Amazon AWS SAA-C03 Data Ingestion, Streaming, Transfer, Transformation, Analytics, and Data Lakes Practice Test 2

 

Topic 16 Practice Test 2 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

Which option best supports this requirement: scale a real-time event stream while preserving per-partition ordering? Constraint: producers need an ordered shard-based stream rather than direct destination delivery. Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. Kinesis enhanced fan-out gives registered consumers dedicated read throughput and low-latency push delivery from shards. Limiting condition: producers need an ordered shard-based stream rather than direct destination delivery; solution must scale a real-time event stream while preserving per-partition ordering. That leaves the key constraint unresolved.
2. Kinesis partition key design addresses the need to preserve ordering for an entity while distributing unrelated entities across shards. The case hinges on producers need an ordered shard-based stream rather than direct destination delivery. The team must scale a real-time event stream while preserving per-partition ordering. That capability is not the required control.
3. Amazon Data Firehose primarily buffers streaming records and delivers them to supported destinations such as S3 with minimal consumer code. Requirement: scale a real-time event stream while preserving per-partition ordering; key constraint: producers need an ordered shard-based stream rather than direct destination delivery. That mismatch makes it a weaker choice.
4. Kinesis Data Streams supports teams that need to build a durable real-time stream that applications consume directly. Case: producers need an ordered shard-based stream rather than direct destination delivery; required action: scale a real-time event stream while preserving per-partition ordering. This aligns cleanly with the requirement.

 

Question 2

The new consumer must not reduce shared polling capacity. What should the team do to give one analytics consumer dedicated throughput while other applications continue reading the stream? Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. Kinesis partition key design supports teams that need to preserve ordering for an entity while distributing unrelated entities across shards. Limiting condition: the new consumer must not reduce shared polling capacity; solution must give one analytics consumer dedicated throughput while other applications continue reading the stream. This solves a neighboring problem instead.
2. Kinesis enhanced fan-out can help a team isolate high-throughput stream consumers from shared read limits. Given the new consumer must not reduce shared polling capacity, the relevant choice must give one analytics consumer dedicated throughput while other applications continue reading the stream. The documented behavior matches the requested result.
3. Kinesis Data Streams is designed to build a durable real-time stream that applications consume directly. Case: the new consumer must not reduce shared polling capacity; required action: give one analytics consumer dedicated throughput while other applications continue reading the stream. That leaves the key constraint unresolved.
4. Amazon Data Firehose is intended to load streaming data into managed destinations without building a custom consumer fleet. Here, the new consumer must not reduce shared polling capacity; design must give one analytics consumer dedicated throughput while other applications continue reading the stream. That capability is not the required control.

 

Question 3

The team needs to avoid a hot shard caused by choosing a low-cardinality partition key, but one key value dominates traffic and creates uneven shard utilization. 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)

 

B

Rationale

1. Kinesis enhanced fan-out is intended to isolate high-throughput stream consumers from shared read limits. The case hinges on one key value dominates traffic and creates uneven shard utilization. The team must avoid a hot shard caused by choosing a low-cardinality partition key. That capability is not the required control.
2. Kinesis partition key design fits situations that require teams to preserve ordering for an entity while distributing unrelated entities across shards. Requirement: avoid a hot shard caused by choosing a low-cardinality partition key; key constraint: one key value dominates traffic and creates uneven shard utilization. That makes this the strongest fit.
3. Kinesis Data Streams addresses the need to build a durable real-time stream that applications consume directly. Because one key value dominates traffic and creates uneven shard utilization, the team must avoid a hot shard caused by choosing a low-cardinality partition key. The scenario calls for another native capability.
4. Amazon Data Firehose buffers streaming records and delivers them to supported destinations such as S3 with minimal consumer code. Here, one key value dominates traffic and creates uneven shard utilization; design must avoid a hot shard caused by choosing a low-cardinality partition key. Its primary role differs from this need.

