Amazon AWS SAA-C03 Data Ingestion, Streaming, Transfer, Transformation, Analytics, and Data Lakes Practice Test 1

 

Topic 16 Practice Test 1 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

Given that the application needs programmatic consumers and ordered records per key, which approach most directly lets the team retain incoming events so multiple custom consumers can process and replay them? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. Kinesis Data Streams is intended to build a durable real-time stream that applications consume directly. The case hinges on the application needs programmatic consumers and ordered records per key. The team must retain incoming events so multiple custom consumers can process and replay them. That capability directly satisfies the constraint.
2. Kinesis partition key design is designed to preserve ordering for an entity while distributing unrelated entities across shards. Limiting condition: the application needs programmatic consumers and ordered records per key; solution must retain incoming events so multiple custom consumers can process and replay them. That leaves the key constraint unresolved.
3. Amazon Data Firehose fits situations that require teams to load streaming data into managed destinations without building a custom consumer fleet. Requirement: retain incoming events so multiple custom consumers can process and replay them; key constraint: the application needs programmatic consumers and ordered records per key. That mismatch makes it a weaker choice.
4. Kinesis enhanced fan-out can help a team isolate high-throughput stream consumers from shared read limits. Because the application needs programmatic consumers and ordered records per key, the team must retain incoming events so multiple custom consumers can process and replay them. The scenario calls for another native capability.

 

Question 2

Which option best supports this requirement: support several latency-sensitive consumers without having them compete for shard read throughput? Constraint: consumer contention is causing read throttling. 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 gives registered consumers dedicated read throughput and low-latency push delivery from shards. The case hinges on consumer contention is causing read throttling. The team must support several latency-sensitive consumers without having them compete for shard read throughput. Its native behavior fits this need.
2. Kinesis partition key design fits situations that require teams to preserve ordering for an entity while distributing unrelated entities across shards. Decisive fact: consumer contention is causing read throttling; requested action: support several latency-sensitive consumers without having them compete for shard read throughput. That mismatch makes it a weaker choice.
3. Kinesis Data Streams addresses the need to build a durable real-time stream that applications consume directly. Given consumer contention is causing read throttling, the relevant choice must support several latency-sensitive consumers without having them compete for shard read throughput. The scenario calls for another native capability.
4. Amazon Data Firehose primarily buffers streaming records and delivers them to supported destinations such as S3 with minimal consumer code. Case: consumer contention is causing read throttling; required action: support several latency-sensitive consumers without having them compete for shard read throughput. That leaves the key constraint unresolved.

 

Question 3

Ordering is required per device rather than globally. What should the team do to keep all events for one device in order while spreading different devices across the stream? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. Kinesis enhanced fan-out supports teams that need to isolate high-throughput stream consumers from shared read limits. The case hinges on ordering is required per device rather than globally. The team must keep all events for one device in order while spreading different devices across the stream. That capability is not the required control.
2. Amazon Data Firehose can help a team load streaming data into managed destinations without building a custom consumer fleet. Requirement: keep all events for one device in order while spreading different devices across the stream; key constraint: ordering is required per device rather than globally. This does not deliver the requested outcome.
3. Kinesis Data Streams is designed to build a durable real-time stream that applications consume directly. Here, ordering is required per device rather than globally; design must keep all events for one device in order while spreading different devices across the stream. Its primary role differs from this need.
4. Kinesis partition key design primarily maps related records to the same shard and therefore controls per-key ordering and distribution. Limiting condition: ordering is required per device rather than globally; solution must keep all events for one device in order while spreading different devices across the stream. That is the direct functional match.

