Tüm alıştırma soruları

1462 soru

Soru 221Soru

A financial analytics firm runs a real-time risk simulation workload on a fleet of Amazon EC2 Linux instances deployed in a single Availability Zone. The application requires a shared file storage solution that can be mounted simultaneously by all instances using the NFS protocol. The workload performs highly intensive metadata operations and requires consistent sub-millisecond latencies of less than 1 ms1 \text{ ms}, a minimum throughput of 2,500 MB/s2,500 \text{ MB/s}, and up to 100,000 IOPS100,000 \text{ IOPS}. Which storage solution will meet these requirements with the lowest latency?

Cevabı ve açıklamayı göster

Cevap: Amazon FSx for OpenZFS file systems

Cevap

Amazon FSx for OpenZFS file systems
The correct file system choice provides fully managed shared file storage using the NFS protocol with sub-millisecond latencies down to 0.10.2 ms0.1\text{--}0.2 \text{ ms}. This solution supports up to 350,000350,000 IOPS and 10 GB/s10 \text{ GB/s} throughput, making it ideal for low-latency, high-frequency database and financial analytics workloads in a single Availability Zone.

Adım Adım Çözüm

1
Analyze the access requirement and protocol.
The storage must be shared, accessed by multiple Linux EC2 instances concurrently, and support the NFS protocol.
This rules out standard EBS volumes (which are single-instance only) and points towards shared file systems (EFS or FSx).
2
Evaluate latency and performance requirements.
The file system must sustain sub-millisecond latencies (less than 1 ms1 \text{ ms}), up to 100,000 IOPS100,000 \text{ IOPS}, and 2,500 MB/s2,500 \text{ MB/s} throughput.
Amazon EFS has a standard metadata latency profile of 13 ms1\text{--}3 \text{ ms}, which is too high for this workload.
3
Compare FSx file system types for NFS low-latency workloads.
Amazon FSx for OpenZFS provides sub-millisecond latencies (around 0.10.2 ms0.1\text{--}0.2 \text{ ms}) over standard NFS and meets all IOPS and throughput constraints.
FSx for Lustre is a parallel file system that requires a Lustre client and is optimized for scale-out parallel processing rather than ultra-low latency NFS metadata operations.

Anahtar Kavram

Low-latency shared file storage optimization using Amazon FSx for OpenZFS
Soru 222Soru

A biotech company is running a distributed genomic analysis application on a cluster of 3232 Amazon EC2 instances. The application requires concurrent, shared access to a dataset that demands up to 80 GB/s80\text{ GB/s} read throughput and sub-millisecond latencies. The raw input data is stored in an Amazon S3 bucket, and the output files must be stored back in the same S3 bucket after the batch run completes. The storage solution must minimize both cost and operational overhead. Which storage configuration meets these requirements?

Cevabı ve açıklamayı göster

Cevap: Create an Amazon FSx for Lustre file system using a scratch deployment, link it to the Amazon S3 bucket as a data repository, and mount it on the instances.

Cevap

Create an Amazon FSx for Lustre file system using a scratch deployment, link it to the Amazon S3 bucket as a data repository, and mount it on the instances.
The correct option is to deploy an Amazon FSx for Lustre scratch file system integrated with the Amazon S3 bucket. FSx for Lustre is optimized for parallel workloads like high-performance computing (HPC) and genomic analysis. It easily scales to tens of gigabytes per second of throughput and millions of IOPS with sub-millisecond latencies. The scratch deployment option is highly cost-effective because it does not replicate data across availability zones, which is appropriate since the source and output data are stored durably in Amazon S3. The integration with S3 allows automated import and export of data with minimal operational effort.

Adım Adım Çözüm

1
Analyze the scaling and instance count requirements.
The workload requires concurrent access from 3232 EC2 instances, which rules out EBS Multi-Attach since it has a maximum limit of 1616 instances.
Eliminate options that cannot scale to the required number of concurrent writers/readers.
2
Evaluate the performance requirements.
The workload requires 80 GB/s80\text{ GB/s} of throughput and sub-millisecond latencies. Amazon EFS does not meet the 80 GB/s80\text{ GB/s} read throughput requirements economically or within standard limits.
Select a storage service designed for massive parallel file system performance.
3
Evaluate cost and operational complexity for the remaining solutions.
Amazon FSx for Lustre scratch deployment is specifically designed for temporary processing of data synced with Amazon S3. It provides high performance at a lower cost than persistent deployments by omitting replication, since the master copy of the data is safe in Amazon S3.
Determine the most cost-effective and operationally simple deployment model.

Anahtar Kavram

Selecting high-performance, cost-effective, and scalable shared storage for parallel processing workloads linked with Amazon S3.
Tahmini Süre:3m 0s
Soru 223Soru

A genetics research laboratory runs a high-performance gene sequencing application on Amazon EC2 instances across multiple Availability Zones in a primary AWS Region. The application requires shared, concurrent access to a common file system that can store petabytes of genomic data with consistent sub-millisecond latencies. The laboratory needs to establish a disaster recovery (DR) architecture in a secondary AWS Region. The DR plan requires a Recovery Point Objective (RPO) of 15 minutes and a Recovery Time Objective (RTO) of less than 15 minutes for the shared storage layer. Which storage architecture should a solutions architect design to meet these requirements with the lowest operational overhead?

Cevabı ve açıklamayı göster

Cevap: Deploy Amazon Elastic File System (Amazon EFS) in the primary Region using the Regional storage class. Enable EFS Replication to automatically replicate data to the secondary AWS Region. In the event of a disaster, mount the replicated EFS file system on EC2 instances in the secondary Region.

Cevap

Deploy Amazon Elastic File System (Amazon EFS) in the primary Region using the Regional storage class and enable native EFS Replication to the secondary Region.
The correct option is the one proposing Amazon Elastic File System (Amazon EFS) with EFS Replication. EFS provides shared, concurrent access suitable for high-performance applications, and native cross-Region replication replicates changes to a destination file system within minutes, satisfying both the 15-minute RPO and the sub-15-minute RTO requirements with minimal operational overhead.

Adım Adım Çözüm

1
Identify the storage access patterns and performance requirements.
The application requires shared, concurrent access to a common file system with sub-millisecond latencies, pointing to Amazon EFS rather than S3 (object storage) or EBS (block storage attached to single instances).
Choosing the correct storage service is the first step in designing a resilient architecture.
2
Evaluate the RPO and RTO constraints.
The RPO is 15 minutes and the RTO is less than 15 minutes. This requires continuous replication rather than daily batch backups.
Low recovery objectives rule out backup-and-restore or cold-tier options like standard S3 Glacier.
3
Select the replication and failover mechanism.
Native EFS Replication automatically and continuously replicates data to another AWS Region. The destination file system is active and mountable immediately, yielding an RTO of minutes and an RPO of minutes.
EFS Replication provides a fully managed, low-operational-overhead disaster recovery solution.

