AWS SAA-C03 真题演练卷 · 第 10 份

Spot Instances are the most cost-effective option for this scenario because they allow you to run EC2 instances at a significantly reduced price compared to On-Demand Instances. Since the batch job is not time-sensitive and can tolerate interruptions, the risk associated with Spot Instances is acceptable. Other options, such as Reserved Instances or Savings Plans, are better suited for predictable, long-term workloads and do not provide the same level of cost savings for this specific use case.

对于这种情况，竞价型实例是最具成本效益的选项，因为与按需实例相比，它们允许您以显着降低的价格运行 EC2 实例。由于批处理作业对时间不敏感并且可以容忍中断，因此与 Spot 实例相关的风险是可以接受的。其他选项（例如预留实例或节省计划）更适合可预测的长期工作负载，并且无法为此特定用例提供相同水平的成本节省。

Spot Instances are the best choice for this scenario because they are highly cost-effective for workloads that can tolerate interruptions, such as batch processing. By taking advantage of unused EC2 capacity, the company can reduce costs significantly compared to On-Demand Instances or Reserved Instances. While Savings Plans offer cost savings, they are not specifically tied to interruptible workloads and would not provide the same level of cost optimization as Spot Instances for this use case.

Spot 实例是这种场景的最佳选择，因为它们对于可以容忍中断的工作负载（例如批处理）来说具有很高的成本效益。与按需实例或预留实例相比，通过利用未使用的 EC2 容量，公司可以显着降低成本。虽然 Savings Plans 可以节省成本，但它们并不专门与可中断的工作负载相关，并且不会为此用例提供与 Spot 实例相同级别的成本优化。

To minimize latency and provide seamless playback, video content should be cached close to the users using Amazon CloudFront. AWS Lambda@Edge enables processing of metadata at the edge locations, close to where the events occur, ensuring low-latency handling of user engagement data. This strategy is optimized for both content delivery and edge processing, meeting the requirements of the scenario.

为了最大限度地减少延迟并提供无缝播放，应使用 Amazon CloudFront 将视频内容缓存在靠近用户的位置。 AWS Lambda@Edge 支持在靠近事件发生位置的边缘位置处理元数据，确保用户参与数据的低延迟处理。该策略针对内容交付和边缘处理进行了优化，满足场景需求。

AWS IoT Greengrass is the ideal solution for this scenario because it allows edge devices to process IoT data locally, reducing latency and enabling near-instant responses. It also integrates seamlessly with AWS services like Amazon S3 for centralized storage and analytics. This makes it a perfect fit for distributed compute strategies in IoT use cases.

AWS IoT Greengrass 是此场景的理想解决方案，因为它允许边缘设备在本地处理 IoT 数据，从而减少延迟并实现近乎即时的响应。它还与 Amazon S3 等 AWS 服务无缝集成，以实现集中存储和分析。这使其非常适合物联网用例中的分布式计算策略。

AWS IoT Greengrass is designed specifically to enable edge processing by allowing IoT devices to run Lambda functions locally, process data in real-time, and operate with intermittent connectivity. This solution aligns perfectly with the requirements of the scenario, where local data processing and reduced reliance on cloud connectivity are essential.

AWS IoT Greengrass 专门设计用于支持边缘处理，允许 IoT 设备在本地运行 Lambda 函数、实时处理数据并通过间歇性连接进行操作。该解决方案完全符合场景的要求，其中本地数据处理和减少对云连接的依赖至关重要。

The best solution for reducing latency and minimizing the transmission of raw video data is to use AWS IoT Greengrass. Greengrass enables IoT devices to process data locally at the edge, allowing the system to perform real-time processing and only send necessary insights to the cloud. This approach is particularly effective for distributed compute strategies in scenarios where low latency is critical.

减少延迟并最大限度地减少原始视频数据传输的最佳解决方案是使用 AWS IoT Greengrass。 Greengrass 使物联网设备能够在边缘本地处理数据，从而使系统能够执行实时处理并仅将必要的见解发送到云端。在低延迟至关重要的场景中，这种方法对于分布式计算策略特别有效。

AWS Snowball Edge is the best solution for this use case because it provides both local compute and storage capabilities, enabling the company to process IoT sensor data with low latency in a remote location. It is specifically designed for environments with limited or no internet connectivity, and it allows the data to be synced to AWS for further processing and storage when connectivity is available.

