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Question 1221Question →

A company is hosting a fleet tracking application on AWS. The application consists of two main database workloads:

* An IoT sensor data ingestion workload that writes to an Amazon DynamoDB table. The traffic is highly unpredictable and spiky, with peak data rates reaching up to 10 times10\text{ times} the baseline activity, and dropping to near zero during off-peak hours.
* A weekly analytics reporting workload that queries an Amazon RDS for MySQL DB instance. The database experiences heavy read traffic during a 4-hour4\text{-hour} window once a week. The reports are non-critical, and the business has stated that high availability and automated failover are not required. The DB instance is currently configured with Multi-AZ enabled and uses provisioned IOPS (io2io2) storage to handle the weekly peak.

Which TWO options should the solutions architect implement to design a cost-optimal database architecture? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure the DynamoDB table to use On-Demand capacity mode.; Modify the RDS DB instance to a Single-AZ deployment and change the storage type from io2io2 to gp3gp3.

Answer

The correct configurations are to configure the DynamoDB table to use On-Demand capacity mode, and to modify the RDS DB instance to a Single-AZ deployment while changing the storage type from io2 to gp3.
Configuring DynamoDB to use On-Demand capacity mode automatically handles the spiky, unpredictable IoT data ingestion workload without the need to pay for idle capacity. For the MySQL database, since high availability is not a requirement, moving from a Multi-AZ deployment to a Single-AZ deployment reduces compute costs by eliminating the standby instance. Additionally, changing the storage type from provisioned IOPS (io2) to general purpose SSD (gp3) provides a cost-effective performance tier where throughput and IOPS can be scaled independently without paying io2 rates.

Step-by-Step Solution

1
Analyze the DynamoDB workload behavior
The workload is highly spiky and unpredictable, with near-zero traffic off-peak. On-Demand capacity mode is optimal as it charges only for the read and write requests executed, preventing cost overhead from idle provisioned capacity.
Choosing the appropriate DynamoDB capacity mode is critical to optimizing costs for spiky, irregular traffic.
2
Evaluate high availability requirements for the RDS instance
The reporting system is non-critical and doesn't require high availability. Disabling Multi-AZ (Single-AZ) reduces the instance cost significantly by removing the redundant standby instance.
Multi-AZ doubles DB instance costs and should be avoided if high availability is not required.
3
Analyze storage performance options for the RDS instance
The database uses io2 storage. Changing to gp3 storage allows provisioning the required baseline performance and scaling throughput/IOPS independently without paying the premium costs associated with io2.
gp3 storage provides a much more cost-effective alternative to io2 for general workloads.

Key Concept

Selecting the most cost-effective database deployment options, scaling strategies, and storage tiers based on workload requirements and SLA constraints.
Question 1222Question →

A company is migrating a business intelligence (BI) application to AWS. The application runs complex analytical queries, including multi-table joins and aggregations, on a 10 TB10\text{ TB} relational database containing historical sales data. The BI analysts run reports throughout the day, and they require query results in less than 55 seconds. The database is updated once per day during a nightly batch loading process. Which database solution should a solutions architect recommend to meet these performance requirements?

Show answer & explanation

Answer: An Amazon Redshift cluster with distribution keys and sort keys optimized for the common query patterns.

Answer

An Amazon Redshift cluster with distribution keys and sort keys optimized for the common query patterns.
The correct option is the Amazon Redshift cluster configuration. Amazon Redshift is a columnar data warehouse optimized for online analytical processing (OLAP) that can efficiently perform complex joins and aggregations on a 10 TB10\text{ TB} dataset in under 55 seconds. By defining appropriate distribution and sort keys, data movement between compute nodes is minimized, and query performance is maximized.

Step-by-Step Solution

1
Analyze the workload characteristics and requirements.
The application requires executing complex analytical queries, joins, and aggregations on a large historical dataset (10 TB10\text{ TB}) with low latency (under 55 seconds) for BI reporting.
Understanding whether the database is used for transaction processing (OLTP) or analytical processing (OLAP) guides the architectural choice.
2
Evaluate the database service options based on the workload type.
Amazon Redshift is optimized for OLAP/data warehousing workloads, whereas Amazon RDS is designed for OLTP workloads and Amazon DynamoDB is a NoSQL database that does not natively support multi-table joins.
Selecting a database model designed for high-performance analytics is critical for query efficiency at a 10 TB10\text{ TB} scale.
3
Determine the optimal performance configuration within the chosen service.
An Amazon Redshift cluster configured with proper distribution keys (to minimize data movement between nodes) and sort keys (to optimize scan performance) will meet the latency requirements.
Distribution and sort keys are key performance tuning mechanisms in Amazon Redshift that prevent network bottlenecks and minimize disk I/O.

Key Concept

Selecting and configuring the optimal AWS database service based on workload patterns (OLAP vs. OLTP) to achieve high performance at scale.
Question 1223Question →

A software company is building a real-time collaborative whiteboarding application. The backend service runs on Amazon EC2 instances and requires sub-millisecond network latency between the instances to synchronize cursor movements and drawing paths across active sessions. The application processes user actions on port 9443, while a management agent on the instances listens on port 80. An Application Load Balancer (ALB) is configured to distribute user session traffic to these instances. Which TWO configurations should a solutions architect implement to achieve optimal performance and ensure correct health monitoring?

Select all that apply

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group.; Configure the ALB target group health check to query port 9443.