Anahtar Kavram

Amazon EFS Replication provides native cross-Region replication for shared file systems, offering low RPO (minutes) and RTO (minutes) for disaster recovery without operational complexity.
Soru 224Soru

A company wants to design a serverless data ingestion pipeline to collect real-time application logs. The pipeline must transform the log format from JSON to Apache Parquet and store the transformed data in Amazon S3 for long-term analytics. The solution must minimize operational overhead. Which TWO actions should the solutions architect take to meet these requirements? (Select TWO.)

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Cevap: Create an Amazon Data Firehose stream to ingest the logs and enable data format conversion using the AWS Glue Data Catalog.; Configure the Amazon Data Firehose stream to deliver the transformed logs directly to the destination Amazon S3 bucket.

Cevap

The correct solution involves creating an Amazon Data Firehose stream to ingest logs and perform format conversion using the AWS Glue Data Catalog, and configuring the stream to deliver the transformed logs directly to the target Amazon S3 bucket.
To design a serverless ingestion pipeline with minimum operational overhead, Amazon Data Firehose is the optimal service because it is fully managed, auto-scales, and provides built-in integration with AWS Glue Data Catalog to convert JSON data to Apache Parquet. Once transformed, it can natively write the files directly to an Amazon S3 bucket without requiring any custom consumer applications or compute resources.

Adım Adım Çözüm

1
Select a serverless ingestion service that supports native data transformation.
Amazon Data Firehose is selected because it is fully managed and supports inline conversion to Apache Parquet.
This minimizes operational overhead by avoiding custom transformation code.
2
Configure the format conversion schema.
Define the schema in the AWS Glue Data Catalog and reference it in the Amazon Data Firehose settings.
AWS Glue provides the metadata schema that Firehose needs to convert JSON records to Parquet.
3
Set up the destination target.
Configure Amazon Data Firehose to write the output directly to the destination Amazon S3 bucket.
This establishes a direct, serverless delivery path to S3 without managing infrastructure.

Anahtar Kavram

Serverless real-time data ingestion and inline transformation using Amazon Data Firehose and AWS Glue.
Tahmini Süre:1m 0s
Soru 225Soru

A logistics company is designing an ingestion pipeline to track high-frequency GPS telemetry from a fleet of 100,000100,000 delivery vehicles. Each vehicle transmits a 2 KB2\text{ KB} JSON payload every 10 seconds10\text{ seconds} containing status updates that must be processed in strict chronological order per vehicle to calculate accurate transit times. The data must be converted to Apache Parquet format and stored in Amazon S3 within 5 minutes5\text{ minutes} of generation. During peak morning hours, a grid-lock event can cause all vehicles to transmit data within a 2 second2\text{ second} window, resulting in transient spikes of up to 50,00050,000 requests per second. Which architecture meets these requirements most performantly and reliably?

Cevabı ve açıklamayı göster

Cevap: Configure an Amazon Kinesis Data Stream in On-Demand mode, using the vehicle ID as the partition key. Configure an Amazon Data Firehose delivery stream to consume from the Kinesis stream, transform the JSON payloads to Apache Parquet format using an AWS Glue table, and deliver the data to Amazon S3 with a buffer interval of 300 seconds300\text{ seconds}.

Cevap

Configure an Amazon Kinesis Data Stream in On-Demand mode with the vehicle ID as the partition key, and use Amazon Data Firehose to transform the data to Parquet via AWS Glue and deliver it to Amazon S3 with a 5-minute buffer interval.
The correct architecture uses Amazon Kinesis Data Streams in On-Demand mode, which scales dynamically to support up to 200 MB/sec200\text{ MB/sec} of write throughput, easily absorbing the 100 MB/sec100\text{ MB/sec} peak spike. Partitioning by the vehicle ID ensures that all status updates for a single vehicle are directed to the same shard, preserving their chronological sequence. Amazon Data Firehose then consumes from the stream, transforms the JSON payload to Parquet format using metadata defined in AWS Glue, and writes the output files to Amazon S3 within the required 5-minute buffering window (300 seconds300\text{ seconds}). This provides a highly performant, fully managed, and cost-effective ingestion pipeline.

Adım Adım Çözüm

1
Calculate the average and peak throughput requirements for the ingestion layer.
Average throughput is 10,00010,000 records/sec (20 MB/sec20\text{ MB/sec}), and peak throughput is 50,00050,000 records/sec (100 MB/sec100\text{ MB/sec}).
This determines the minimum shard or partition capacity needed to prevent ingestion bottlenecks.
2
Select an ingestion service that supports both strict ordering per device and the calculated throughput scaling.
Kinesis Data Streams On-Demand is selected, using the vehicle ID as the partition key.
On-Demand mode automatically scales to accommodate the 100 MB/sec100\text{ MB/sec} peak, while the partition key guarantees that all updates for a specific vehicle are routed to the same shard and processed sequentially.
3
Design a serverless transformation and delivery mechanism to output Parquet files to Amazon S3.
Amazon Data Firehose consumes from the Kinesis stream, converts JSON to Parquet using AWS Glue Schema Registry, and writes to Amazon S3.
Firehose handles the buffering, format conversion, and S3 delivery serverlessly without needing to manage EC2 instances or custom consumer application scaling.

Anahtar Kavram

High-throughput real-time data ingestion with ordering guarantees and serverless format transformation.
Tahmini Süre:2m 30s
Soru 226Soru

A financial firm needs to back up its daily transaction logs to AWS. The backups must be stored in a highly durable manner across multiple Availability Zones, and they must be immediately accessible for the first 30 days. For compliance, these backups must be replicated to a secondary AWS Region. After 30 days, the backups are rarely accessed but must be retained for 7 years. Which TWO actions should a solutions architect combine to meet these requirements?

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Cevap: Store the daily backups in an Amazon S3 bucket, and configure Amazon S3 Cross-Region Replication (CRR) to copy them to a bucket in a secondary AWS Region.; Configure an Amazon S3 Lifecycle policy to transition the backup objects in both buckets to Amazon S3 Glacier Flexible Retrieval after 30 days.