 

Question 4

Given that small events should be efficiently buffered without custom workers, which approach most directly lets the team batch incoming records by size or interval before placing them in an analytics destination? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. Kinesis Data Streams primarily captures ordered streaming records in shards for custom consumers and replay within the stream retention period. Case: small events should be efficiently buffered without custom workers; required action: batch incoming records by size or interval before placing them in an analytics destination. That leaves the key constraint unresolved.
2. Kinesis partition key design addresses the need to preserve ordering for an entity while distributing unrelated entities across shards. Given small events should be efficiently buffered without custom workers, the relevant choice must batch incoming records by size or interval before placing them in an analytics destination. The scenario calls for another native capability.
3. Amazon Data Firehose can help a team load streaming data into managed destinations without building a custom consumer fleet. Decisive fact: small events should be efficiently buffered without custom workers; requested action: batch incoming records by size or interval before placing them in an analytics destination. This is the precise capability required.
4. Kinesis enhanced fan-out supports teams that need to isolate high-throughput stream consumers from shared read limits. Here, small events should be efficiently buffered without custom workers; design must batch incoming records by size or interval before placing them in an analytics destination. That capability is not the required control.

 

Question 5

Which option best supports this requirement: redact a field from telemetry before Firehose writes to the analytics destination? Constraint: a simple custom transformation is needed without running a separate consumer service. Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. AWS Glue crawler can help a team discover data schema and partitions automatically. Requirement: redact a field from telemetry before Firehose writes to the analytics destination; key constraint: a simple custom transformation is needed without running a separate consumer service. This does not deliver the requested outcome.
2. EventBridge Pipes is designed to build point-to-point event integration with minimal custom glue code. Here, a simple custom transformation is needed without running a separate consumer service; design must redact a field from telemetry before Firehose writes to the analytics destination. Its primary role differs from this need.
3. Firehose Lambda transformation is intended to apply lightweight record transformation inside a managed delivery pipeline. Because a simple custom transformation is needed without running a separate consumer service, the team must redact a field from telemetry before Firehose writes to the analytics destination. The documented behavior matches the requested result.
4. Amazon MSK fits situations that require teams to run Kafka-compatible streaming applications without self-managing brokers. Limiting condition: a simple custom transformation is needed without running a separate consumer service; solution must redact a field from telemetry before Firehose writes to the analytics destination. That leaves the key constraint unresolved.

 

Question 6

The organization cannot rewrite its Kafka consumers. What should the team do to retain Kafka semantics for an existing ecosystem while moving broker operations to a managed AWS service? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. AWS Glue crawler fits situations that require teams to discover data schema and partitions automatically. Case: the organization cannot rewrite its Kafka consumers; required action: retain Kafka semantics for an existing ecosystem while moving broker operations to a managed AWS service. That leaves the key constraint unresolved.
2. Firehose Lambda transformation addresses the need to apply lightweight record transformation inside a managed delivery pipeline. Decisive fact: the organization cannot rewrite its Kafka consumers; requested action: retain Kafka semantics for an existing ecosystem while moving broker operations to a managed AWS service. This does not deliver the requested outcome.
3. EventBridge Pipes connects a supported event source to a target with filtering and optional enrichment. Because the organization cannot rewrite its Kafka consumers, the team must retain Kafka semantics for an existing ecosystem while moving broker operations to a managed AWS service. This would address the wrong decision point.
4. Amazon MSK primarily provides managed Apache Kafka clusters and Kafka-compatible APIs for streaming workloads. The case hinges on the organization cannot rewrite its Kafka consumers. The team must retain Kafka semantics for an existing ecosystem while moving broker operations to a managed AWS service. Its native behavior fits this need.