Answer

Launch the EC2 instances in a cluster placement group and configure the ALB target group health check to query port 9443.
To achieve sub-millisecond latency between instances, they must be launched in a cluster placement group, which places them in close proximity within a single Availability Zone. To ensure that the Application Load Balancer correctly detects application failures, the health check port must match the application port (port 9443) rather than the management port (port 80).

Step-by-Step Solution

1
Analyze the network latency requirements between the EC2 instances.
The application requires sub-millisecond node-to-node latency for real-time synchronization.
This requirement is satisfied by using a cluster placement group, which physically groups instances within a single Availability Zone.
2
Determine the application port that is serving client traffic.
The collaborative whiteboard service processes user actions on port 9443.
This identifies the correct target port for load balancing and health checks.
3
Evaluate the health check port configuration.
The load balancer health check must target port 9443 to monitor the actual application process.
Targeting the default management port (port 80) would fail to detect if the whiteboarding service on port 9443 crashes, leading to black-holing client traffic.

Key Concept

Low-latency compute node placement and application-aware load balancer health checks are critical for high-performing, resilient architectures.
Question 1224Question →

A digital publishing company hosts a web application in the us-east-1 Region. The application uses an Amazon RDS for MySQL database to store article metadata. The company wants to implement a disaster recovery (DR) strategy in the us-west-2 Region. The solution must achieve a Recovery Point Objective (RPO) of 1515 minutes and a Recovery Time Objective (RTO) of 22 hours while minimizing ongoing infrastructure costs.

Which strategy should a solutions architect recommend to meet these requirements?

Show answer & explanation

Answer: Replicate the database to the secondary Region. Keep application servers in the secondary Region turned off, or deploy them using AWS CloudFormation templates only during a failover event.

Answer

Replicate the database to the secondary Region. Keep application servers in the secondary Region turned off, or deploy them using AWS CloudFormation templates only during a failover event.
The correct strategy is a Pilot Light approach. Replicating the database to the secondary Region ensures the data is ready and meets the 15-minute RPO. Keeping application servers shut down or deploying them on demand using templates minimizes ongoing infrastructure costs while comfortably meeting the 2-hour RTO.

Step-by-Step Solution

1
Identify the target constraints
RPO is 1515 minutes, RTO is 22 hours, and ongoing infrastructure costs must be minimized.
The solution must satisfy all constraints, especially the balance between speed of recovery (RTO/RPO) and cost.
2
Evaluate database replication options
Database must be actively replicated to the secondary Region (e.g., using cross-region read replicas) to meet the 1515-minute RPO.
Traditional daily backups do not meet a 1515-minute RPO constraint.
3
Evaluate compute scaling options
Since the RTO is 22 hours, application servers do not need to be running continuously. They can be created on-demand or kept turned off.
Keeping compute resources powered down or provisioning them on-demand via templates (Pilot Light) minimizes ongoing costs while easily fitting into the 22-hour window.

Key Concept

Disaster Recovery (DR) Strategies (Pilot Light vs. Warm Standby)
Estimated Time:1m 0s
Question 1225Question →

A gaming company runs a matchmaking service on Amazon EC2 instances within an Auto Scaling group (ASG). The instances are located in a private subnet and registered with an Application Load Balancer (ALB) target group. The matchmaking service is configured to listen on TCP port 7000. The target group is configured to route traffic to port 7000. Currently, all EC2 instances are marked as unhealthy by the ALB.

The security group for the ALB allows inbound HTTPS traffic from 0.0.0.0/0 and all outbound traffic. The security group for the EC2 instances allows inbound TCP traffic on port 7000 from the ALB's security group. The Network ACL (NACL) for the private subnet has been configured to allow inbound traffic on port 7000 and outbound traffic on port 7000.

Which two changes should the solutions architect make to resolve this issue and allow the instances to register as healthy? (Select two.)

Select all that apply

Show answer & explanation

Answer: Modify the target group's health check settings to query port 7000 rather than the default port.; Update the outbound rules of the private subnet's network access control list (NACL) to allow TCP traffic to ephemeral ports (1024-65535).

Answer

The solutions architect should modify the target group's health check settings to query port 7000 rather than the default port, and update the outbound rules of the private subnet's network access control list (NACL) to allow TCP traffic to ephemeral ports (1024-65535).
The correct options modify the target group health check port to query the custom service port (port 7000) and open the outbound network ACL to allow return traffic on ephemeral ports (1024-65535). Since target groups default to port 80 for health checks, the mismatch must be corrected. Additionally, because network ACLs are stateless, return packets from port 7000 to the load balancer's ephemeral ports must be permitted explicitly.

Step-by-Step Solution

1
Analyze the target group health check behavior.
By default, an Elastic Load Balancing target group performs health checks using the HTTP protocol on the default port (port 80). Since the EC2 instances are hosting the matchmaking service on port 7000 and their security group only allows inbound traffic on port 7000, any checks to port 80 will fail.
Correcting the target group health check port configuration is necessary to ensure the load balancer probes the active port of the application.
2
Evaluate the network access control list (NACL) configuration.
Network ACLs are stateless. While the inbound NACL allows incoming connections to port 7000, the outbound NACL restricts response traffic to only port 7000. When the Application Load Balancer initiates a connection, it uses an ephemeral source port (1024-65535). The EC2 instances attempt to send response packets to this ephemeral port, but the outbound NACL blocks them.
Allowing outbound TCP traffic to ephemeral ports (1024-65535) is required in the private subnet's NACL to permit successful return communications.

