery. Its primary role differs from this need.
3. AWS Glue crawler primarily inspects supported data stores and updates table metadata in the Glue Data Catalog. Limiting condition: the change is lightweight and belongs in the delivery path; solution must convert source records into the destination schema during managed stream delivery. That leaves the key constraint unresolved.
4. Firehose Lambda transformation addresses the need to apply lightweight record transformation inside a managed delivery pipeline. Because the change is lightweight and belongs in the delivery path, the team must convert source records into the destination schema during managed stream delivery. The documented behavior matches the requested result.

 

Question 6

The team needs to support Kafka partitions, consumer groups, and ecosystem tooling in AWS, but the workload requires native Kafka compatibility rather than Kinesis APIs. Which option is best? Choose ONE.

1. AWS Glue crawler
2. Firehose Lambda transformation
3. Amazon MSK
4. EventBridge Pipes

Correct Answer(s)

 

C

Rationale

1. AWS Glue crawler supports teams that need to discover data schema and partitions automatically. Here, the workload requires native Kafka compatibility rather than Kinesis APIs; design must support Kafka partitions, consumer groups, and ecosystem tooling in AWS. That capability is not the required control.
2. Firehose Lambda transformation can help a team apply lightweight record transformation inside a managed delivery pipeline. Decisive fact: the workload requires native Kafka compatibility rather than Kinesis APIs; requested action: support Kafka partitions, consumer groups, and ecosystem tooling in AWS. This does not deliver the requested outcome.
3. Amazon MSK is intended to run Kafka-compatible streaming applications without self-managing brokers. Given the workload requires native Kafka compatibility rather than Kinesis APIs, the relevant choice must support Kafka partitions, consumer groups, and ecosystem tooling in AWS. The documented behavior matches the requested result.
4. EventBridge Pipes is designed to build point-to-point event integration with minimal custom glue code. The case hinges on the workload requires native Kafka compatibility rather than Kinesis APIs. The team must support Kafka partitions, consumer groups, and ecosystem tooling in AWS. Its primary role differs from this need.

 

Question 7

Given that the integration should apply declarative filtering in transit, which approach most directly lets the team drop irrelevant source events before they reach an expensive downstream service? Choose ONE.

1. Firehose Lambda transformation
2. EventBridge Pipes
3. AWS Glue crawler
4. Amazon MSK

Correct Answer(s)

 

B

Rationale

1. Firehose Lambda transformation is intended to apply lightweight record transformation inside a managed delivery pipeline. Because the integration should apply declarative filtering in transit, the team must drop irrelevant source events before they reach an expensive downstream service. The scenario calls for another native capability.
2. EventBridge Pipes addresses the need to build point-to-point event integration with minimal custom glue code. Requirement: drop irrelevant source events before they reach an expensive downstream service; key constraint: the integration should apply declarative filtering in transit. This is the precise capability required.
3. AWS Glue crawler inspects supported data stores and updates table metadata in the Glue Data Catalog. Given the integration should apply declarative filtering in transit, the relevant choice must drop irrelevant source events before they reach an expensive downstream service. This would address the wrong decision point.
4. Amazon MSK fits situations that require teams to run Kafka-compatible streaming applications without self-managing brokers. Limiting condition: the integration should apply declarative filtering in transit; solution must drop irrelevant source events before they reach an expensive downstream service. That leaves the key constraint unresolved.

 

Question 8

Which option best supports this requirement: infer table definitions from a new data source before ETL jobs run? Constraint: the catalog should be populated by inspecting the source. Choose ONE.

1. Amazon MSK
2. EventBridge Pipes
3. AWS Glue crawler
4. Firehose Lambda transformation

Correct Answer(s)

 

C

Rationale

1. Amazon MSK supports teams that need to run Kafka-compatible streaming applications without self-managing brokers. Given the catalog should be populated by inspecting the source, the relevant choice must infer table definitions from a new data source before ETL jobs run. The scenario calls for another native capability.
2. EventBridge Pipes addresses the need to build point-to-point event integration with minimal custom glue code. Decisive fact: the catalog should be populated by inspecting the source; requested action: infer table definitions from a new data source before ETL jobs run. This does not deliver the requested outcome.
3. AWS Glue crawler primarily inspects supported data stores and updates table metadata in the Glue Data Catalog. The case hinges on the catalog should be populated by inspecting the source. The team must infer table definitions from a new data source before ETL jobs run. Its native behavior fits this need.
4. Firehose Lambda transformation can help a team apply lightweight record transformation inside a managed delivery pipeline. Case: the catalog should be populated by inspecting the source; required action: infer table definitions from a new data source before ETL jobs run. This solves a neighboring problem instead.