Cevap

Storing the daily backups in an Amazon S3 bucket with Cross-Region Replication (CRR) enabled, and configuring an S3 Lifecycle policy to transition the backup objects to Amazon S3 Glacier Flexible Retrieval after 30 days.
Storing the daily backups in Amazon S3 ensures the data is replicated across multiple Availability Zones, providing high resilience and immediate retrieval. Cross-Region Replication (CRR) automates the replication of these backups to a secondary AWS Region for disaster recovery and compliance. Finally, an Amazon S3 Lifecycle policy configured to transition the backups to Amazon S3 Glacier Flexible Retrieval after 30 days minimizes long-term storage costs while complying with the 7-year retention period, as the data is rarely accessed after the initial month.

Adım Adım Çözüm

1
Store the data in Amazon S3 Standard to ensure high availability and durability across multiple Availability Zones, meeting the requirement of immediate accessibility for the first 30 days.
The backups are durable and immediately accessible.
S3 Standard provides low-latency, high-throughput access to objects with automatic replication across multiple AZs.
2
Configure Amazon S3 Cross-Region Replication (CRR) to copy new objects automatically to a destination bucket in a secondary AWS Region.
A copy of the backups is safely stored in a secondary AWS Region for compliance.
S3 CRR is the standard method for replicating S3 objects across different AWS Regions automatically.
3
Apply an Amazon S3 Lifecycle policy to transition objects from S3 Standard to S3 Glacier Flexible Retrieval after 30 days.
Data older than 30 days is moved to lower-cost archive storage, meeting the 7-year retention requirement cost-effectively.
S3 Glacier Flexible Retrieval is ideal for archiving data that is rarely accessed and can tolerate retrieval times of minutes to hours.

Anahtar Kavram

Leveraging Amazon S3 features such as Cross-Region Replication (CRR) and Lifecycle policies to build resilient, highly available, and cost-effective storage architectures.
Soru 227Soru

A gaming company is launching a new multiplayer game and needs to design a high-performing data ingestion and transformation pipeline for player session telemetry. The pipeline must ingest real-time telemetry events and process them in strict chronological order per player session to update live leaderboards with sub-second latency. Concurrently, the same raw event data must be converted into Apache Parquet format and stored in Amazon S3 for long-term historical analytics. Which combination of AWS services should a solutions architect select to meet these requirements? (Select two.)

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Cevabı ve açıklamayı göster

Cevap: Ingest the telemetry using Amazon Kinesis Data Streams, specifying the player session ID as the partition key, and consume the stream using a custom application to update the leaderboards.; Use Amazon Data Firehose to read from the Kinesis Data Stream, use AWS Glue to transform the telemetry data to Parquet format, and write the output files to Amazon S3.

Cevap

To ingest and transform player session telemetry while maintaining strict chronological ordering per session with sub-second latency, use Amazon Kinesis Data Streams partitioned by the player session ID for the real-time path, and consume from the same stream using Amazon Data Firehose with AWS Glue to convert the telemetry to Parquet format for Amazon S3 storage.
The correct answer consists of using Amazon Kinesis Data Streams for real-time ingestion, combined with Amazon Data Firehose and AWS Glue for near-real-time transformation and storage. Ingesting telemetry into Amazon Kinesis Data Streams with the player session ID as the partition key guarantees that all records for a given session are routed to the same shard and processed in strict order. A custom consumer application can read this stream to update the leaderboard with sub-second latency. Concurrently, Amazon Data Firehose can consume the same Kinesis stream, invoke AWS Glue for schema-based transformation to Parquet format, and deliver the structured files to Amazon S3, meeting the analytics requirement without administrative overhead.

Adım Adım Çözüm

1
Analyze ingestion and ordering requirements
Identify that player telemetry requires sub-second processing and strict chronological ordering per session ID.
Guaranteed ordering per session is necessary to prevent out-of-order state updates on leaderboards.
2
Evaluate real-time ingestion options for ordered data
Select Amazon Kinesis Data Streams with the session ID as the partition key.
Kinesis Data Streams routes records with the same partition key to the same shard, preserving the ingestion sequence. Custom consumers can then process shards with sub-second latency.
3
Evaluate transformation and storage requirements
Configure Amazon Data Firehose to read from the Kinesis stream, use AWS Glue for Parquet conversion, and write to Amazon S3.
This provides a serverless, zero-maintenance mechanism to batch, transform, and store historical data in a high-performing columnar format (Parquet).

Anahtar Kavram

Decoupling real-time stream ingestion and near-real-time batch transformation using Amazon Kinesis Data Streams and Amazon Data Firehose to meet distinct performance, ordering, and storage format constraints.
Tahmini Süre:2m 0s
Soru 228Soru

A company is launching a new corporate training platform. The application uses an Amazon RDS for MariaDB DB instance to store employee progress data. The platform requires high availability with automatic failover to handle Availability Zone outages without manual intervention. Which two configurations or solutions should a solutions architect implement to meet these requirements? (Select TWO.)

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Cevap: Modify the DB instance to use a Multi-AZ deployment configuration.; Configure the application to connect to the database using the database instance DNS endpoint.

Cevap

To achieve high availability with automatic failover, the solutions architect should modify the database to use a Multi-AZ deployment and configure the application to connect using the database instance DNS endpoint.
Enabling a Multi-AZ deployment ensures Amazon RDS maintains a synchronous standby replica in a different Availability Zone. During an outage, RDS automatically performs a failover. Using the DNS endpoint in the application configuration is critical because AWS updates this endpoint's DNS record during failover to point to the standby replica, allowing the application to reconnect automatically.

Adım Adım Çözüm

1
Enable Multi-AZ deployment for the database instance.
Amazon RDS creates a synchronous standby replica in a different Availability Zone.
This provides physical redundancy across Availability Zones and enables automatic failover capabilities.
2
Point the application's connection string to the RDS DNS endpoint.
The application connects using a domain name rather than a hardcoded IP address.
During failover, AWS automatically updates the DNS CNAME record of the endpoint to resolve to the new primary database instance.

Anahtar Kavram

Amazon RDS Multi-AZ deployments provide high availability and automatic failover by maintaining a synchronous standby replica in a different Availability Zone and updating the DNS endpoint during failover.
Tahmini Süre:1m 0s
Soru 229Soru

An analytics platform uses a fleet of processing nodes on Amazon EC2 in private subnets across two Availability Zones in `us-west-2` to ingest raw telemetry logs. These nodes need to write approximately 200 TB200\text{ TB} of structured data monthly to an Amazon DynamoDB table in the same Region. They also need to periodically replicate processed summaries (80 TB80\text{ TB} monthly) to an on-premises database via an existing 10 Gbps10\text{ Gbps} AWS Direct Connect connection. Which combination of architectural decisions will achieve the most cost-effective data transfer routing? (Select TWO.)