AWS Snowball Edge 是此用例的最佳解决方案，因为它提供本地计算和存储功能，使公司能够在远程位置以低延迟处理物联网传感器数据。它专为互联网连接有限或没有互联网连接的环境而设计，它允许将数据同步到 AWS，以便在连接可用时进行进一步处理和存储。

AWS Snowball Edge is the most suitable choice for the given scenario because it is designed to operate in remote or disconnected environments. It provides local compute and storage capabilities, allowing workloads to run locally without internet connectivity. AWS Outposts, while offering hybrid functionality, requires continuous connectivity to AWS, and other options like AWS Direct Connect and Amazon EC2 are not designed for disconnected or remote environments.

AWS Snowball Edge 是给定场景的最合适选择，因为它设计用于在远程或断开连接的环境中运行。它提供本地计算和存储功能，允许工作负载在没有互联网连接的情况下本地运行。 AWS Outposts 虽然提供混合功能，但需要与 AWS 持续连接，而 AWS Direct Connect 和 Amazon EC2 等其他选项并非专为断开连接或远程环境而设计。

AWS Snowball Edge is specifically designed for hybrid environments where internet connectivity is unreliable or unavailable. It enables local data processing and can sync with AWS later, making it the optimal solution for the company's requirements. The other options either depend on stable internet connectivity or do not address the need for local data processing.

AWS Snowball Edge 专为互联网连接不可靠或不可用的混合环境而设计。它支持本地数据处理，并可以稍后与 AWS 同步，使其成为满足公司需求的最佳解决方案。其他选项要么依赖于稳定的互联网连接，要么无法满足本地数据处理的需求。

AWS Outposts is specifically designed to extend AWS resources to customer-operated on-premises environments, providing low latency, consistent AWS service performance, and integration with AWS. This makes it the ideal choice for running applications that require local processing and seamless access to AWS services in environments with poor connectivity to AWS Regions.

AWS Outposts 专门设计用于将 AWS 资源扩展到客户运营的本地环境，提供低延迟、一致的 AWS 服务性能以及与 AWS 的集成。这使其成为在与 AWS 区域连接较差的环境中运行需要本地处理和无缝访问 AWS 服务的应用程序的理想选择。

Compute-optimized instances, such as C6g, are designed for high-performance workloads that require significant compute power and low latency. These instances are ideal for high-performance computing (HPC) applications, as they provide enhanced networking capabilities and optimized CPU performance. Other instance types, such as memory-optimized or general-purpose instances, are not specialized for compute-intensive tasks.

计算优化实例（例如 C6g）专为需要大量计算能力和低延迟的高性能工作负载而设计。这些实例非常适合高性能计算 (HPC) 应用程序，因为它们提供增强的网络功能和优化的 CPU 性能。其他实例类型（例如内存优化实例或通用实例）并非专门用于计算密集型任务。

The application described in the scenario requires high computational power and low latency for processing, which aligns with the capabilities of Compute Optimized instances such as the C5 family. Compute Optimized instances are specifically designed to deliver high performance for compute-intensive workloads, making them the best choice for this use case.

该场景中描述的应用程序需要高计算能力和低处理延迟，这与 C5 系列等计算优化实例的功能相一致。计算优化实例专门为计算密集型工作负载提供高性能而设计，使其成为此用例的最佳选择。

Compute-optimized EC2 instances, such as the C6g family, are designed for applications requiring high-performance CPUs with low latency. This makes them an excellent fit for compute-intensive workloads, such as real-time multiplayer gaming. Other instance families, like memory-optimized (R6g) and general-purpose (M6a), are better suited for workloads with different resource needs, while burstable instances (T4g) are not designed to handle sustained high-performance requirements.

计算优化的 EC2 实例（例如 C6g 系列）专为需要高性能 CPU 和低延迟的应用程序而设计。这使得它们非常适合计算密集型工作负载，例如实时多人游戏。其他实例系列，例如内存优化型 (R6g) 和通用型 (M6a)，更适合具有不同资源需求的工作负载，而突发型实例 (T4g) 则无法满足持续的高性能要求。

Memory Optimized (R5) instances are the best choice for memory-intensive applications that require high memory capacity for in-memory computations. These instances provide the optimal memory-to-vCPU ratio, ensuring efficient processing of large datasets while keeping costs in check. Other instance families, like Compute Optimized or Storage Optimized, do not prioritize high memory capacity and would not be a cost-effective or performance-efficient choice for this workload.