 

Question 4

The team needs to deliver application telemetry continuously into S3 with managed buffering, but the team does not need custom replay consumers and wants low operational overhead. Which option is best? Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. Kinesis enhanced fan-out fits situations that require teams to isolate high-throughput stream consumers from shared read limits. Case: the team does not need custom replay consumers and wants low operational overhead; required action: deliver application telemetry continuously into S3 with managed buffering. That leaves the key constraint unresolved.
2. Amazon Data Firehose is intended to load streaming data into managed destinations without building a custom consumer fleet. Given the team does not need custom replay consumers and wants low operational overhead, the relevant choice must deliver application telemetry continuously into S3 with managed buffering. The documented behavior matches the requested result.
3. Kinesis partition key design is designed to preserve ordering for an entity while distributing unrelated entities across shards. The case hinges on the team does not need custom replay consumers and wants low operational overhead. The team must deliver application telemetry continuously into S3 with managed buffering. Its primary role differs from this need.
4. Kinesis Data Streams captures ordered streaming records in shards for custom consumers and replay within the stream retention period. Because the team does not need custom replay consumers and wants low operational overhead, the team must deliver application telemetry continuously into S3 with managed buffering. This would address the wrong decision point.

 

Question 5

Given that the team wants managed delivery plus a small per-record transform, which approach most directly lets the team normalize JSON fields before streaming records land in S3? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

3. AWS Glue crawler primarily inspects supported data stores and updates table metadata in the Glue Data Catalog. Here, the team wants managed delivery plus a small per-record transform; design must normalize JSON fields before streaming records land in S3. Its primary role differs from this need.
4. EventBridge Pipes supports teams that need to build point-to-point event integration with minimal custom glue code. Because the team wants managed delivery plus a small per-record transform, the team must normalize JSON fields before streaming records land in S3. The scenario calls for another native capability.
5. Amazon MSK provides managed Apache Kafka clusters and Kafka-compatible APIs for streaming workloads. Given the team wants managed delivery plus a small per-record transform, the relevant choice must normalize JSON fields before streaming records land in S3. This would address the wrong decision point.
6. Firehose Lambda transformation addresses the need to apply lightweight record transformation inside a managed delivery pipeline. Requirement: normalize JSON fields before streaming records land in S3; key constraint: the team wants managed delivery plus a small per-record transform. This is the precise capability required.

 

Question 6

Which option best supports this requirement: migrate producers and consumers that already depend on Kafka protocols and client libraries? Constraint: application compatibility with Apache Kafka is mandatory. 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 is intended to discover data schema and partitions automatically. Decisive fact: application compatibility with Apache Kafka is mandatory; requested action: migrate producers and consumers that already depend on Kafka protocols and client libraries. This does not deliver the requested outcome.
2. Firehose Lambda transformation supports teams that need to apply lightweight record transformation inside a managed delivery pipeline. Given application compatibility with Apache Kafka is mandatory, the relevant choice must migrate producers and consumers that already depend on Kafka protocols and client libraries. The scenario calls for another native capability.
3. Amazon MSK is designed to run Kafka-compatible streaming applications without self-managing brokers. Because application compatibility with Apache Kafka is mandatory, the team must migrate producers and consumers that already depend on Kafka protocols and client libraries. This addresses the decision at the right layer.
4. EventBridge Pipes can help a team build point-to-point event integration with minimal custom glue code. Case: application compatibility with Apache Kafka is mandatory; required action: migrate producers and consumers that already depend on Kafka protocols and client libraries. This solves a neighboring problem instead.

 

Question 7

The flow has one source, one target, and simple transformation needs. What should the team do to filter messages from a queue and invoke a target after a small enrichment step? 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 addresses the need to apply lightweight record transformation inside a managed delivery pipeline. Limiting condition: the flow has one source, one target, and simple transformation needs; solution must filter messages from a queue and invoke a target after a small enrichment step. This solves a neighboring problem instead.
2. EventBridge Pipes is intended to build point-to-point event integration with minimal custom glue code. Requirement: filter messages from a queue and invoke a target after a small enrichment step; key constraint: the flow has one source, one target, and simple transformation needs. This is the precise capability required.
3. AWS Glue crawler fits situations that require teams to discover data schema and partitions automatically. Here, the flow has one source, one target, and simple transformation needs; design must filter messages from a queue and invoke a target after a small enrichment step. Its primary role differs from this need.
4. Amazon MSK provides managed Apache Kafka clusters and Kafka-compatible APIs for streaming workloads. Decisive fact: the flow has one source, one target, and simple transformation needs; requested action: filter messages from a queue and invoke a target after a small enrichment step. That mismatch makes it a weaker choice.