Key Concept

Auto Scaling and Elastic Load Balancing target group health check configuration, and security group/network ACL interactions.
Estimated Time:2m 0s
Question 1226Question →

A retail company runs a containerized web application on AWS. The application consists of a front-end service hosted on Amazon Elastic Container Service (Amazon ECS) on AWS Fargate that runs 24/7 with a highly predictable baseline load of 8 tasks. The application also runs a nightly backend reporting job on Amazon ECS that processes sales data for 3 hours. The reporting job can tolerate interruptions and resume from checkpoints, but must complete within a 6-hour execution window. The company also stores intermediate reporting logs in Amazon S3 for 10 days before deleting them.

Which two strategies should a solutions architect recommend to minimize compute and storage costs for these workloads? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the baseline AWS Fargate tasks.; Configure the Amazon ECS service for the nightly reporting job to use the Fargate Spot capacity provider.

Answer

The correct strategies are to purchase a Compute Savings Plan to cover the baseline Fargate tasks, and to configure the Amazon ECS service for the nightly reporting job to use the Fargate Spot capacity provider.
Purchasing a Compute Savings Plan covers the continuous 24/7 baseline of the ECS Fargate tasks, yielding significant discounts compared to On-Demand pricing. Additionally, using Fargate Spot for the nightly reporting job utilizes spare AWS capacity at a heavy discount, which is appropriate because the job is fault-tolerant and has a flexible completion window.

Step-by-Step Solution

1
Analyze the baseline Fargate tasks workload.
The tasks run 24/7 with a highly predictable baseline load of 8 tasks.
Workloads with stable, predictable, and continuous compute needs are ideal candidates for commitment-based pricing models like Compute Savings Plans to maximize discount rates.
2
Analyze the nightly reporting job workload.
The job runs for 3 hours, can tolerate interruptions, and has a flexible completion window of 6 hours.
Since the job is fault-tolerant and has flexible execution times, Fargate Spot is the most cost-effective compute choice, offering significant discounts by using spare capacity.
3
Evaluate the storage optimization strategy for intermediate logs.
Storing logs in S3 Standard-IA for only 10 days incurs a cost penalty.
Amazon S3 Standard-IA enforces a minimum 30-day storage billing duration. Moving logs that are deleted in 10 days to S3 Standard-IA results in paying for 20 days of non-existent data, making standard S3 more cost-effective.
4
Evaluate the database pricing and scope of Compute Savings Plans.
Compute Savings Plans cannot cover Amazon RDS database instances.
Compute Savings Plans are restricted to EC2, Fargate, and Lambda. They do not apply to database engines or RDS, which require Database Reserved Instances.

Key Concept

Selecting cost-optimized compute purchasing options (Compute Savings Plans for predictable baseline workloads, Spot instances for fault-tolerant batch workloads) and evaluating minimum storage duration charges in storage tiers.
Question 1227Question →

A gaming platform publishes real-time player match results. These results must be processed by two separate backend systems: a leaderboard service and an achievements service. To ensure accurate game statistics, the match results must be processed by both systems in the exact sequence they occur. Which combination of AWS services should the solutions architect select to decouple these systems while preserving the order of the match results? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Amazon Simple Notification Service (Amazon SNS) FIFO topic; Amazon Simple Queue Service (Amazon SQS) FIFO queues

Answer

Amazon Simple Notification Service (Amazon SNS) FIFO topic and Amazon Simple Queue Service (Amazon SQS) FIFO queues
To decouple the game platform from the leaderboard and achievements services while maintaining message ordering, a combination of Amazon SNS FIFO and Amazon SQS FIFO is the standard architectural pattern. The SNS FIFO topic handles the message fan-out to multiple subscribers, and the SQS FIFO queues ensure that each service consumes the messages in the exact order they were sent.

Step-by-Step Solution

1
Identify the requirement to fan out message processing to multiple independent downstream systems (leaderboard and achievements services).
An Amazon SNS topic is required to publish messages once and deliver them to multiple subscribers.
SNS provides the publish/subscribe (fan-out) capability needed to send the same event to multiple destinations.
2
Identify the requirement to maintain strict ordering of events for both downstream systems.
FIFO (First-In-First-Out) features must be enabled on both the SNS topic and the SQS queues.
Standard SNS topics and SQS queues do not guarantee message ordering, whereas FIFO variants guarantee strict ordering and deduplication.
3
Combine the services to form an ordered fan-out pattern.
The architecture will consist of an SNS FIFO topic publishing to multiple SQS FIFO queues subscribed to it.
This pattern decouples the publisher from the subscribers while ensuring both systems receive events in the exact sequence they were generated.

Key Concept

De-coupling with Fan-Out and Message Ordering
Question 1228Question →

A media company is designing a high-performing backend solution to transcode user-uploaded video files. Each video transcoding job takes between 20 to 30 minutes to complete, and the workload is continuous throughout the day. The company wants to minimize the operational overhead of managing servers while ensuring the system scales dynamically to handle the continuous processing load. Which compute solution should a solutions architect recommend to meet these requirements?

Show answer & explanation

Answer: Package the transcoding application as a container and run it on Amazon Elastic Container Service (Amazon ECS) using the AWS Fargate launch type.