 

Question 9

The pipeline should avoid reprocessing previously handled input where supported. What should the team do to schedule repeatable serverless ETL with bookmarks to process new data? Choose ONE.

1. AWS Glue Data Catalog
2. AWS Lake Formation
3. Amazon Athena
4. AWS Glue ETL jobs

Correct Answer(s)

 

D

Rationale

1. AWS Glue Data Catalog fits situations that require teams to share data-lake table definitions across engines. Here, the pipeline should avoid reprocessing previously handled input where supported; design must schedule repeatable serverless ETL with bookmarks to process new data. Its primary role differs from this need.
2. AWS Lake Formation is designed to govern access to data-lake tables and columns centrally. Given the pipeline should avoid reprocessing previously handled input where supported, the relevant choice must schedule repeatable serverless ETL with bookmarks to process new data. This would address the wrong decision point.
3. Amazon Athena can help a team perform ad hoc SQL analysis without provisioning a data warehouse cluster. Limiting condition: the pipeline should avoid reprocessing previously handled input where supported; solution must schedule repeatable serverless ETL with bookmarks to process new data. This solves a neighboring problem instead.
4. AWS Glue ETL jobs is intended to perform scalable batch transformations without managing a persistent cluster. Requirement: schedule repeatable serverless ETL with bookmarks to process new data; key constraint: the pipeline should avoid reprocessing previously handled input where supported. This is the precise capability required.

 

Question 10

The team needs to maintain partition and column metadata independently of the underlying S3 objects, but query engines need a catalog rather than file relocation. Which option is best? Choose ONE.

1. AWS Glue ETL jobs
2. AWS Lake Formation
3. Amazon Athena
4. AWS Glue Data Catalog

Correct Answer(s)

 

D

Rationale

1. AWS Glue ETL jobs addresses the need to perform scalable batch transformations without managing a persistent cluster. Case: query engines need a catalog rather than file relocation; required action: maintain partition and column metadata independently of the underlying S3 objects. This solves a neighboring problem instead.
2. AWS Lake Formation centralizes fine-grained data lake permissions and governance over cataloged data. Requirement: maintain partition and column metadata independently of the underlying S3 objects; key constraint: query engines need a catalog rather than file relocation. That mismatch makes it a weaker choice.
3. Amazon Athena fits situations that require teams to perform ad hoc SQL analysis without provisioning a data warehouse cluster. The case hinges on query engines need a catalog rather than file relocation. The team must maintain partition and column metadata independently of the underlying S3 objects. Its primary role differs from this need.
4. AWS Glue Data Catalog primarily stores centralized technical metadata for tables and schemas used by multiple analytics services. Because query engines need a catalog rather than file relocation, the team must maintain partition and column metadata independently of the underlying S3 objects. This addresses the decision at the right layer.

 

Question 11

Given that the organization needs governed authorization above raw object access, which approach most directly lets the team separate data-lake administrative roles from consumers while controlling table-level access? Choose ONE.

1. AWS Lake Formation
2. AWS Glue Data Catalog
3. AWS Glue ETL jobs
4. Amazon Athena

Correct Answer(s)

 

A

Rationale

1. AWS Lake Formation can help a team govern access to data-lake tables and columns centrally. Here, the organization needs governed authorization above raw object access; design must separate data-lake administrative roles from consumers while controlling table-level access. That capability directly satisfies the constraint.
2. AWS Glue Data Catalog primarily stores centralized technical metadata for tables and schemas used by multiple analytics services. Given the organization needs governed authorization above raw object access, the relevant choice must separate data-lake administrative roles from consumers while controlling table-level access. This would address the wrong decision point.
3. AWS Glue ETL jobs supports teams that need to perform scalable batch transformations without managing a persistent cluster. Requirement: separate data-lake administrative roles from consumers while controlling table-level access; key constraint: the organization needs governed authorization above raw object access. This does not deliver the requested outcome.
4. Amazon Athena is designed to perform ad hoc SQL analysis without provisioning a data warehouse cluster. Decisive fact: the organization needs governed authorization above raw object access; requested action: separate data-lake administrative roles from consumers while controlling table-level access. That mismatch makes it a weaker choice.