 

Question 7

The team needs to connect a stream source to Step Functions without maintaining a polling Lambda, but managed source polling and filtering are preferred. Which option is best? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. EventBridge Pipes can help a team build point-to-point event integration with minimal custom glue code. Limiting condition: managed source polling and filtering are preferred; solution must connect a stream source to Step Functions without maintaining a polling Lambda. This aligns cleanly with the requirement.
2. Firehose Lambda transformation supports teams that need to apply lightweight record transformation inside a managed delivery pipeline. Because managed source polling and filtering are preferred, the team must connect a stream source to Step Functions without maintaining a polling Lambda. The scenario calls for another native capability.
3. Amazon MSK is designed to run Kafka-compatible streaming applications without self-managing brokers. Given managed source polling and filtering are preferred, the relevant choice must connect a stream source to Step Functions without maintaining a polling Lambda. This would address the wrong decision point.
4. AWS Glue crawler primarily inspects supported data stores and updates table metadata in the Glue Data Catalog. Here, managed source polling and filtering are preferred; design must connect a stream source to Step Functions without maintaining a polling Lambda. Its primary role differs from this need.

 

Question 8

Given that the files already exist and the problem is metadata discovery, which approach most directly lets the team detect partitions added to an S3 data lake and expose them through catalog metadata? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. AWS Glue crawler inspects supported data stores and updates table metadata in the Glue Data Catalog. Requirement: detect partitions added to an S3 data lake and expose them through catalog metadata; key constraint: the files already exist and the problem is metadata discovery. That makes this the strongest fit.
2. EventBridge Pipes is intended to build point-to-point event integration with minimal custom glue code. Decisive fact: the files already exist and the problem is metadata discovery; requested action: detect partitions added to an S3 data lake and expose them through catalog metadata. This does not deliver the requested outcome.
3. Amazon MSK fits situations that require teams to run Kafka-compatible streaming applications without self-managing brokers. The case hinges on the files already exist and the problem is metadata discovery. The team must detect partitions added to an S3 data lake and expose them through catalog metadata. Its primary role differs from this need.
4. Firehose Lambda transformation is designed to apply lightweight record transformation inside a managed delivery pipeline. Because the files already exist and the problem is metadata discovery, the team must detect partitions added to an S3 data lake and expose them through catalog metadata. This would address the wrong decision point.

 

Question 9

Which option best supports this requirement: join large data sets before writing curated tables back to S3? Constraint: the workload needs distributed transformation rather than interactive SQL only. Choose ONE.

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

Correct Answer(s)

 

B

Rationale

3. AWS Lake Formation addresses the need to govern access to data-lake tables and columns centrally. Limiting condition: the workload needs distributed transformation rather than interactive SQL only; solution must join large data sets before writing curated tables back to S3. This solves a neighboring problem instead.
4. AWS Glue ETL jobs primarily provide managed serverless data integration using Spark or supported engines to transform data. Given the workload needs distributed transformation rather than interactive SQL only, the relevant choice must join large data sets before writing curated tables back to S3. This addresses the decision at the right layer.
5. AWS Glue Data Catalog supports teams that need to share data-lake table definitions across engines. Requirement: join large data sets before writing curated tables back to S3; key constraint: the workload needs distributed transformation rather than interactive SQL only. This does not deliver the requested outcome.
6. Amazon Athena runs serverless SQL queries directly against data in S3 using catalog metadata. Decisive fact: the workload needs distributed transformation rather than interactive SQL only; requested action: join large data sets before writing curated tables back to S3. That mismatch makes it a weaker choice.

 

Question 10

Duplicating table definitions across engines should be avoided. What should the team do to register a schema once so several AWS analytics tools can discover the data consistently? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. AWS Lake Formation is designed to govern access to data-lake tables and columns centrally. Requirement: register a schema once so several AWS analytics tools can discover the data consistently; key constraint: duplicating table definitions across engines should be avoided. That mismatch makes it a weaker choice.
2. AWS Glue ETL jobs is intended to perform scalable batch transformations without managing a persistent cluster. Case: duplicating table definitions across engines should be avoided; required action: register a schema once so several AWS analytics tools can discover the data consistently. This solves a neighboring problem instead.
3. AWS Glue Data Catalog supports teams that need to share data-lake table definitions across engines. Decisive fact: duplicating table definitions across engines should be avoided; requested action: register a schema once so several AWS analytics tools can discover the data consistently. This is the precise capability required.
4. Amazon Athena can help a team perform ad hoc SQL analysis without provisioning a data warehouse cluster. The case hinges on duplicating table definitions across engines should be avoided. The team must register a schema once so several AWS analytics tools can discover the data consistently. That capability is not the required control.