Answer

Package the transcoding application as a container and run it on Amazon Elastic Container Service (Amazon ECS) using the AWS Fargate launch type.
The correct option is to package the application as a container and run it on Amazon ECS with AWS Fargate. Fargate is a serverless compute engine for containers that removes the need to provision and manage EC2 instances, minimizing operational overhead. Because ECS tasks do not have the strict 15-minute execution limit found in AWS Lambda, they can run to completion for the 20 to 30-minute transcoding workloads.

Step-by-Step Solution

1
Analyze the workload runtime requirements.
Identify that the transcoding jobs require 20 to 30 minutes of continuous compute execution.
This rules out compute options with short execution timeouts, such as AWS Lambda's 15-minute limit.
2
Evaluate the management overhead and scaling constraints.
Determine that serverless container orchestration (AWS Fargate) is preferred over managing EC2 Auto Scaling groups to meet the goal of minimal operational overhead.
AWS Fargate handles provisioning, configuring, and scaling the virtual machines, leaving only container management to the user.
3
Verify storage and network requirements against the options.
Confirm that EBS gp3 does not support Multi-Attach and spread placement groups do not optimize network performance.
Ensures that the selected architectural pattern is technically feasible and optimal under AWS service limits.

Key Concept

Selecting appropriate compute services based on execution time limits, scaling requirements, and operational management constraints.
Question 1229Question →

A company is designing a new web application and must select database options that minimize costs while meeting performance and availability requirements. The application has two primary database workloads:

1. A user session data store that experiences highly unpredictable, spiky traffic throughout the day with long periods of complete inactivity.
2. A relational transaction database that requires automatic failover across multiple Availability Zones, but has low, variable usage that drops significantly at night.

Which two database configurations should a solutions architect recommend to meet these requirements most cost-effectively? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Deploy an Amazon DynamoDB table using on-demand capacity mode for the user session store.; Deploy Amazon Aurora Serverless v2 in a Multi-AZ configuration for the relational database.

Answer

The correct configurations are deploying Amazon DynamoDB in on-demand capacity mode for the user session store and deploying Amazon Aurora Serverless v2 in a Multi-AZ configuration for the relational database.
On-demand capacity mode for Amazon DynamoDB is highly cost-effective for unpredictable, spiky workloads because it charges per request without requiring provisioned capacity that would be wasted during idle times. Amazon Aurora Serverless v2 automatically scales compute capacity to match application demand, making it ideal for variable workloads that drop off at night, and when deployed in Multi-AZ, it provides the required automatic failover.

Step-by-Step Solution

1
Analyze the session store workload characteristics.
The session store has unpredictable traffic spikes and long idle periods, meaning capacity needs are highly variable and include zero-usage times.
Choosing the right capacity mode for DynamoDB depends on predicting the traffic pattern to minimize costs.
2
Evaluate the capacity mode for the session store.
Amazon DynamoDB on-demand capacity mode is selected because it charges strictly per request, avoiding any charge when idle, whereas provisioned capacity mode would charge continuously for unused capacity.
This guarantees that the company does not pay for over-provisioned database throughput during inactive hours.
3
Analyze the relational database workload and availability requirements.
The relational database requires automatic failover (Multi-AZ) and has low, variable usage that drops significantly at night.
The solution must support scaling down compute power during off-peak times while keeping high availability intact.
4
Evaluate the relational database engine and configuration.
Amazon Aurora Serverless v2 in a Multi-AZ deployment is selected. It scales compute capacity in small increments dynamically and scales down when traffic drops, while maintaining the secondary replica for failover.
This prevents paying for static provisioning of peak capacity during off-peak hours while satisfying the Multi-AZ requirement.

Key Concept

Selecting the cost-optimal capacity scaling mechanism (on-demand vs. provisioned vs. serverless scaling) and high-availability options based on workload patterns.
Question 1230Question →

A financial services firm is migrating its core transaction processing platform to AWS. The architecture consists of two main components that will run continuously 24/7: a fleet of Amazon EC2 instances for the application layer, and an Amazon RDS for MySQL database. Both components have highly predictable compute requirements. The firm wants to commit to a 3-year term to achieve the maximum possible discount on their compute costs. Which combination of purchasing options offers the most cost-optimized solution?

Show answer & explanation

Answer: Purchase a Compute Savings Plan for the Amazon EC2 application fleet, and purchase Amazon RDS Reserved Instances for the Amazon RDS database.

Answer

Purchase a Compute Savings Plan for the Amazon EC2 application fleet, and purchase Amazon RDS Reserved Instances for the Amazon RDS database.
The correct strategy combines a Compute Savings Plan for the EC2 application layer with RDS Reserved Instances for the database layer. This is necessary because AWS Compute Savings Plans apply discounts to EC2, AWS Lambda, and AWS Fargate, but they do not apply to Amazon RDS database instances, which must be covered under RDS Reserved Instances.