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Cevap: Configure a Gateway VPC Endpoint for Amazon DynamoDB in the VPC and associate it with the private subnet route tables.; Associate the Direct Connect Gateway directly with a Virtual Private Gateway (VGW) attached to the VPC, and route the on-premises traffic over a Private Virtual Interface (Private VIF).

Cevap

Configure a Gateway VPC Endpoint for Amazon DynamoDB in the VPC and associate it with the private subnet route tables; and associate the Direct Connect Gateway directly with a Virtual Private Gateway (VGW) attached to the VPC, routing the on-premises traffic over a Private Virtual Interface (Private VIF).
The correct architecture uses a Gateway VPC Endpoint for Amazon DynamoDB because it does not charge hourly or data processing fees, unlike NAT Gateways or Interface VPC Endpoints. Furthermore, associating the Direct Connect Gateway directly with the Virtual Private Gateway (VGW) and routing traffic over a Private Virtual Interface (Private VIF) avoids AWS Transit Gateway data processing fees ($0.02 per GB), saving substantial costs for the replication workload.

Adım Adım Çözüm

1
Analyze the data transfer requirements for DynamoDB.
Identify that 200 TB200\text{ TB} of data needs to be written from private EC2 instances to DynamoDB in the same Region.
To determine the most cost-effective path to DynamoDB without routing traffic through a NAT Gateway.
2
Compare Gateway VPC Endpoints and Interface VPC Endpoints for DynamoDB.
Gateway VPC Endpoints are free of charge, whereas NAT Gateways charge 0.045/GB0.045\text{/GB} and Interface Endpoints charge 0.01/GB0.01\text{/GB} plus hourly fees.
To choose the routing mechanism that avoids all processing charges for the 200 TB200\text{ TB} DynamoDB traffic.
3
Analyze the data replication path to the on-premises database via the Direct Connect connection.
Identify that 80 TB80\text{ TB} of data will be transferred out monthly.
To choose between Transit Gateway and a Direct Connect Gateway linked to a Virtual Private Gateway (VGW).
4
Compare Transit Gateway with direct VGW attachment for Direct Connect routing.
Transit Gateway introduces a processing fee of 0.02/GB0.02\text{/GB} (1,600 for 80 TB1,600\text{ for } 80\text{ TB}), while associating the Direct Connect Gateway directly with the VPC's VGW via a Private VIF incurs no processing fees.
To select the configuration that minimizes unnecessary middle-mile data processing charges.

Anahtar Kavram

Selecting the most cost-effective VPC endpoint type (Gateway vs Interface) and routing architecture (VGW with Private VIF vs Transit Gateway with Transit VIF) to minimize AWS network processing charges.
Tahmini Süre:3m 0s
Soru 230Soru

An IoT analytics company collects telemetry data from millions of globally distributed devices. The ingestion endpoints are hosted on Amazon EC2 instances behind Network Load Balancers (NLBs) in the us-east-1 and eu-west-1 Regions. The telemetry client applications transmit packets over UDP and require minimal network latency and high availability. During peak periods, regional public internet congestion causes packet loss and latency spikes. The architecture must dynamically route client traffic to the closest healthy Region and fail over within seconds if a Region becomes unreachable. Which two actions should a solutions architect take to meet these requirements?

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Cevap: Associate the Network Load Balancers in both Regions as endpoints of an AWS Global Accelerator accelerator.; Update the client applications to connect to the Anycast IP addresses provided by the AWS Global Accelerator accelerator.

Cevap

Associate the Network Load Balancers in both Regions as endpoints of an AWS Global Accelerator accelerator, and update the client applications to connect to the Anycast IP addresses provided by the AWS Global Accelerator accelerator.
The correct answer combines associating the Network Load Balancers in both Regions as endpoints of an AWS Global Accelerator accelerator with updating the client applications to connect to the Anycast IP addresses provided by the accelerator. AWS Global Accelerator routes client UDP traffic over the AWS global private network instead of the public internet, avoiding regional congestion, and uses static Anycast IP addresses to dynamically route connections to the nearest healthy endpoint. In the event of an outage in one Region, Global Accelerator detects the failure via endpoint health checks and automatically redirects client traffic to the alternate Region within seconds, bypassing DNS caching delays.

Adım Adım Çözüm

1
Analyze the performance and latency requirements of the UDP workload.
The telemetry data uses UDP, suffers from public internet congestion, and requires low-latency routing to the closest Region with rapid, sub-minute failover.
This determines that DNS-based routing (Route 53) is inadequate because client-side caching of DNS answers delays failover beyond the 'seconds' requirement.
2
Evaluate AWS Global Accelerator for global UDP routing.
AWS Global Accelerator provides Anycast IP addresses that ingest UDP traffic at the nearest AWS edge location and transport it over the congestion-free AWS global network to the NLB endpoints.
This directly resolves the public internet packet loss and latency spikes while providing health-check-driven failover to the alternative Region within seconds.
3
Select the required architectural steps to implement AWS Global Accelerator.
The NLBs in both Regions are associated as endpoints with the accelerator, and the client applications are configured to direct their traffic to the accelerator's Anycast IP addresses.
These two steps establish the scalable and highly available global routing path.

Anahtar Kavram

AWS Global Accelerator uses static Anycast IP addresses and the AWS global network to provide low-latency, high-performance routing and sub-minute automatic failover for TCP and UDP workloads across multiple Regions.
Soru 231Soru

A financial services company is designing a hybrid network architecture to connect its on-premises trading application in London to its AWS resources. The AWS environment consists of a primary production VPC in the `eu-west-1` Region and a disaster recovery VPC in the `us-east-1` Region. The on-premises application requires a primary, highly reliable connection with a minimum throughput of 5 Gbps5\text{ Gbps} to the `eu-west-1` Region. Additionally, the company requires a backup connection to `eu-west-1` that must support a minimum throughput of 2 Gbps2\text{ Gbps} during failover events. This backup path must also allow communication with the `us-east-1` VPC. Which two options should the solutions architect choose to meet these requirements?

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Cevabı ve açıklamayı göster

Cevap: Provision an AWS Direct Connect connection with a 10 Gbps10\text{ Gbps} physical port to an AWS Direct Connect location in London, and configure a transit virtual interface (transit VIF) to an AWS Transit Gateway.; Set up an AWS Site-to-Site VPN connection terminating on an AWS Transit Gateway with Equal-Cost Multi-Path (ECMP) routing enabled, utilizing at least two active VPN tunnels.