 

Question 11

The team needs to centralize cross-account sharing of governed data lake resources, but multiple accounts need controlled access to cataloged datasets. Which option is best? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. AWS Glue Data Catalog stores centralized technical metadata for tables and schemas used by multiple analytics services. Case: multiple accounts need controlled access to cataloged datasets; required action: centralize cross-account sharing of governed data lake resources. That leaves the key constraint unresolved.
2. Amazon Athena addresses the need to perform ad hoc SQL analysis without provisioning a data warehouse cluster. Here, multiple accounts need controlled access to cataloged datasets; design must centralize cross-account sharing of governed data lake resources. That capability is not the required control.
3. AWS Glue ETL jobs fits situations that require teams to perform scalable batch transformations without managing a persistent cluster. Given multiple accounts need controlled access to cataloged datasets, the relevant choice must centralize cross-account sharing of governed data lake resources. This would address the wrong decision point.
4. AWS Lake Formation is intended to govern access to data-lake tables and columns centrally. Limiting condition: multiple accounts need controlled access to cataloged datasets; solution must centralize cross-account sharing of governed data lake resources. This aligns cleanly with the requirement.

 

Question 12

Given that the data should remain in the lake and no cluster should be maintained, which approach most directly lets the team investigate a partitioned S3 log data set with serverless SQL? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. Amazon Athena can help a team perform ad hoc SQL analysis without provisioning a data warehouse cluster. Because the data should remain in the lake and no cluster should be maintained, the team must investigate a partitioned S3 log data set with serverless SQL. The documented behavior matches the requested result.
2. AWS Glue Data Catalog supports teams that need to share data-lake table definitions across engines. Case: the data should remain in the lake and no cluster should be maintained; required action: investigate a partitioned S3 log data set with serverless SQL. This solves a neighboring problem instead.
3. AWS Glue ETL jobs addresses the need to perform scalable batch transformations without managing a persistent cluster. The case hinges on the data should remain in the lake and no cluster should be maintained. The team must investigate a partitioned S3 log data set with serverless SQL. That capability is not the required control.
4. AWS Lake Formation primarily centralizes fine-grained data lake permissions and governance over cataloged data. Requirement: investigate a partitioned S3 log data set with serverless SQL; key constraint: the data should remain in the lake and no cluster should be maintained. That mismatch makes it a weaker choice.

 

Question 13

Which option best supports this requirement: extend an existing Redshift workload to query external lake tables? Constraint: the analysts already use Redshift SQL and need lake access. Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. AWS Database Migration Service fits situations that require teams to migrate or continuously replicate supported database data with minimal downtime. Decisive fact: the analysts already use Redshift SQL and need lake access; requested action: extend an existing Redshift workload to query external lake tables. That mismatch makes it a weaker choice.
2. Amazon Redshift Spectrum can help a team combine Redshift warehouse queries with large external S3 datasets. Given the analysts already use Redshift SQL and need lake access, the relevant choice must extend an existing Redshift workload to query external lake tables. The documented behavior matches the requested result.
3. AWS DataSync is intended to move large file or object datasets with managed scheduling, verification, and network optimization. Here, the analysts already use Redshift SQL and need lake access; design must extend an existing Redshift workload to query external lake tables. That capability is not the required control.
4. Amazon EMR is designed to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Case: the analysts already use Redshift SQL and need lake access; required action: extend an existing Redshift workload to query external lake tables. That leaves the key constraint unresolved.