Step-by-Step Solution

1
Analyze the compute requirements and services in the proposed architecture.
The architecture contains EC2 instances (for the application layer) and an RDS for MySQL database, both running 24/7.
Identifying the specific services allows selection of the appropriate AWS discount model for each resource type.
2
Evaluate the eligibility of each resource type under AWS Savings Plans.
Compute Savings Plans apply automatically to EC2, Fargate, and Lambda. They do not apply to Amazon RDS.
Savings Plans have a defined scope of services they cover, and Amazon RDS is excluded.
3
Identify the correct discount mechanism for the Amazon RDS database.
Amazon RDS database instances must be covered by RDS Reserved Instances to receive discounts.
RDS database workloads cannot use Savings Plans and must rely on database-specific Reserved Instances.
4
Select the combination of purchasing models that covers both services and maximizes the 3-year term discount.
Combine a Compute Savings Plan for EC2 and RDS Reserved Instances for RDS.
This matches each resource to its eligible purchasing option, ensuring maximum coverage and optimization.

Key Concept

AWS Savings Plans and Reserved Instances have distinct scopes of application; specifically, Compute Savings Plans do not cover Amazon RDS databases.
Question 1231Question →

An online gaming corporation hosts its multiplayer game servers on Amazon EC2 instances within public and private subnets. The security team wants to monitor network traffic for anomalous behaviors, such as unauthorized port scanning and communication with known malicious IP addresses. Additionally, they must ensure that any modifications to subnet-level network boundaries are recorded and audited for compliance. Which combination of actions should a solutions architect recommend to satisfy these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Enable Amazon GuardDuty to analyze VPC Flow Logs and detect network-layer threat patterns and traffic anomalies.; Use AWS Config to record configuration changes for Network Access Control Lists (NACLs) and evaluate them for compliance.

Answer

The correct options are enabling Amazon GuardDuty to analyze VPC Flow Logs for network-layer threat detection, and using AWS Config to record and evaluate Network Access Control List configuration changes for compliance.
Threat detection is achieved by enabling Amazon GuardDuty, which ingests VPC Flow Logs to analyze and alert on malicious network activity such as port scanning or communication with command-and-control servers. Auditing subnet boundaries requires AWS Config, which monitors configuration changes for Network ACLs to ensure compliance over time.

Step-by-Step Solution

1
Address the requirement to monitor network traffic for anomalous behaviors and malicious communication.
Enable Amazon GuardDuty, which automatically analyzes VPC Flow Logs to detect threats such as port scanning or known malicious IP communication.
VPC Flow Logs capture network traffic metadata, and GuardDuty applies threat intelligence and machine learning to identify security threats.
2
Address the requirement to audit subnet-level network boundary changes.
Use AWS Config to monitor and record modifications to Network Access Control Lists (NACLs), which act as subnet-level firewalls.
AWS Config tracks configuration changes and histories of resources like NACLs, allowing compliance auditing.

Key Concept

Using Amazon GuardDuty for intelligent threat detection via VPC Flow Logs and AWS Config for auditing resource compliance of Network ACLs.
Question 1232Question →

A financial services company is designing a transaction processing system. The system receives account balance update messages that must be processed in the exact sequence they are received to prevent reconciliation errors. The volume of these messages is highly unpredictable, characterized by sudden bursts of activity followed by long periods of idle time. Each message takes approximately 1515 milliseconds to process. The company requires a highly cost-effective, serverless architecture that scales automatically to handle peak loads and incurs no compute costs during idle periods.

Which solution meets these requirements?

Show answer & explanation

Answer: Configure an Amazon SQS FIFO queue to receive the messages. Configure an AWS Lambda function with the queue as an event source to process the messages and write the results to an Amazon DynamoDB table configured with on-demand capacity mode.

Answer

Configure an Amazon SQS FIFO queue to receive the messages, use an AWS Lambda function triggered by the queue via event source mapping to process the updates, and store the results in an Amazon DynamoDB table configured with on-demand capacity mode.
The correct solution uses an Amazon SQS FIFO queue to guarantee message ordering, an AWS Lambda function triggered by the queue to process updates dynamically with zero idle compute costs, and Amazon DynamoDB on-demand capacity mode to handle unpredictable traffic spikes cost-effectively.

Step-by-Step Solution

1
Analyze the database and processing requirements of the scenario.
The workload is highly unpredictable with idle periods, and transaction updates must be processed in the exact sequence they are received.
This requires a queuing mechanism that guarantees first-in, first-out (FIFO) order, coupled with serverless compute and database services that scale to zero to maximize cost efficiency.
2
Evaluate the messaging queue options.
Amazon SQS FIFO queue is selected over SQS standard queue.
An SQS standard queue does not guarantee message ordering, whereas an SQS FIFO queue ensures that messages are processed sequentially, preventing reconciliation errors.
3
Evaluate the compute options for processing the messages.
AWS Lambda triggered by native SQS integration is selected over continuously running EC2 instances or Lambda with continuous polling loops.
AWS Lambda with SQS event source mapping scales compute resources dynamically in response to queue depth, executing in milliseconds, and costing nothing during idle periods. EC2 instances incur ongoing runtime costs when idle, and custom polling loops inside Lambda are highly cost-inefficient and will time out after 1515 minutes.
4
Evaluate the database scaling and capacity mode.
Amazon DynamoDB with on-demand capacity mode is selected over provisioned capacity mode.
On-demand capacity mode automatically scales read/write capacity to accommodate unpredictable spikes and charges nothing for idle capacity, whereas provisioned capacity mode requires manual configuration that leads to idle costs or resource throttling.