 

Question 8

The team needs to catalog newly landed S3 files whose schema changes over time, but analysts need table metadata without manually defining every partition. Which option is best? Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. EventBridge Pipes can help a team build point-to-point event integration with minimal custom glue code. The case hinges on analysts need table metadata without manually defining every partition. The team must catalog newly landed S3 files whose schema changes over time. That capability is not the required control.
2. AWS Glue crawler supports teams that need to discover data schema and partitions automatically. Decisive fact: analysts need table metadata without manually defining every partition; requested action: catalog newly landed S3 files whose schema changes over time. This is the precise capability required.
3. Amazon MSK addresses the need to run Kafka-compatible streaming applications without self-managing brokers. Case: analysts need table metadata without manually defining every partition; required action: catalog newly landed S3 files whose schema changes over time. This solves a neighboring problem instead.
4. Firehose Lambda transformation primarily invokes Lambda to transform or enrich records before Firehose delivers them to a destination. Because analysts need table metadata without manually defining every partition, the team must catalog newly landed S3 files whose schema changes over time. This would address the wrong decision point.

 

Question 9

Given that the transformation is batch-oriented and cluster management should be avoided, which approach most directly lets the team convert raw S3 records to partitioned Parquet using a managed Spark job? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. AWS Glue ETL jobs can help a team perform scalable batch transformations without managing a persistent cluster. Here, the transformation is batch-oriented and cluster management should be avoided; design must convert raw S3 records to partitioned Parquet using a managed Spark job. That capability directly satisfies the constraint.
2. Amazon Athena is intended to perform ad hoc SQL analysis without provisioning a data warehouse cluster. Limiting condition: the transformation is batch-oriented and cluster management should be avoided; solution must convert raw S3 records to partitioned Parquet using a managed Spark job. This solves a neighboring problem instead.
3. AWS Lake Formation fits situations that require teams to govern access to data-lake tables and columns centrally. Given the transformation is batch-oriented and cluster management should be avoided, the relevant choice must convert raw S3 records to partitioned Parquet using a managed Spark job. This would address the wrong decision point.
4. AWS Glue Data Catalog is designed to share data-lake table definitions across engines. Decisive fact: the transformation is batch-oriented and cluster management should be avoided; requested action: convert raw S3 records to partitioned Parquet using a managed Spark job. That mismatch makes it a weaker choice.

 

Question 10

Which option best supports this requirement: let Athena and other analytics services use the same table metadata for S3 data? Constraint: the problem is a reusable central metastore. Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. AWS Glue Data Catalog addresses the need to share data-lake table definitions across engines. The case hinges on the problem is a reusable central metastore. The team must let Athena and other analytics services use the same table metadata for S3 data. That capability directly satisfies the constraint.
2. AWS Lake Formation centralizes fine-grained data lake permissions and governance over cataloged data. Limiting condition: the problem is a reusable central metastore; solution must let Athena and other analytics services use the same table metadata for S3 data. That leaves the key constraint unresolved.
3. AWS Glue ETL jobs fits situations that require teams to perform scalable batch transformations without managing a persistent cluster. Because the problem is a reusable central metastore, the team must let Athena and other analytics services use the same table metadata for S3 data. This would address the wrong decision point.
4. Amazon Athena primarily runs serverless SQL queries directly against data in S3 using catalog metadata. Requirement: let Athena and other analytics services use the same table metadata for S3 data; key constraint: the problem is a reusable central metastore. That mismatch makes it a weaker choice.