 

Question 14

Migration should preserve ecosystem APIs and jobs. What should the team do to process petabyte-scale data with Hadoop-compatible tooling already used by the engineering team? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. Amazon EMR runs managed big-data frameworks such as Spark and Hadoop for flexible large-scale processing. Here, migration should preserve ecosystem APIs and jobs; design must process petabyte-scale data with Hadoop-compatible tooling already used by the engineering team. Its native behavior fits this need.
2. AWS Database Migration Service addresses the need to migrate or continuously replicate supported database data with minimal downtime. Because migration should preserve ecosystem APIs and jobs, the team must process petabyte-scale data with Hadoop-compatible tooling already used by the engineering team. The scenario calls for another native capability.
3. AWS DataSync fits situations that require teams to move large file or object datasets with managed scheduling, verification, and network optimization. Requirement: process petabyte-scale data with Hadoop-compatible tooling already used by the engineering team; key constraint: migration should preserve ecosystem APIs and jobs. That mismatch makes it a weaker choice.
4. Amazon Redshift Spectrum primarily lets Redshift query external data in S3 through external schemas while joining it with warehouse data. Limiting condition: migration should preserve ecosystem APIs and jobs; solution must process petabyte-scale data with Hadoop-compatible tooling already used by the engineering team. That leaves the key constraint unresolved.

 

Question 15

The team needs to replicate ongoing database changes into an analytics target after an initial full load, but the source is transactional and continuous change capture is required. Which option is best? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. Amazon EMR can help a team execute customizable big-data jobs that need ecosystem frameworks or cluster control. Decisive fact: the source is transactional and continuous change capture is required; requested action: replicate ongoing database changes into an analytics target after an initial full load. This does not deliver the requested outcome.
2. Amazon Redshift Spectrum is designed to combine Redshift warehouse queries with large external S3 datasets. The case hinges on the source is transactional and continuous change capture is required. The team must replicate ongoing database changes into an analytics target after an initial full load. Its primary role differs from this need.
3. AWS Database Migration Service primarily moves databases and can replicate ongoing changes with change data capture. Case: the source is transactional and continuous change capture is required; required action: replicate ongoing database changes into an analytics target after an initial full load. That is the direct functional match.
4. AWS DataSync supports teams that need to move large file or object datasets with managed scheduling, verification, and network optimization. Here, the source is transactional and continuous change capture is required; design must replicate ongoing database changes into an analytics target after an initial full load. That capability is not the required control.

 

Question 16

Given that the source is file storage and the migration must be managed and incremental, which approach most directly lets the team synchronize data from an NFS server into EFS while preserving supported metadata? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. AWS DataSync is designed to move large file or object datasets with managed scheduling, verification, and network optimization. Here, the source is file storage and the migration must be managed and incremental; design must synchronize data from an NFS server into EFS while preserving supported metadata. Its native behavior fits this need.
2. Amazon Redshift Spectrum fits situations that require teams to combine Redshift warehouse queries with large external S3 datasets. Limiting condition: the source is file storage and the migration must be managed and incremental; solution must synchronize data from an NFS server into EFS while preserving supported metadata. That leaves the key constraint unresolved.
3. AWS Database Migration Service moves databases and can replicate ongoing changes with change data capture. Given the source is file storage and the migration must be managed and incremental, the relevant choice must synchronize data from an NFS server into EFS while preserving supported metadata. This would address the wrong decision point.
4. Amazon EMR is intended to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Because the source is file storage and the migration must be managed and incremental, the team must synchronize data from an NFS server into EFS while preserving supported metadata. The scenario calls for another native capability.

 

Question 17

Which option best supports this requirement: expose a managed FTPS endpoint for a legacy business-to-business file exchange? Constraint: partners cannot change their transfer protocol yet. Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. Amazon AppFlow moves data between supported SaaS applications and AWS services with managed connectors. Because partners cannot change their transfer protocol yet, the team must expose a managed FTPS endpoint for a legacy business-to-business file exchange. This would address the wrong decision point.
2. AWS Transfer Family primarily provides managed SFTP, FTPS, FTP, and AS2 endpoints backed by AWS storage. The case hinges on partners cannot change their transfer protocol yet. The team must expose a managed FTPS endpoint for a legacy business-to-business file exchange. Its native behavior fits this need.
3. AWS Glue streaming ETL supports teams that need to apply stateful or scalable streaming transformations before writing curated outputs. Given partners cannot change their transfer protocol yet, the relevant choice must expose a managed FTPS endpoint for a legacy business-to-business file exchange. The scenario calls for another native capability.
4. S3 Transfer Acceleration addresses the need to accelerate client uploads to an S3 bucket over the public internet from distant locations. Decisive fact: partners cannot change their transfer protocol yet; requested action: expose a managed FTPS endpoint for a legacy business-to-business file exchange. This does not deliver the requested outcome.