Key Concept

Building highly cost-effective, event-driven architectures with AWS Lambda, Amazon SQS FIFO, and Amazon DynamoDB on-demand to handle unpredictable, ordered transactional workloads while scaling to zero during idle periods.
Question 1233Question →

A logistics and supply chain enterprise is building a package tracking system. When a package's delivery status changes, three distinct downstream systems must be updated: a real-time customer tracking portal, an analytics data warehouse, and an automated customer SMS notification service. The sequence of status updates must be strictly preserved on a per-package basis to prevent displaying incorrect states (such as showing 'Delivered' before 'In Transit'). The SMS notification service occasionally experiences intermittent downtime, and its messages must be retried independently without delaying processing for the tracking portal or the data warehouse. Which architecture meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Publish status updates to an Amazon SNS FIFO topic. Subscribe three separate Amazon SQS FIFO queues to the topic (one for each downstream system), and have each system consume messages from its respective queue.

Answer

Publish status updates to an Amazon SNS FIFO topic. Subscribe three separate Amazon SQS FIFO queues to the topic (one for each downstream system), and have each system consume messages from its respective queue.
The solution utilizing an Amazon SNS FIFO topic fanned out to three separate Amazon SQS FIFO queues successfully meets all constraints. SNS FIFO maintains ordering across the fan-out boundary, and the SQS FIFO queues maintain strict order per message group (using the package ID as the MessageGroupId). Crucially, having separate SQS FIFO queues for each service ensures that if the SMS service goes down, messages accumulate in its specific queue and can be retried independently without blocking the tracking portal or the data warehouse.

Step-by-Step Solution

1
Analyze the fan-out requirement.
The message must be fanned out to three distinct downstream consumers (customer portal, analytics warehouse, SMS service) in a decoupled manner.
This establishes that a publish-subscribe pattern is needed.
2
Analyze the ordering requirement.
Status updates must be processed strictly in sequence per package.
This requires first-in, first-out (FIFO) message processing, ruling out standard queues and topics.
3
Analyze retry and outage isolation.
One downstream consumer (SMS service) experiences downtime. Its failures must be retried without impacting the other two consumers.
Each consumer needs its own dedicated queue so that backlogs or errors in one consumer do not affect the throughput of the others.
4
Select the optimal serverless AWS services.
Amazon SNS FIFO fanning out to multiple Amazon SQS FIFO queues provides both ordered delivery and isolated buffering with minimal operational overhead.
This avoids the operational complexity of managing custom consumer groups in Kinesis or writing complex retry logic in a coupled Lambda function.

Key Concept

Combining Amazon SNS FIFO and Amazon SQS FIFO queues in tandem allows for ordered message fan-out and decoupled consumer processing, isolating failures and retries for individual subscribers.
Estimated Time:2m 30s
Question 1234Question →

A logistics firm hosts its order-processing application on Amazon EC2 instances within a private subnet. The security team must implement a solution to monitor the environment for network-layer threat activity, such as brute-force attacks or command-and-control (C2) communication, and receive alerts if any instances start communicating with known malicious IP addresses.

Which solution meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Enable Amazon GuardDuty to analyze VPC Flow Logs and DNS query logs for threat detection, and configure an Amazon EventBridge rule to send notifications through Amazon SNS when a finding is generated.

Answer

Enable Amazon GuardDuty to analyze VPC Flow Logs and DNS query logs for threat detection, and configure an Amazon EventBridge rule to send notifications through Amazon SNS when a finding is generated.
The correct solution uses Amazon GuardDuty, which natively integrates and analyzes VPC Flow Logs, DNS query logs, and CloudTrail events. It detects threat patterns such as command-and-control (C2) communication and brute-force attempts without requiring any software agents. By routing GuardDuty findings through Amazon EventBridge to an Amazon SNS topic, the security team receives automated, real-time alerts with minimal operational effort.

Step-by-Step Solution

1
Enable Amazon GuardDuty in the AWS account.
GuardDuty automatically begins consuming metadata streams from VPC Flow Logs, DNS logs, and AWS CloudTrail events without requiring additional agent installation or infrastructure setup.
This establishes the core intelligent threat detection engine, which uses threat intelligence feeds and machine learning to identify brute-force attacks and C2 activity.
2
Configure an Amazon EventBridge rule with a pattern matching GuardDuty finding events.
EventBridge detects when GuardDuty publishes a threat finding and triggers the configured target.
This allows for real-time orchestration of alerting pathways based on security findings.
3
Target the EventBridge rule to an Amazon Simple Notification Service (Amazon SNS) topic with subscriber endpoints (such as email or Slack webhook).
Notifications are automatically dispatched to the security team whenever a matching threat is detected.
This satisfies the requirement for proactive alerting with minimal operational overhead.

Key Concept

Continuous security monitoring and threat detection at the network layer using managed AWS services.
Estimated Time:2m 0s
Question 1235Question →

A university enrollment portal experiences extremely high read traffic on registration day, causing the database to experience performance degradation. The application's database tier consists of an Amazon Aurora MySQL DB cluster, and the workload primarily consists of queries checking course availability. A solutions architect needs to optimize the database architecture to ensure high performance and scale read capacity dynamically to handle the peak traffic. Which solution meets these requirements with the lowest latency?

Show answer & explanation

Answer: Configure Aurora Auto Scaling to dynamically adjust the number of Aurora Replicas in the DB cluster, and configure the application to send search queries to the Aurora reader endpoint.