 

Question 12

Which option best supports this requirement: expose query results from a data lake to analysts who need standard SQL and pay-per-scan economics? Constraint: the workload does not require continuously provisioned compute. Choose ONE.

1. AWS Glue ETL jobs
2. AWS Lake Formation
3. AWS Glue Data Catalog
4. Amazon Athena

Correct Answer(s)

 

D

Rationale

1. AWS Glue ETL jobs is designed to perform scalable batch transformations without managing a persistent cluster. Requirement: expose query results from a data lake to analysts who need standard SQL and pay-per-scan economics; key constraint: the workload does not require continuously provisioned compute. That mismatch makes it a weaker choice.
2. AWS Lake Formation fits situations that require teams to govern access to data-lake tables and columns centrally. Because the workload does not require continuously provisioned compute, the team must expose query results from a data lake to analysts who need standard SQL and pay-per-scan economics. This would address the wrong decision point.
3. AWS Glue Data Catalog stores centralized technical metadata for tables and schemas used by multiple analytics services. Limiting condition: the workload does not require continuously provisioned compute; solution must expose query results from a data lake to analysts who need standard SQL and pay-per-scan economics. That leaves the key constraint unresolved.
4. Amazon Athena is intended to perform ad hoc SQL analysis without provisioning a data warehouse cluster. Case: the workload does not require continuously provisioned compute; required action: expose query results from a data lake to analysts who need standard SQL and pay-per-scan economics. This aligns cleanly with the requirement.

 

Question 13

Warehouse storage should be reserved for hotter data. What should the team do to keep infrequently queried facts in S3 while making them available in Redshift joins? Choose ONE.

1. AWS Database Migration Service
2. Amazon EMR
3. AWS DataSync
4. Amazon Redshift Spectrum

Correct Answer(s)

 

D

Rationale

1. AWS Database Migration Service moves databases and can replicate ongoing changes with change data capture. Case: warehouse storage should be reserved for hotter data; required action: keep infrequently queried facts in S3 while making them available in Redshift joins. That leaves the key constraint unresolved.
2. Amazon EMR supports teams that need to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Limiting condition: warehouse storage should be reserved for hotter data; solution must keep infrequently queried facts in S3 while making them available in Redshift joins. This solves a neighboring problem instead.
3. AWS DataSync primarily accelerates and automates online data transfer between supported storage systems and AWS storage services. Decisive fact: warehouse storage should be reserved for hotter data; requested action: keep infrequently queried facts in S3 while making them available in Redshift joins. That mismatch makes it a weaker choice.
4. Amazon Redshift Spectrum addresses the need to combine Redshift warehouse queries with large external S3 datasets. Here, warehouse storage should be reserved for hotter data; design must keep infrequently queried facts in S3 while making them available in Redshift joins. That capability directly satisfies the constraint.

 

Question 14

The team needs to operate a long-running analytics cluster that combines multiple open-source big-data frameworks, but the workload needs persistent framework-level control. Which option is best? Choose ONE.

1. AWS DataSync
2. AWS Database Migration Service
3. Amazon EMR
4. Amazon Redshift Spectrum

Correct Answer(s)

 

C

Rationale

1. AWS DataSync can help a team move large file or object datasets with managed scheduling, verification, and network optimization. Because the workload needs persistent framework-level control, the team must operate a long-running analytics cluster that combines multiple open-source big-data frameworks. The scenario calls for another native capability.
2. AWS Database Migration Service is intended to migrate or continuously replicate supported database data with minimal downtime. The case hinges on the workload needs persistent framework-level control. The team must operate a long-running analytics cluster that combines multiple open-source big-data frameworks. That capability is not the required control.
3. Amazon EMR is designed to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Limiting condition: the workload needs persistent framework-level control; solution must operate a long-running analytics cluster that combines multiple open-source big-data frameworks. That is the direct functional match.
4. Amazon Redshift Spectrum supports teams that need to combine Redshift warehouse queries with large external S3 datasets. Case: the workload needs persistent framework-level control; required action: operate a long-running analytics cluster that combines multiple open-source big-data frameworks. This solves a neighboring problem instead.

 

Question 15

Given that the migration needs managed data movement rather than host-level file copying, which approach most directly lets the team move data between heterogeneous database engines while keeping the application online? Choose ONE.