Cevap

Provision an AWS Direct Connect connection with a 10 Gbps10\text{ Gbps} physical port to an AWS Direct Connect location in London, configure a transit virtual interface to an AWS Transit Gateway, and set up an AWS Site-to-Site VPN connection terminating on an AWS Transit Gateway with Equal-Cost Multi-Path (ECMP) routing enabled, utilizing at least two active VPN tunnels.
To meet the 5 Gbps5\text{ Gbps} primary throughput requirement and route traffic to multiple VPCs across different Regions, the company must provision an AWS Direct Connect connection with a 10 Gbps10\text{ Gbps} port and link it via a transit virtual interface to an AWS Transit Gateway. To support the backup requirement of 2 Gbps2\text{ Gbps}, the company must use AWS Transit Gateway with ECMP routing enabled over multiple active VPN tunnels, since a single VPN tunnel has a performance limitation of 1.25 Gbps1.25\text{ Gbps}.

Adım Adım Çözüm

1
Evaluate the primary bandwidth requirement.
The primary path requires at least 5 Gbps5\text{ Gbps} throughput, which exceeds standard VPN capabilities. This requires provisioning a dedicated 10 Gbps10\text{ Gbps} AWS Direct Connect connection.
Direct Connect provides high, consistent bandwidth and lower latency than internet-based connections.
2
Address the multi-Region routing requirement.
Attach the Direct Connect connection to an AWS Transit Gateway using a Transit Virtual Interface (transit VIF).
Transit Gateway allows central management of routing to multiple VPCs across different Regions (using Transit Gateway peering for the remote Region).
3
Evaluate the backup connection throughput requirement.
The backup connection must support 2 Gbps2\text{ Gbps} throughput. Since a single Site-to-Site VPN tunnel is limited to 1.25 Gbps1.25\text{ Gbps}, the solutions architect must terminate the VPN on Transit Gateway and enable Equal-Cost Multi-Path (ECMP) routing.
ECMP aggregates the throughput of multiple active VPN tunnels, allowing the total bandwidth to exceed the single-tunnel threshold of 1.25 Gbps1.25\text{ Gbps}.

Anahtar Kavram

Scaling hybrid network throughput using Direct Connect with Transit Gateway and scaling Site-to-Site VPN bandwidth beyond single-tunnel limits using ECMP on Transit Gateway.
Tahmini Süre:3m 0s
Soru 232Soru

A logistics company runs a global supply chain portal that uses a PostgreSQL-compatible relational database. The application is deployed in a primary AWS Region (us-east-1) and a secondary AWS Region (us-west-2). The database tier must support a Recovery Point Objective (RPO) of under 11 second and a Recovery Time Objective (RTO) of under 22 minutes for disaster recovery. The secondary region must also serve local read traffic with low latency. Which database configuration meets these requirements with the least operational overhead?

Cevabı ve açıklamayı göster

Cevap: Deploy an Amazon Aurora Global Database with the primary cluster in us-east-1 and a secondary cluster in us-west-2. In the event of a regional outage, promote the secondary cluster to primary.

Cevap

Deploy an Amazon Aurora Global Database with the primary cluster in us-east-1 and a secondary cluster in us-west-2. In the event of a regional outage, promote the secondary cluster to primary.
Amazon Aurora Global Database is designed for globally distributed applications. It uses fast, storage-based replication that keeps lag under 11 second (meeting the RPO of under 11 second). During a regional disaster, the secondary cluster can be promoted to a primary read-write cluster in under 22 minutes (meeting the RTO of under 22 minutes). Local read replicas in the secondary cluster can serve local read traffic with minimal latency, fulfilling the read scaling constraint with minimal operational overhead.

Adım Adım Çözüm

1
Analyze the requirements for database engine compatibility, cross-region capability, RTO, RPO, and read scaling.
Identify that the database must be PostgreSQL-compatible, support cross-region read scaling, and have an RPO under 11 second and RTO under 22 minutes.
This establishes the boundaries for evaluating AWS database replication mechanisms.
2
Evaluate Amazon Aurora Global Database performance metrics against the RTO and RPO targets.
Aurora Global Database features typical RPO of less than 11 second and can be promoted to a primary cluster in less than 22 minutes.
This meets both the recovery criteria and local read requirements in the secondary region.
3
Examine alternative database replication solutions such as RDS Multi-AZ with cross-region read replicas or DMS Pilot Light setups.
Determine that RDS read replicas do not support direct write failovers without manual intervention, and Pilot Light configurations fail the RTO target.
This rules out the distractors as they cannot satisfy the combination of recovery speed and replication capability.

Anahtar Kavram

Amazon Aurora Global Database provides cross-region replication with sub-second RPO and fast promotion for under 2-minute RTO, while supporting local reads.
Tahmini Süre:2m 0s
Soru 233Soru

A global logistics platform is migrating its critical shipment tracking database to Amazon Aurora PostgreSQL. The database must be highly available within the local region, achieving a Recovery Time Objective (RTO) of less than 3030 seconds for failovers. Additionally, the system must handle a heavy volume of read requests without degrading the performance of write transactions. Which combination of configurations should a solutions architect implement to meet these requirements? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Deploy an Amazon Aurora DB cluster across multiple Availability Zones with a primary writer instance and at least one reader instance.; Configure the application to use the Aurora cluster writer endpoint for write operations and the reader endpoint for read operations.

Cevap

Deploy an Amazon Aurora DB cluster across multiple Availability Zones with a primary writer instance and at least one reader instance, and configure the application to use the Aurora cluster writer endpoint for write operations and the reader endpoint for read operations.
Deploying an Amazon Aurora DB cluster across multiple Availability Zones with a reader instance ensures automatic, rapid failover (typically under 3030 seconds) when the primary writer fails. By utilizing the cluster's dedicated writer and reader endpoints, the application can cleanly separate write transactions from heavy read volumes, ensuring read scaling does not degrade write performance.

Adım Adım Çözüm

1
Select a database configuration that supports automatic regional failover under 3030 seconds.
Amazon Aurora Multi-AZ deployment with at least one Aurora Replica (reader) is chosen. In the event of a writer failure, Aurora promotes the reader to primary writer within 3030 seconds.
Meets the high availability and strict RTO requirements.
2
Address the read scaling requirement without impacting write performance.
Configure application connection endpoints to direct read traffic to the Aurora reader endpoint and write traffic to the writer endpoint.
This offloads read operations from the writer instance, preserving write transaction throughput.