Answer

Configure Aurora Auto Scaling to dynamically adjust the number of Aurora Replicas in the DB cluster, and configure the application to send search queries to the Aurora reader endpoint.
The correct option is to configure Aurora Auto Scaling with Aurora Replicas and direct read queries to the reader endpoint. Aurora Replicas share the same storage volume as the primary instance, allowing them to serve read requests with very low latency. Aurora Auto Scaling dynamically manages the number of replicas based on CPU utilization or connection count, and the Aurora reader endpoint automatically load-balances connections among these replicas, ensuring high performance under heavy read loads.

Step-by-Step Solution

1
Analyze the workload requirements
The application is read-heavy (checking course availability) and experiences temporary traffic spikes during registration days.
Identifying that the workload is read-heavy helps narrow down options to those that scale read capacity.
2
Evaluate Amazon Aurora database scaling features
Amazon Aurora supports up to 15 replicas that share the same storage volume. Aurora Auto Scaling can dynamically add or remove replicas based on metric thresholds like CPU utilization.
Dynamic replica scaling directly addresses traffic spikes without manually resizing instances or altering database engines.
3
Select the correct endpoint for distributing read queries
The reader endpoint load-balances read operations across the available Aurora Replicas.
Directing reads to the reader endpoint ensures they are offloaded from the primary instance, maintaining overall system performance.

Key Concept

Scaling read capacity in Amazon Aurora DB clusters using Aurora Replicas and Auto Scaling
Question 1236Question →

An IoT smart home company is deploying its application platform to AWS. The platform consists of two main workloads:

1. A backend microservice API that handles device telemetry updates. The traffic is highly unpredictable and spiky, with individual request durations averaging less than 300 ms300\text{ ms}.
2. A background data processing engine that runs continuously 2424 hours a day, 77 days a week (24/724/7) on Amazon Elastic Container Service (Amazon ECS) using the AWS Fargate launch type.

Which two solutions should a solutions architect recommend to achieve the most cost-effective compute architecture?

Select all that apply

Show answer & explanation

Answer: Host the backend API on AWS Lambda to leverage automatic scaling and pay-per-request billing.; Purchase a Compute Savings Plan to reduce the cost of the Amazon ECS on AWS Fargate workload.

Answer

To optimize costs, host the backend API on AWS Lambda to benefit from automatic scaling and pay-per-request pricing, and purchase a Compute Savings Plan to cover the continuous Amazon ECS on AWS Fargate background workload.
Hosting the backend API on AWS Lambda is correct because it charges only per request and execution duration, which is highly cost-effective for unpredictable, spiky traffic with short execution times. Purchasing a Compute Savings Plan is the correct discount strategy for the Amazon ECS on AWS Fargate workload because Compute Savings Plans apply a discount to Fargate, Lambda, and EC2 compute usage.

Step-by-Step Solution

1
Analyze the backend API requirements.
The API handles spiky, unpredictable traffic with very short request durations (under 300 ms300\text{ ms}).
This workload is a perfect fit for AWS Lambda's pay-per-request billing model, avoiding idle server costs.
2
Analyze the background processing engine requirements.
The engine runs continuously 24/724/7 on ECS Fargate, establishing a highly predictable baseline load.
Because it runs continuously, it requires a pricing discount commitment rather than On-Demand pricing.
3
Evaluate purchasing strategies for Fargate and other services.
A Compute Savings Plan covers Fargate compute costs, whereas EC2 Instance Savings Plans and Compute Savings Plans do not cover non-compute services like DynamoDB.
Compute Savings Plans provide the required discounts for Fargate workloads while retaining flexibility.

Key Concept

Selecting the optimal compute model (Lambda vs. ECS Fargate) and purchasing strategy (Compute Savings Plans) based on traffic patterns and service coverage rules.
Estimated Time:1m 30s
Question 1237Question →

A logistics company is designing a serverless telemetry tracking system for its fleet of 10,00010,000 delivery vehicles. Each vehicle uploads its GPS coordinates to AWS in unpredictable, sudden bursts throughout the day. The location updates must be processed in the exact order they are received for each vehicle to map route history accurately. The processing execution is lightweight, taking less than 200 ms200\text{ ms} per message. The processed coordinates must be stored in a NoSQL database. Which combination of AWS services and configurations will meet these requirements most cost-effectively? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: AWS Lambda to process the updates, triggered by an Amazon SQS FIFO queue.; Amazon DynamoDB configured with on-demand capacity mode to store the telemetry data.

Answer

AWS Lambda triggered by an Amazon SQS FIFO queue, and Amazon DynamoDB configured with on-demand capacity mode.
The combination of AWS Lambda triggered by Amazon SQS FIFO queues and Amazon DynamoDB on-demand capacity mode provides a fully serverless, automatically scaling, and cost-efficient architecture. AWS Lambda scales dynamically to process bursty incoming messages without charging for idle time, while the SQS FIFO queue ensures coordinates are processed in strict order per vehicle. Amazon DynamoDB on-demand mode handles unpredictable spikes without requiring over-provisioning, ensuring costs directly align with usage.

Step-by-Step Solution

1
Determine the optimal compute model based on workload characteristics.
Since the processing is lightweight (200 ms200\text{ ms}) and triggered in unpredictable bursts, AWS Lambda is chosen to scale down to zero when idle.
This avoids paying for idle compute resources that would occur with running servers continuously.
2
Select the correct messaging layer to enforce ordering.
An Amazon SQS FIFO queue is chosen over a standard queue.
FIFO queues guarantee that messages are processed in the exact order they arrive, fulfilling the route tracking requirement.
3
Select the optimal database and capacity model.
Amazon DynamoDB with on-demand capacity mode is selected.
On-demand mode accommodates sudden traffic bursts instantly without manual scaling configuration and incurs no cost when the system is idle.