1. Amazon Redshift Spectrum
2. AWS Database Migration Service
3. Amazon EMR
4. AWS DataSync

Correct Answer(s)

 

B

Rationale

1. Amazon Redshift Spectrum lets Redshift query external data in S3 through external schemas while joining it with warehouse data. The case hinges on the migration needs managed data movement rather than host-level file copying. The team must move data between heterogeneous database engines while keeping the application online. Its primary role differs from this need.
2. AWS Database Migration Service addresses the need to migrate or continuously replicate supported database data with minimal downtime. Given the migration needs managed data movement rather than host-level file copying, the relevant choice must move data between heterogeneous database engines while keeping the application online. The documented behavior matches the requested result.
3. Amazon EMR fits situations that require teams to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Decisive fact: the migration needs managed data movement rather than host-level file copying; requested action: move data between heterogeneous database engines while keeping the application online. That mismatch makes it a weaker choice.
4. AWS DataSync is intended to move large file or object datasets with managed scheduling, verification, and network optimization. Here, the migration needs managed data movement rather than host-level file copying; design must move data between heterogeneous database engines while keeping the application online. That capability is not the required control.

 

Question 16

Which option best supports this requirement: move a large S3-compatible object set into AWS with automated integrity verification? Constraint: the transfer occurs online and should be accelerated and monitored. Choose ONE.

1. Amazon Redshift Spectrum
2. AWS DataSync
3. Amazon EMR
4. AWS Database Migration Service

Correct Answer(s)

 

B

Rationale

1. Amazon Redshift Spectrum addresses the need to combine Redshift warehouse queries with large external S3 datasets. Because the transfer occurs online and should be accelerated and monitored, the team must move a large S3-compatible object set into AWS with automated integrity verification. The scenario calls for another native capability.
2. AWS DataSync can help a team move large file or object datasets with managed scheduling, verification, and network optimization. Requirement: move a large S3-compatible object set into AWS with automated integrity verification; key constraint: the transfer occurs online and should be accelerated and monitored. This is the precise capability required.
3. Amazon EMR primarily runs managed big-data frameworks such as Spark and Hadoop for flexible large-scale processing. Limiting condition: the transfer occurs online and should be accelerated and monitored; solution must move a large S3-compatible object set into AWS with automated integrity verification. That leaves the key constraint unresolved.
4. AWS Database Migration Service supports teams that need to migrate or continuously replicate supported database data with minimal downtime. The case hinges on the transfer occurs online and should be accelerated and monitored. The team must move a large S3-compatible object set into AWS with automated integrity verification. That capability is not the required control.

 

Question 17

The integration requires AS2 rather than object APIs. What should the team do to accept AS2 business documents into AWS storage using a managed service? Choose ONE.

1. AWS Transfer Family
2. AWS Glue streaming ETL
3. S3 Transfer Acceleration
4. Amazon AppFlow

Correct Answer(s)

 

A

Rationale

1. AWS Transfer Family can help a team receive partner file transfers using familiar managed protocols. Decisive fact: the integration requires AS2 rather than object APIs; requested action: accept AS2 business documents into AWS storage using a managed service. This is the precise capability required.
2. AWS Glue streaming ETL is intended to apply stateful or scalable streaming transformations before writing curated outputs. Given the integration requires AS2 rather than object APIs, the relevant choice must accept AS2 business documents into AWS storage using a managed service. The scenario calls for another native capability.
3. S3 Transfer Acceleration is designed to accelerate client uploads to an S3 bucket over the public internet from distant locations. The case hinges on the integration requires AS2 rather than object APIs. The team must accept AS2 business documents into AWS storage using a managed service. Its primary role differs from this need.
4. Amazon AppFlow fits situations that require teams to ingest SaaS application data without custom API integration code. Case: the integration requires AS2 rather than object APIs; required action: accept AS2 business documents into AWS storage using a managed service. That leaves the key constraint unresolved.

 

Question 18

The team needs to apply field mapping while moving supported SaaS data into a data lake, but the ingestion layer needs managed SaaS connectivity and simple transformation. Which option is best? Choose ONE.