Anahtar Kavram

Amazon Aurora Multi-AZ failover mechanics and endpoint routing for read/write scaling.
Soru 234Soru

A financial services company is building a real-time fraud detection and transaction auditing system. The system receives clickstream data from millions of mobile devices. During normal business hours, the data ingestion rate averages 15 MB/s15\text{ MB/s}, but it peaks at 45 MB/s45\text{ MB/s}. The average size of each clickstream payload is 1 KB1\text{ KB}. The solutions architect must design a system that satisfies two requirements: first, a fraud-detection application must consume the data with sub-second latency and process user clickstreams in strict sequential order based on the user session ID; second, the raw data must be transformed into Apache Parquet format and stored in Amazon S3 within 5 minutes5\text{ minutes} of ingestion for downstream batch analysis. Which architecture meets these performance and ordering requirements most cost-effectively?

Cevabı ve açıklamayı göster

Cevap: Ingest the data using Amazon Kinesis Data Streams configured with 4545 shards, using the user session ID as the partition key. Configure the fraud-detection application to consume records from the stream in real time. Create an Amazon Data Firehose delivery stream that reads from the same Kinesis data stream, transforms the incoming data into Parquet using an inline AWS Lambda function, and writes the output directly to Amazon S3 Standard.

Cevap

The architecture that uses Amazon Kinesis Data Streams with 4545 shards (partitioned by user session ID) for real-time consumption, along with Amazon Data Firehose writing to Amazon S3 Standard with an inline AWS Lambda function for Parquet transformation.
The correct option is correct because the peak write throughput of 45 MB/s45\text{ MB/s} and 45,000 records/s45,000\text{ records/s} requires at least 4545 shards in Amazon Kinesis Data Streams to prevent write throttling. Using the user session ID as the partition key guarantees that all records for a session land in the same shard and are consumed in sequential order, meeting the ordering requirement. Amazon Data Firehose can read from the stream to buffer, transform, and write data to Amazon S3 Standard within the required 5 minutes5\text{ minutes}. Using S3 Standard avoids the minimum storage billing duration constraints associated with S3 Standard-IA.

Adım Adım Çözüm

1
Calculate the minimum number of Kinesis Data Streams shards required for peak ingestion throughput.
Peak throughput of 45 MB/s45\text{ MB/s} and 45,000 records/s45,000\text{ records/s} requires max(45 MB/s1 MB/s/shard,45,000 records/s1,000 records/s/shard)=45\max\left(\frac{45\text{ MB/s}}{1\text{ MB/s/shard}}, \frac{45,000\text{ records/s}}{1,000\text{ records/s/shard}}\right) = 45 shards.
Each shard supports up to 1 MB/s1\text{ MB/s} write throughput or 1,0001,000 write records per second.
2
Identify the mechanism for maintaining user clickstream ordering.
Using the user session ID as the partition key in Kinesis Data Streams.
This ensures all events for a given session are mapped to the same shard and processed sequentially.
3
Select the data delivery and transformation path for batch analysis.
Amazon Data Firehose reads from the stream, transforms data to Parquet using AWS Lambda, and stores it in Amazon S3 Standard.
Firehose simplifies the buffering, transformation, and ingestion to S3 within the 5-minute requirement, and S3 Standard avoids the 30-day minimum duration charge of S3 Standard-IA.

Anahtar Kavram

High-performing data ingestion and transformation involving Kinesis Data Streams, Data Firehose, and S3 storage tiering.
Soru 235Soru

A financial auditing enterprise hosts its document archiving application on AWS. The primary storage consists of an Amazon S3 bucket located in us-east-1 containing compliance records. The company must establish a disaster recovery (DR) site in us-west-2 that guarantees a Recovery Point Objective (RPO) of 1515 minutes and a Recovery Time Objective (RTO) of 1515 minutes. The data must be stored with a durability of 99.999999999%99.999999999\%. Which storage architecture meets these requirements?

Cevabı ve açıklamayı göster

Cevap: Configure Amazon S3 Cross-Region Replication (CRR) to a destination S3 bucket in us-west-2 with S3 Replication Time Control (S3 RTC) enabled, and run the application in an active-passive configuration.

Cevap

Configure Amazon S3 Cross-Region Replication (CRR) to a destination S3 bucket in us-west-2 with S3 Replication Time Control (S3 RTC) enabled, and run the application in an active-passive configuration.
The correct solution uses Amazon S3 Cross-Region Replication (CRR) with S3 Replication Time Control (S3 RTC). S3 RTC guarantees that 99.9%99.9\% of uploaded objects are replicated to the destination region within 1515 minutes, directly fulfilling the 1515-minute RPO. Because the files are replicated to standard S3 storage, they are immediately accessible during a failover event, easily satisfying the 1515-minute RTO. Amazon S3 Standard also natively provides 99.999999999%99.999999999\% data durability.

Adım Adım Çözüm

1
Analyze the durability and recovery metrics required by the business scenario.
The platform needs 99.999999999%99.999999999\% (11 nines) durability, an RPO of 1515 minutes, and an RTO of 1515 minutes.
These constraints narrow the storage target to Amazon S3 Standard (for durability) and require a replication mechanism that completes within 1515 minutes with immediate data availability.
2
Evaluate the replication options for Amazon S3 across regions.
Standard Amazon S3 Cross-Region Replication (CRR) does not provide a replication time guarantee, but enabling S3 Replication Time Control (S3 RTC) guarantees replication of 99.9%99.9\% of objects within 1515 minutes.
To satisfy a strict 1515-minute RPO, a replication time SLA like S3 RTC is required to ensure files are copied to the secondary region within the threshold.
3
Evaluate the accessibility of replicated storage classes in the secondary region.
Objects replicated to S3 Standard in the destination bucket are immediately readable. In contrast, S3 Glacier classes require a retrieval process.
The RTO is 1515 minutes, meaning data must be ready for immediate read operations. Glacier Standard retrieval takes 33 to 55 hours, which violates the RTO.

Anahtar Kavram

High availability and disaster recovery for object storage using Amazon S3 Cross-Region Replication (CRR) and S3 Replication Time Control (S3 RTC) to meet tight RPO/RTO SLAs.
Tahmini Süre:2m 0s
Soru 236Soru

A solutions architect is designing a high-performance real-time bidding application. The application requires microsecond read latency when retrieving active user profiles (uniquely identified by UUID v4) and must support up to 2,000,0002,000,000 write operations per second during peak events. Additionally, the system must run hourly aggregation queries based on campaign ID. A small subset of popular campaigns receives more than 95%95\% of all bids, creating a highly skewed write pattern. Which combination of database configurations will meet these requirements while preventing write throttling on the primary table and its indexes? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache read requests for user profiles, reducing retrieval latency to the microsecond level.; Implement write sharding on the Global Secondary Index (GSI) by appending a randomized partition key suffix to the campaign ID attribute before writing to the table.