Key Concept

Combining event-driven serverless compute (AWS Lambda) with order-preserving message queues (Amazon SQS FIFO) and auto-scaling databases (Amazon DynamoDB On-Demand) to optimize costs for unpredictable, sequential workloads.
Question 1238Question →

A company is building a ride-sharing application. The application must process passenger ride-status updates, such as requested, driver assigned, and completed, asynchronously. To ensure the passenger history displays correctly, these updates must be processed in the exact sequence they occurred. The system processes approximately 150 updates per second. Which solution meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Send the ride-status update messages to an Amazon SQS FIFO queue and process them sequentially.

Answer

Send the ride-status update messages to an Amazon SQS FIFO queue and process them sequentially.
The correct option correctly identifies Amazon SQS FIFO queues as the native AWS solution to guarantee that messages are processed in the exact order they are received. Since the application processes 150 updates per second, it falls well within the default limit of 300 transactions per second for FIFO queues without batching, satisfying the request with minimal operational complexity.

Step-by-Step Solution

1
Analyze the requirements for message ordering and throughput.
The application requires strict first-in, first-out (FIFO) ordering for ride-status updates at a rate of 150 updates per second.
This determines whether a standard queue or a FIFO queue is required.
2
Evaluate the capabilities of Amazon SQS queue types.
Amazon SQS FIFO queues guarantee order preservation and support up to 300 transactions per second (TPS) without batching, which exceeds the required 150 updates per second. Amazon SQS Standard queues do not guarantee ordering.
This identifies the only queue type that natively supports the strict ordering constraint without custom sorting logic.
3
Select the solution with the least operational overhead.
Using an Amazon SQS FIFO queue natively fulfills the ordering requirement without requiring additional consumer sorting logic or complex custom applications.
This satisfies the requirement to minimize operational overhead.

Key Concept

Amazon SQS FIFO queues preserve the exact ordering of messages and are ideal for low-to-medium throughput applications requiring strict sequencing.
Estimated Time:45s
Question 1239Question →

A financial services company is deploying a distributed, tightly-coupled risk modeling simulation on a fleet of Amazon EC2 instances in a private subnet. The simulation requires ultra-low latency and high-throughput node-to-node communication. Additionally, the simulation nodes must download several terabytes of historical market data from an Amazon S3 bucket at the beginning of each simulation run. Which combination of actions should a solutions architect recommend to meet these requirements in a high-performing and cost-effective manner? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group within a single Availability Zone.; Create a gateway VPC endpoint for Amazon S3 and associate it with the route tables of the private subnet.

Answer

To meet the requirements, the solutions architect should launch the EC2 instances in a cluster placement group and create a gateway VPC endpoint for Amazon S3.
The correct strategy combines a cluster placement group to ensure ultra-low latency, high-throughput node-to-node networking with a gateway VPC endpoint for Amazon S3 to download large datasets cost-effectively and with high performance, bypassing the NAT gateway.

Step-by-Step Solution

1
Identify the networking requirement for tightly-coupled nodes.
Determine that ultra-low latency and high-throughput node-to-node communication requires a cluster placement group in a single Availability Zone.
Cluster placement groups enable instances to be physically close together on the same network hardware.
2
Determine the data transfer requirements for fetching terabytes of data from Amazon S3.
Identify that routing S3 traffic via a NAT gateway in a private subnet setup would incur high data processing costs and potential throughput limits.
Gateway VPC endpoints provide a direct, free route to Amazon S3 within the AWS network, bypassing the NAT gateway.

Key Concept

High-performing and cost-effective networking for EC2 instances and S3 connections.
Question 1240Question →

A company wants to set up a disaster recovery (DR) solution for its database in a secondary AWS Region. The solution must support a Recovery Point Objective (RPO) of 24 hours and a Recovery Time Objective (RTO) of 24 hours. The company wants to minimize costs and does not want to run any active database instances in the secondary Region during normal operations. Which DR strategy meets these requirements?

Show answer & explanation

Answer: Backup and restore by copying database snapshots to the secondary Region

Answer

Backup and restore by copying database snapshots to the secondary Region
The correct answer is the option that suggests backup and restore by copying snapshots. Backup and Restore is the most cost-effective DR strategy. Backups (snapshots) of the database are copied to a secondary Region. No active database or compute resources run in the secondary Region under normal conditions, which meets the cost requirement. When a disaster occurs, the database is restored from the snapshot, which can comfortably be completed within the 24-hour RTO.

Step-by-Step Solution

1
Analyze the requirements for RPO, RTO, and cost.
The target RPO is 24 hours, the RTO is 24 hours, and there must be zero running compute/database costs in the secondary region.
This establishes the boundaries for evaluating the DR options.
2
Evaluate the Backup and Restore strategy against the constraints.
Backup and Restore keeps only backups in the secondary region and creates resources only when a disaster occurs, which aligns with zero running database instances and is the most cost-effective solution.
To verify if the cheapest option meets the recovery timeline.

Key Concept

Disaster Recovery strategies (Backup & Restore, Pilot Light, Warm Standby, Multi-Site) differ in RTO, RPO, and cost.
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