 

Question 11

Fine-grained lake governance is the requirement. What should the team do to grant analysts access to selected columns of cataloged lake tables without broad S3 permissions? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. Amazon Athena primarily runs serverless SQL queries directly against data in S3 using catalog metadata. Decisive fact: fine-grained lake governance is the requirement; requested action: grant analysts access to selected columns of cataloged lake tables without broad S3 permissions. That mismatch makes it a weaker choice.
2. AWS Glue Data Catalog is designed to share data-lake table definitions across engines. Case: fine-grained lake governance is the requirement; required action: grant analysts access to selected columns of cataloged lake tables without broad S3 permissions. That leaves the key constraint unresolved.
3. AWS Lake Formation supports teams that need to govern access to data-lake tables and columns centrally. Limiting condition: fine-grained lake governance is the requirement; solution must grant analysts access to selected columns of cataloged lake tables without broad S3 permissions. This aligns cleanly with the requirement.
4. AWS Glue ETL jobs can help a team perform scalable batch transformations without managing a persistent cluster. Given fine-grained lake governance is the requirement, the relevant choice must grant analysts access to selected columns of cataloged lake tables without broad S3 permissions. The scenario calls for another native capability.

 

Question 12

The team needs to query Parquet files in S3 occasionally without loading them into a database first, but usage is intermittent and analysts want SQL. Which option is best? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. AWS Glue Data Catalog is intended to share data-lake table definitions across engines. The case hinges on usage is intermittent and analysts want SQL. The team must query Parquet files in S3 occasionally without loading them into a database first. That capability is not the required control.
2. AWS Glue ETL jobs is designed to perform scalable batch transformations without managing a persistent cluster. Limiting condition: usage is intermittent and analysts want SQL; solution must query Parquet files in S3 occasionally without loading them into a database first. That leaves the key constraint unresolved.
3. Amazon Athena fits situations that require teams to perform ad hoc SQL analysis without provisioning a data warehouse cluster. Requirement: query Parquet files in S3 occasionally without loading them into a database first; key constraint: usage is intermittent and analysts want SQL. That makes this the strongest fit.
4. AWS Lake Formation centralizes fine-grained data lake permissions and governance over cataloged data. Here, usage is intermittent and analysts want SQL; design must query Parquet files in S3 occasionally without loading them into a database first. Its primary role differs from this need.

 

Question 13

Given that moving the full archive into Redshift is unnecessary, which approach most directly lets the team join curated warehouse tables with historical data that remains in S3? Choose ONE.

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

Correct Answer(s)

 

D

Rationale

3. AWS Database Migration Service supports teams that need to migrate or continuously replicate supported database data with minimal downtime. Here, moving the full archive into Redshift is unnecessary; design must join curated warehouse tables with historical data that remains in S3. That capability is not the required control.
4. Amazon EMR addresses the need to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Given moving the full archive into Redshift is unnecessary, the relevant choice must join curated warehouse tables with historical data that remains in S3. The scenario calls for another native capability.
5. AWS DataSync primarily accelerates and automates online data transfer between supported storage systems and AWS storage services. Case: moving the full archive into Redshift is unnecessary; required action: join curated warehouse tables with historical data that remains in S3. That leaves the key constraint unresolved.
6. Amazon Redshift Spectrum lets Redshift query external data in S3 through external schemas while joining it with warehouse data. The case hinges on moving the full archive into Redshift is unnecessary. The team must join curated warehouse tables with historical data that remains in S3. Its native behavior fits this need.

 

Question 14

Which option best supports this requirement: run an existing Spark application with custom dependencies and tuning across a large data set? Constraint: the team needs more framework flexibility than a simple managed ETL job. Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. Amazon Redshift Spectrum is designed to combine Redshift warehouse queries with large external S3 datasets. Here, the team needs more framework flexibility than a simple managed ETL job; design must run an existing Spark application with custom dependencies and tuning across a large data set. Its primary role differs from this need.
2. AWS Database Migration Service is intended to migrate or continuously replicate supported database data with minimal downtime. Because the team needs more framework flexibility than a simple managed ETL job, the team must run an existing Spark application with custom dependencies and tuning across a large data set. The scenario calls for another native capability.
3. AWS DataSync can help a team move large file or object datasets with managed scheduling, verification, and network optimization. Requirement: run an existing Spark application with custom dependencies and tuning across a large data set; key constraint: the team needs more framework flexibility than a simple managed ETL job. This does not deliver the requested outcome.
4. Amazon EMR supports teams that need to execute customizable big-data jobs that need ecosystem frameworks or cluster control. The case hinges on the team needs more framework flexibility than a simple managed ETL job. The team must run an existing Spark application with custom dependencies and tuning across a large data set. That capability directly satisfies the constraint.