内存优化 (R5) 实例是需要高内存容量进行内存计算的内存密集型应用程序的最佳选择。这些实例提供最佳的内存与 vCPU 比率，确保高效处理大型数据集，同时控制成本。其他实例系列（例如计算优化型或存储优化型）不会优先考虑高内存容量，因此对于此工作负载而言不是经济高效或性能高效的选择。

The best solution is to refactor the application into AWS Lambda functions and use Amazon API Gateway for routing. This approach optimizes compute utilization by adopting a serverless architecture, which automatically scales with traffic and eliminates the need to manage infrastructure. It also enables the application to be decoupled into microservices, which aligns with the company's goal.

最好的解决方案是将应用程序重构为 AWS Lambda 函数并使用 Amazon API Gateway 进行路由。这种方法通过采用无服务器架构来优化计算利用率，该架构可以根据流量自动扩展，并且无需管理基础设施。它还使应用程序能够解耦为微服务，这符合公司的目标。

To optimize compute utilization, the company should prioritize serverless computing, containerization, and microservices architecture. AWS Lambda with Amazon API Gateway provides a fully managed serverless solution. Amazon ECS with AWS Fargate allows running containers without managing server infrastructure. Breaking the application into microservices and using Amazon EKS ensures modularity and scalability. Solutions like EC2 Auto Scaling with larger instances or Amazon Lightsail are less effective for the stated requirements.

为了优化计算利用率，公司应优先考虑无服务器计算、容器化和微服务架构。 AWS Lambda 与 Amazon API Gateway 提供完全托管的无服务器解决方案。 Amazon ECS 与 AWS Fargate 允许运行容器而无需管理服务器基础设施。将应用程序分解为微服务并使用 Amazon EKS 确保了模块化和可扩展性。具有较大实例的 EC2 Auto Scaling 或 Amazon Lightsail 等解决方案对于规定的要求效果较差。

Amazon ECS with Fargate and AWS Lambda are both serverless solutions that abstract server management and automatically scale to handle unpredictable traffic patterns. ECS with Fargate is ideal for containerized workloads, while AWS Lambda is perfect for event-driven applications. Both options minimize operational overhead and optimize compute utilization, aligning with the company's requirements for flexibility and seamless scaling.

带 Fargate 的 Amazon ECS 和 AWS Lambda 都是无服务器解决方案，可抽象服务器管理并自动扩展以处理不可预测的流量模式。具有 Fargate 的 ECS 非常适合容器化工作负载，而 AWS Lambda 则非常适合事件驱动的应用程序。这两种选项都最大限度地减少了运营开销并优化了计算利用率，符合公司对灵活性和无缝扩展的要求。

Amazon ECS with AWS Fargate is the best option for this scenario because it allows the startup to package microservices into containers and deploy them without managing servers. Fargate automatically allocates the right amount of compute resources for each container, enabling independent scaling of microservices and optimizing compute utilization. This minimizes operational overhead and costs, making it an ideal solution for unpredictable traffic patterns.

Amazon ECS 和 AWS Fargate 是这种场景的最佳选择，因为它允许初创公司将微服务打包到容器中并部署它们，而无需管理服务器。 Fargate 自动为每个容器分配适量的计算资源，从而实现微服务的独立扩展并优化计算利用率。这最大限度地减少了运营开销和成本，使其成为不可预测的流量模式的理想解决方案。

Dynamic scaling policies in an Auto Scaling group allow you to automatically adjust the number of EC2 instances based on real-time metrics, such as CPU utilization or application load. This strategy is ideal for handling highly variable traffic patterns efficiently, as it ensures the application scales out during spikes and scales in when traffic decreases, minimizing costs. Other options either require manual intervention, are designed for predictable traffic patterns, or do not address scaling at all.

Auto Scaling 组中的动态扩展策略允许您根据实时指标（例如 CPU 利用率或应用程序负载）自动调整 EC2 实例的数量。该策略非常适合有效处理高度变化的流量模式，因为它可以确保应用程序在高峰期间横向扩展，并在流量下降时横向收缩，从而最大限度地降低成本。其他选项要么需要手动干预，要么是为可预测的流量模式而设计的，要么根本不解决扩展问题。

Dynamic scaling policies with Auto Scaling groups allow the application to adjust its capacity based on real-time demand. By using metrics such as request counts from the ALB target group, the system can automatically add or remove instances as traffic increases or decreases. This ensures optimal performance during high traffic while controlling costs during low traffic periods. Scheduled scaling is less flexible, hibernation is not designed for traffic scaling, and manual intervention is not practical or efficient.