 

Question 18

The business wants no custom extraction service. What should the team do to trigger a managed flow from a SaaS source into an AWS analytics destination? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. AWS Glue streaming ETL supports teams that need to apply stateful or scalable streaming transformations before writing curated outputs. Because the business wants no custom extraction service, the team must trigger a managed flow from a SaaS source into an AWS analytics destination. The scenario calls for another native capability.
2. AWS Transfer Family is designed to receive partner file transfers using familiar managed protocols. Given the business wants no custom extraction service, the relevant choice must trigger a managed flow from a SaaS source into an AWS analytics destination. This would address the wrong decision point.
3. S3 Transfer Acceleration is intended to accelerate client uploads to an S3 bucket over the public internet from distant locations. Requirement: trigger a managed flow from a SaaS source into an AWS analytics destination; key constraint: the business wants no custom extraction service. This does not deliver the requested outcome.
4. Amazon AppFlow can help a team ingest SaaS application data without custom API integration code. Limiting condition: the business wants no custom extraction service; solution must trigger a managed flow from a SaaS source into an AWS analytics destination. This aligns cleanly with the requirement.

 

Question 19

The team needs to reduce transfer time for international partners sending objects directly to S3, but the data path is client-to-S3 and latency over long internet routes is the bottleneck. 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)

 

D

Rationale

1. AWS Glue streaming ETL uses Glue streaming jobs to continuously transform supported streaming sources with Spark. Requirement: reduce transfer time for international partners sending objects directly to S3; key constraint: the data path is client-to-S3 and latency over long internet routes is the bottleneck. That mismatch makes it a weaker choice.
2. Amazon AppFlow is intended to ingest SaaS application data without custom API integration code. Decisive fact: the data path is client-to-S3 and latency over long internet routes is the bottleneck; requested action: reduce transfer time for international partners sending objects directly to S3. This does not deliver the requested outcome.
3. AWS Transfer Family addresses the need to receive partner file transfers using familiar managed protocols. Case: the data path is client-to-S3 and latency over long internet routes is the bottleneck; required action: reduce transfer time for international partners sending objects directly to S3. This solves a neighboring problem instead.
4. S3 Transfer Acceleration fits situations that require teams to accelerate client uploads to an S3 bucket over the public internet from distant locations. The case hinges on the data path is client-to-S3 and latency over long internet routes is the bottleneck. The team must reduce transfer time for international partners sending objects directly to S3. Its native behavior fits this need.

 

Question 20

Given that the team needs continuous transformations without operating an EMR cluster, which approach most directly lets the team run a managed Spark streaming pipeline with checkpoints over incoming events? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. S3 Transfer Acceleration supports teams that need to accelerate client uploads to an S3 bucket over the public internet from distant locations. Requirement: run a managed Spark streaming pipeline with checkpoints over incoming events; key constraint: the team needs continuous transformations without operating an EMR cluster. This does not deliver the requested outcome.
2. Amazon AppFlow primarily moves data between supported SaaS applications and AWS services with managed connectors. Given the team needs continuous transformations without operating an EMR cluster, the relevant choice must run a managed Spark streaming pipeline with checkpoints over incoming events. This would address the wrong decision point.
3. AWS Glue streaming ETL can help a team apply stateful or scalable streaming transformations before writing curated outputs. Here, the team needs continuous transformations without operating an EMR cluster; design must run a managed Spark streaming pipeline with checkpoints over incoming events. That capability directly satisfies the constraint.
4. AWS Transfer Family addresses the need to receive partner file transfers using familiar managed protocols. Limiting condition: the team needs continuous transformations without operating an EMR cluster; solution must run a managed Spark streaming pipeline with checkpoints over incoming events. This solves a neighboring problem instead.

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