 

Question 15

Downtime must be minimized and the source remains active during migration. What should the team do to migrate a relational database while continuing to capture source changes until cutover? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. Amazon EMR addresses the need to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Decisive fact: downtime must be minimized and the source remains active during migration; requested action: migrate a relational database while continuing to capture source changes until cutover. This does not deliver the requested outcome.
2. AWS DataSync accelerates and automates online data transfer between supported storage systems and AWS storage services. Because downtime must be minimized and the source remains active during migration, the team must migrate a relational database while continuing to capture source changes until cutover. This would address the wrong decision point.
3. AWS Database Migration Service fits situations that require teams to migrate or continuously replicate supported database data with minimal downtime. Case: downtime must be minimized and the source remains active during migration; required action: migrate a relational database while continuing to capture source changes until cutover. That is the direct functional match.
4. Amazon Redshift Spectrum is intended to combine Redshift warehouse queries with large external S3 datasets. Given downtime must be minimized and the source remains active during migration, the relevant choice must migrate a relational database while continuing to capture source changes until cutover. The scenario calls for another native capability.

 

Question 16

The team needs to copy millions of on-premises files to S3 repeatedly over an existing network link, but the transfer should be scheduled and verified without custom rsync orchestration. Which option is best? Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. Amazon EMR addresses the need to execute customizable big-data jobs that need ecosystem frameworks or cluster control. Requirement: copy millions of on-premises files to S3 repeatedly over an existing network link; key constraint: the transfer should be scheduled and verified without custom rsync orchestration. This does not deliver the requested outcome.
2. AWS DataSync can help a team move large file or object datasets with managed scheduling, verification, and network optimization. Limiting condition: the transfer should be scheduled and verified without custom rsync orchestration; solution must copy millions of on-premises files to S3 repeatedly over an existing network link. This aligns cleanly with the requirement.
3. AWS Database Migration Service primarily moves databases and can replicate ongoing changes with change data capture. Here, the transfer should be scheduled and verified without custom rsync orchestration; design must copy millions of on-premises files to S3 repeatedly over an existing network link. Its primary role differs from this need.
4. Amazon Redshift Spectrum supports teams that need to combine Redshift warehouse queries with large external S3 datasets. Because the transfer should be scheduled and verified without custom rsync orchestration, the team must copy millions of on-premises files to S3 repeatedly over an existing network link. The scenario calls for another native capability.

 

Question 17

Given that the interface protocol must remain SFTP, which approach most directly lets the team let external partners upload files over SFTP directly into S3 without managing SFTP servers? Choose ONE.

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

Correct Answer(s)

 

C

Rationale

1. AWS Glue streaming ETL fits situations that require teams to apply stateful or scalable streaming transformations before writing curated outputs. The case hinges on the interface protocol must remain SFTP. The team must let external partners upload files over SFTP directly into S3 without managing SFTP servers. Its primary role differs from this need.
2. Amazon AppFlow is designed to ingest SaaS application data without custom API integration code. Because the interface protocol must remain SFTP, the team must let external partners upload files over SFTP directly into S3 without managing SFTP servers. This would address the wrong decision point.
3. AWS Transfer Family can help a team receive partner file transfers using familiar managed protocols. Case: the interface protocol must remain SFTP; required action: let external partners upload files over SFTP directly into S3 without managing SFTP servers. This aligns cleanly with the requirement.
4. S3 Transfer Acceleration is intended to accelerate client uploads to an S3 bucket over the public internet from distant locations. Decisive fact: the interface protocol must remain SFTP; requested action: let external partners upload files over SFTP directly into S3 without managing SFTP servers. This does not deliver the requested outcome.