Cevap

Deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache read requests, and implement write sharding on the Global Secondary Index (GSI) by appending a randomized partition key suffix to the campaign ID attribute.
Deploying Amazon DynamoDB Accelerator (DAX) caches frequent read queries for user profiles, reducing retrieval latency from milliseconds to microseconds. Implementing write sharding by appending a randomized suffix to the campaign ID attribute distributes the highly skewed write volume across multiple partition keys on the Global Secondary Index (GSI). This prevents a few popular campaign partition keys from becoming hot partitions, avoiding write throttling on both the GSI and the base table.

Adım Adım Çözüm

1
Address the microsecond read latency requirement for user profiles.
Identify that standard DynamoDB read latency is in the single-digit millisecond range. To achieve microsecond latency, an in-memory cache is required. Amazon DynamoDB Accelerator (DAX) is the optimal choice as it integrates natively with DynamoDB APIs.
DAX caches reads, offloading the database and reducing read latency to microseconds without requiring code changes to manage cache invalidation.
2
Analyze the write pattern and identify potential bottlenecks.
The application requires up to 2,000,0002,000,000 writes per second, and 95%95\% of these writes are concentrated on a few popular campaign IDs.
This highly skewed distribution will lead to write throttling due to hot partitions if campaign ID is used directly as a partition key.
3
Select the indexing strategy for hourly aggregations.
A Global Secondary Index (GSI) is needed to query data by campaign ID. However, writing directly to a GSI with campaign ID as the partition key would create hot partitions, causing write operations to fail.
GSI writes are throttled if a single GSI partition is overloaded, which backpressures and throttles writes on the base table.
4
Mitigate the hot partition key issue on the index.
Implement write sharding by appending a random integer suffix (e.g., from a range of 1 to N) to the campaign ID attribute before writing to the table.
This distributes the writes across multiple physical partitions for the index, allowing the database to scale horizontally and support the heavy write load without throttling.

Anahtar Kavram

Mitigating DynamoDB hot partitions through write sharding on partition keys with highly skewed data distributions, and using DAX for microsecond read caching.
Tahmini Süre:3m 0s
Soru 237Soru

A company is migrating a legacy transactional system to AWS. The system receives events that must be processed in the exact order they are generated. The event volume is highly unpredictable, with hours of complete silence followed by sudden spikes of traffic. The processing of each event is lightweight and takes less than 5 seconds. Which two configuration options should a solutions architect combine to design a serverless, cost-effective architecture that scales automatically and preserves message order? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Configure an Amazon SQS FIFO queue to store the incoming events and preserve their sequence.; Use an AWS Lambda function triggered by the queue to process the events, which automatically scales down to zero when no events are present.

Cevap

The correct architecture uses an Amazon SQS FIFO queue to guarantee message order, combined with an AWS Lambda function that scales down to zero when idle.
The combination of an Amazon SQS FIFO queue and an AWS Lambda function provides a serverless architecture that scales on-demand. SQS FIFO ensures messages are processed in the correct order, and AWS Lambda processes the messages as they arrive, scaling down to zero when the queue is empty to avoid idle compute costs.

Adım Adım Çözüm

1
Analyze ordering requirements.
The legacy system requires events to be processed in the exact sequence they are generated.
This necessitates the use of Amazon SQS FIFO queues instead of standard queues, which only offer best-effort ordering.
2
Evaluate scaling and idle costs.
The traffic profile is highly unpredictable with periods of complete silence.
An event-driven AWS Lambda configuration is ideal as it scales to zero instances during idle hours, minimizing compute costs compared to continuously running services.

Anahtar Kavram

Combining Amazon SQS FIFO queues for message sequencing with event-driven AWS Lambda triggers for automated scaling and cost-efficiency.
Soru 238Soru

A fleet management company collects data from delivery trucks and stores it in Amazon S3. The data consists of two types of files:

* Vehicle telematics data: Millions of small JSON files averaging 10 KB10\text{ KB} in size. These files are accessed frequently during the first 1414 days, and must be retained for 9090 days.
* Dashcam video recordings: Large MP4 files averaging 500 MB500\text{ MB} in size. These files are accessed frequently during the first 1515 days for safety reviews, and must be archived for a total of 6060 days for compliance audits. When an audit occurs, these videos must be retrievable within minutes.

Which two storage configurations will meet these requirements at the lowest cost?

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Cevabı ve açıklamayı göster

Cevap: Keep the vehicle telematics data in S3 Standard, and configure a lifecycle rule to delete the objects after 9090 days.; Configure a lifecycle rule for the dashcam video recordings to transition them to S3 Standard-IA after 1515 days, and delete them after 6060 days.

Cevap

Keeping the vehicle telematics data in S3 Standard and deleting it after 9090 days, and configuring a lifecycle rule for the dashcam video recordings to transition them to S3 Standard-IA after 1515 days and deleting them after 6060 days.
The correct solution keeps the small telematics files in S3 Standard because their 10 KB10\text{ KB} size would trigger the 128 KB128\text{ KB} minimum billing penalty if transitioned to S3 Standard-IA. Conversely, the 500 MB500\text{ MB} video files are large enough to avoid the minimum size penalty, and transitioning them to S3 Standard-IA after 1515 days leaves them in S3 Standard-IA for 4545 days (satisfying the 3030-day minimum storage duration limit) while still ensuring millisecond-level retrievals.

Adım Adım Çözüm

1
Evaluate the vehicle telematics data storage strategy based on file size and retention constraints.
The telematics files average 10 KB10\text{ KB}, which is well below the S3 Standard-IA minimum billing size threshold of 128 KB128\text{ KB}. Storing these files in S3 Standard-IA would result in a 1,280%1,280\% billed size penalty per object. Therefore, they should remain in S3 Standard and be deleted after 9090 days.
To avoid paying for minimum billing size overhead and transition costs on small objects.
2
Evaluate the dashcam video recordings storage strategy based on retrieval time, file size, and retention constraints.
The videos average 500 MB500\text{ MB} (above the 128 KB128\text{ KB} minimum size) and need to be stored for 6060 days total. Frequent access ends after 1515 days. Transitioning them to S3 Standard-IA after 1515 days means they are stored there for 4545 days, which exceeds the 3030-day minimum storage duration of S3 Standard-IA, avoiding early deletion penalties. S3 Standard-IA also satisfies the requirement for millisecond-level (minutes-range) retrievals.
To minimize storage costs for large, infrequently accessed files requiring rapid retrieval.
3
Verify network routing and check compliance of alternative storage tiers.
Glacier tiers fail the retrieval time requirement, and routing large uploads through a NAT Gateway adds unnecessary data processing charges.
To ensure both cost efficiency and compliance with the retrieval SLA.