1. AWS Glue streaming ETL
2. Amazon AppFlow
3. AWS Transfer Family
4. S3 Transfer Acceleration

Correct Answer(s)

 

B

Rationale

1. AWS Glue streaming ETL addresses the need to apply stateful or scalable streaming transformations before writing curated outputs. Requirement: apply field mapping while moving supported SaaS data into a data lake; key constraint: the ingestion layer needs managed SaaS connectivity and simple transformation. This does not deliver the requested outcome.
2. Amazon AppFlow moves data between supported SaaS applications and AWS services with managed connectors. Given the ingestion layer needs managed SaaS connectivity and simple transformation, the relevant choice must apply field mapping while moving supported SaaS data into a data lake. This addresses the decision at the right layer.
3. AWS Transfer Family primarily provides managed SFTP, FTPS, FTP, and AS2 endpoints backed by AWS storage. Here, the ingestion layer needs managed SaaS connectivity and simple transformation; design must apply field mapping while moving supported SaaS data into a data lake. Its primary role differs from this need.
4. S3 Transfer Acceleration fits situations that require teams to accelerate client uploads to an S3 bucket over the public internet from distant locations. Limiting condition: the ingestion layer needs managed SaaS connectivity and simple transformation; solution must apply field mapping while moving supported SaaS data into a data lake. That leaves the key constraint unresolved.

 

Question 19

Given that the transfer is online and geographically distant, which approach most directly lets the team speed uploads to S3 while preserving the bucket as the final destination and avoiding an appliance? Choose ONE.

1. Amazon AppFlow
2. AWS Glue streaming ETL
3. S3 Transfer Acceleration
4. AWS Transfer Family

Correct Answer(s)

 

C

Rationale

1. Amazon AppFlow can help a team ingest SaaS application data without custom API integration code. Case: the transfer is online and geographically distant; required action: speed uploads to S3 while preserving the bucket as the final destination and avoiding an appliance. This solves a neighboring problem instead.
2. AWS Glue streaming ETL is designed to apply stateful or scalable streaming transformations before writing curated outputs. Because the transfer is online and geographically distant, the team must speed uploads to S3 while preserving the bucket as the final destination and avoiding an appliance. This would address the wrong decision point.
3. S3 Transfer Acceleration supports teams that need to accelerate client uploads to an S3 bucket over the public internet from distant locations. Given the transfer is online and geographically distant, the relevant choice must speed uploads to S3 while preserving the bucket as the final destination and avoiding an appliance. The documented behavior matches the requested result.
4. AWS Transfer Family primarily provides managed SFTP, FTPS, FTP, and AS2 endpoints backed by AWS storage. The case hinges on the transfer is online and geographically distant. The team must speed uploads to S3 while preserving the bucket as the final destination and avoiding an appliance. Its primary role differs from this need.

 

Question 20

Which option best supports this requirement: enrich high-volume stream records using distributed processing before landing curated data? Constraint: the transformation needs Spark-scale processing rather than simple per-record mapping. Choose ONE.

1. AWS Transfer Family
2. Amazon AppFlow
3. AWS Glue streaming ETL
4. S3 Transfer Acceleration

Correct Answer(s)

 

C

Rationale

1. AWS Transfer Family is intended to receive partner file transfers using familiar managed protocols. Requirement: enrich high-volume stream records using distributed processing before landing curated data; key constraint: the transformation needs Spark-scale processing rather than simple per-record mapping. This does not deliver the requested outcome.
2. Amazon AppFlow is designed to ingest SaaS application data without custom API integration code. Given the transformation needs Spark-scale processing rather than simple per-record mapping, the relevant choice must enrich high-volume stream records using distributed processing before landing curated data. This would address the wrong decision point.
3. AWS Glue streaming ETL fits situations that require teams to apply stateful or scalable streaming transformations before writing curated outputs. Here, the transformation needs Spark-scale processing rather than simple per-record mapping; design must enrich high-volume stream records using distributed processing before landing curated data. Its native behavior fits this need.
4. S3 Transfer Acceleration uses Amazon CloudFront edge locations and optimized network paths to speed long-distance uploads to S3. Decisive fact: the transformation needs Spark-scale processing rather than simple per-record mapping; requested action: enrich high-volume stream records using distributed processing before landing curated data. That mismatch makes it a weaker choice.

• < Amazon AWS SAA-C03 Data Ingestion, Streaming, Transfer, Transformation, Analytics, and Data Lakes Practice Test 2
• Amazon AWS SAA-C03 Cost-Optimized Storage, Tiering, Lifecycle, Backup, Archive, and Transfer Practice Test 1 >