 

Question 18

Which option best supports this requirement: transfer Salesforce records into S3 on a schedule without maintaining connector code? Constraint: the source is a supported SaaS application. Choose ONE.

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

Correct Answer(s)

 

D

Rationale

1. S3 Transfer Acceleration addresses the need to accelerate client uploads to an S3 bucket over the public internet from distant locations. Limiting condition: the source is a supported SaaS application; solution must transfer Salesforce records into S3 on a schedule without maintaining connector code. This solves a neighboring problem instead.
2. AWS Glue streaming ETL fits situations that require teams to apply stateful or scalable streaming transformations before writing curated outputs. Here, the source is a supported SaaS application; design must transfer Salesforce records into S3 on a schedule without maintaining connector code. Its primary role differs from this need.
3. AWS Transfer Family provides managed SFTP, FTPS, FTP, and AS2 endpoints backed by AWS storage. Decisive fact: the source is a supported SaaS application; requested action: transfer Salesforce records into S3 on a schedule without maintaining connector code. That mismatch makes it a weaker choice.
4. Amazon AppFlow primarily moves data between supported SaaS applications and AWS services with managed connectors. Given the source is a supported SaaS application, the relevant choice must transfer Salesforce records into S3 on a schedule without maintaining connector code. This addresses the decision at the right layer.

 

Question 19

Clients are far from the bucket Region and use the internet. What should the team do to improve large uploads from globally distributed users to one S3 bucket without deploying regional upload servers? Choose ONE.

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

Correct Answer(s)

 

B

Rationale

1. AWS Glue streaming ETL supports teams that need to apply stateful or scalable streaming transformations before writing curated outputs. Decisive fact: clients are far from the bucket Region and use the internet; requested action: improve large uploads from globally distributed users to one S3 bucket without deploying regional upload servers. This does not deliver the requested outcome.
2. S3 Transfer Acceleration can help a team accelerate client uploads to an S3 bucket over the public internet from distant locations. The case hinges on clients are far from the bucket Region and use the internet. The team must improve large uploads from globally distributed users to one S3 bucket without deploying regional upload servers. That capability directly satisfies the constraint.
3. Amazon AppFlow is designed to ingest SaaS application data without custom API integration code. Requirement: improve large uploads from globally distributed users to one S3 bucket without deploying regional upload servers; key constraint: clients are far from the bucket Region and use the internet. That mismatch makes it a weaker choice.
4. AWS Transfer Family primarily provides managed SFTP, FTPS, FTP, and AS2 endpoints backed by AWS storage. Because clients are far from the bucket Region and use the internet, the team must improve large uploads from globally distributed users to one S3 bucket without deploying regional upload servers. This would address the wrong decision point.

 

Question 20

The team needs to continuously transform Kinesis records with Spark logic before writing them to a lake, but the transform is more substantial than a small Firehose Lambda function. Which option is best? Choose ONE.

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

Correct Answer(s)

 

A

Rationale

1. AWS Glue streaming ETL is designed to apply stateful or scalable streaming transformations before writing curated outputs. Decisive fact: the transform is more substantial than a small Firehose Lambda function; requested action: continuously transform Kinesis records with Spark logic before writing them to a lake. That makes this the strongest fit.
2. AWS Transfer Family is intended to receive partner file transfers using familiar managed protocols. Limiting condition: the transform is more substantial than a small Firehose Lambda function; solution must continuously transform Kinesis records with Spark logic before writing them to a lake. This solves a neighboring problem instead.
3. Amazon AppFlow moves data between supported SaaS applications and AWS services with managed connectors. Case: the transform is more substantial than a small Firehose Lambda function; required action: continuously transform Kinesis records with Spark logic before writing them to a lake. That leaves the key constraint unresolved.
4. S3 Transfer Acceleration fits situations that require teams to accelerate client uploads to an S3 bucket over the public internet from distant locations. Given the transform is more substantial than a small Firehose Lambda function, the relevant choice must continuously transform Kinesis records with Spark logic before writing them to a lake. This would address the wrong decision point.

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