Anahtar Kavram

S3 Storage Class Analysis, billing minimums (128 KB128\text{ KB} for S3 Standard-IA), and storage duration minimums (3030 days for S3 Standard-IA).
Tahmini Süre:2m 0s
Soru 239Soru

An automotive manufacturer is designing a telemetry ingestion and processing platform for its new fleet of connected vehicles. The system must ingest continuous data from 10,00010,000 vehicles, resulting in a peak write throughput of 80 MB/s80\text{ MB/s} and 100,000100,000 records per second. The telemetry data includes a sequence number and must be processed in the exact order it is received per vehicle for real-time anomaly detection. Additionally, the data must be transformed into Apache Parquet format and stored in Amazon S3 for historical analytics with a maximum delivery latency of 55 minutes. Which combination of actions should the solutions architect recommend to design a high-performing and scalable ingestion and transformation pipeline? (Select two.)

Geçerli olan tümünü seçin

Cevabı ve açıklamayı göster

Cevap: Configure an Amazon Kinesis Data Stream with at least 100100 shards, using the vehicle identifier as the partition key.; Configure an Amazon Kinesis Data Firehose delivery stream with the Kinesis Data Stream as the source, enabling native format conversion to Apache Parquet using an AWS Glue table schema, and deliver the data to Amazon S3.

Cevap

Configure an Amazon Kinesis Data Stream with at least 100 shards, using the vehicle identifier as the partition key, and configure an Amazon Kinesis Data Firehose delivery stream with the Kinesis Data Stream as the source, enabling native format conversion to Apache Parquet using an AWS Glue table schema to deliver the data to Amazon S3.
The correct combination configures an Amazon Kinesis Data Stream with at least 100100 shards and uses Kinesis Data Firehose for Parquet conversion. To handle 100,000100,000 records per second, the stream must have at least 100100 shards since each shard is limited to 1,0001,000 write records per second. Using the vehicle identifier as the partition key ensures strict ordering per vehicle. Kinesis Data Firehose can consume from the stream and use its native conversion capabilities with AWS Glue to convert the JSON payload into Apache Parquet before writing to Amazon S3 within the 5-minute requirement.

Adım Adım Çözüm

1
Determine the required shard capacity for data ingestion based on stream limitations.
At least 100100 shards are needed.
A Kinesis Data Stream shard supports up to 1 MB/s1\text{ MB/s} or 1,0001,000 records/sec write throughput. The vehicle telemetry produces 80 MB/s80\text{ MB/s} (requiring 8080 shards) and 100,000100,000 records/sec (requiring 100100 shards). The record count is the bottleneck, so the stream must be provisioned with at least 100100 shards.
2
Configure the partition key to preserve message ordering per vehicle.
Partition key is set to the vehicle identifier.
By using the vehicle identifier as the partition key, all telemetry records from a specific vehicle are directed to the same shard, which guarantees strict in-order delivery to the real-time anomaly detection consumer.
3
Select the optimal delivery and transformation mechanism for historical storage.
Use Amazon Kinesis Data Firehose with the Kinesis Data Stream as the source, enabling native Parquet format conversion.
Kinesis Data Firehose can read directly from Kinesis Data Streams and perform native Apache Parquet conversion using a schema defined in the AWS Glue Data Catalog. This handles the transformation without custom code (like AWS Lambda) and natively buffers up to 5 minutes before delivering to Amazon S3.

Anahtar Kavram

Calculating Kinesis Data Stream shards based on both throughput and record counts while preserving key-based message ordering, and integrating with Kinesis Data Firehose for native Parquet conversion.
Tahmini Süre:3m 0s
Soru 240Soru

A logistics company is designing the database tier for a real-time package tracking system. The database must be compatible with MySQL, support automatic failover within the primary AWS Region with a Recovery Time Objective (RTO) of less than 30 seconds and a Recovery Point Objective (RPO) of 0, and provide a cross-Region disaster recovery (DR) solution with an RTO of under 15 minutes and an RPO of under 5 seconds. Which combination of AWS database configurations will meet these requirements? (Select TWO.)

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Cevabı ve açıklamayı göster

Cevap: Deploy the primary database as an Amazon Aurora MySQL DB cluster with a writer instance in one Availability Zone and a reader instance in another Availability Zone.; Configure an Amazon Aurora Global Database by adding a secondary Aurora DB cluster in the recovery Region, utilizing storage-level replication.

Cevap

Deploying the primary database as an Amazon Aurora MySQL DB cluster with a writer and a reader instance in different Availability Zones, and configuring an Amazon Aurora Global Database by adding a secondary cluster in the recovery Region.
The correct combination consists of deploying the primary database as an Amazon Aurora MySQL DB cluster with a reader instance in a different Availability Zone (providing local Multi-AZ high availability with RTO < 30 seconds and RPO = 0 via shared storage replication) and configuring an Amazon Aurora Global Database with a secondary cluster in the recovery Region (meeting the RTO of under 15 minutes and RPO of under 5 seconds through low-latency storage-level replication).

Adım Adım Çözüm

1
Evaluate the database compatibility and local resilience requirements.
The requirement for MySQL compatibility, local RTO < 30 seconds, and local RPO = 0 points to Amazon Aurora MySQL. Deploying an Aurora cluster with a writer in one Availability Zone and a reader in a different Availability Zone enables automatic failover in under 30 seconds with 0 data loss (RPO = 0) because the underlying storage volume is replicated across three Availability Zones.
Traditional RDS read replicas or single-instance databases do not support automatic failover or fail to meet the 30-second RTO.
2
Evaluate the disaster recovery requirements for the secondary Region.
The requirements of RTO < 15 minutes and RPO < 5 seconds are met by Amazon Aurora Global Databases. Aurora Global Databases use storage-based replication to copy data to a secondary Region with a latency of less than 1 second (meeting the RPO of under 5 seconds). If a failover is needed, the secondary cluster can be promoted to a standalone read/write cluster in under a minute (meeting the RTO of under 15 minutes).
Backup-based strategies (like Pilot Light with hourly copies) fail the 5-second RPO, and standard RDS replica failover across Regions is not automated.

Anahtar Kavram

Amazon Aurora Multi-AZ deployment and Aurora Global Databases for high availability and low-RPO/RTO cross-Region disaster recovery.
Tahmini Süre:2m 30s
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Tüm alıştırma soruları — AWS Certified Solutions Architect - Associate | Examkin