具有 Auto Scaling 组的动态扩展策略允许应用程序根据实时需求调整其容量。通过使用 ALB 目标组的请求计数等指标，系统可以随着流量的增加或减少自动添加或删除实例。这确保了高流量期间的最佳性能，同时在低流量期间控制成本。计划扩展不太灵活，休眠不是为流量扩展而设计的，并且手动干预不实用或不高效。

To handle fluctuating traffic while minimizing costs, you should leverage Auto Scaling with both dynamic and scheduled scaling policies. Dynamic scaling ensures the system responds to real-time traffic changes, while scheduled scaling optimizes for predictable traffic patterns. Additionally, introducing Spot Instances into the Auto Scaling group reduces costs without compromising scalability or availability. EC2 Hibernate and manual management are not suitable strategies for this scenario.

为了处理波动的流量，同时最大限度地降低成本，您应该利用具有动态和计划扩展策略的 Auto Scaling。动态扩展可确保系统响应实时流量变化，而计划扩展可针对可预测的流量模式进行优化。此外，将 Spot 实例引入 Auto Scaling 组可以降低成本，而不会影响可扩展性或可用性。 EC2 Hibernate 和手动管理不适合此场景。

The correct answer is to use Auto Scaling with a launch template and an Amazon Elastic Load Balancer (ELB). Auto Scaling allows the application to automatically increase or decrease the number of EC2 instances based on demand, ensuring cost efficiency and high availability. ELB ensures that traffic is distributed evenly across instances, providing better performance during high traffic periods. Hibernation is not suitable for Auto Scaling, as it is used for different use cases, like quickly resuming long-running instances.

正确的答案是将 Auto Scaling 与启动模板和 Amazon Elastic Load Balancer (ELB) 结合使用。 Auto Scaling 允许应用程序根据需求自动增加或减少 EC2 实例的数量，从而确保成本效率和高可用性。 ELB 确保流量在实例之间均匀分布，从而在高流量期间提供更好的性能。休眠不适合 Auto Scaling，因为它用于不同的用例，例如快速恢复长时间运行的实例。

Amazon DynamoDB with on-demand capacity mode is ideal for applications with unpredictable or highly variable workloads. It automatically scales to meet demand and charges only for the read and write capacity actually consumed, making it the most cost-optimized solution for the given scenario.

具有按需容量模式的 Amazon DynamoDB 非常适合具有不可预测或高度可变工作负载的应用程序。它会自动扩展以满足需求，并且仅根据实际消耗的读写容量收费，使其成为给定场景中成本最优化的解决方案。

For unpredictable traffic spikes with frequent read/write operations, Amazon DynamoDB with on-demand capacity mode is the most cost-effective solution. It provides automatic scaling that aligns with the e-commerce application's workload, ensuring no overprovisioning or underutilization. Additionally, DynamoDB's durability and high availability features meet the requirements for the application.

对于频繁读/写操作的不可预测的流量峰值，具有按需容量模式的 Amazon DynamoDB 是最具成本效益的解决方案。它提供与电子商务应用程序的工作负载相匹配的自动扩展，确保不会出现过度配置或利用率不足的情况。此外，DynamoDB 的耐用性和高可用性功能满足应用程序的要求。

Amazon Aurora Serverless v2 is the most cost-optimized solution for this scenario because it provides the ability to scale database capacity automatically based on workload demands and charges only for the consumed capacity. This avoids the need to provision resources for peak traffic, reducing overall costs while maintaining scalability and high availability.

Amazon Aurora Serverless v2 是此场景中成本最优化的解决方案，因为它提供了根据工作负载需求自动扩展数据库容量的能力，并且仅对消耗的容量进行收费。这避免了为高峰流量调配资源的需要，降低了总体成本，同时保持了可扩展性和高可用性。

Using Read Replicas with Amazon RDS MySQL is the most cost-optimized solution in this scenario. Read Replicas allow the company to handle increased read traffic by distributing it across multiple replicas without scaling up the primary database. This approach is cost-efficient and minimizes operational overhead compared to alternatives like scaling up the instance or migrating to a more complex database solution.

将只读副本与 Amazon RDS MySQL 结合使用是此场景中成本最优化的解决方案。只读副本允许公司通过将读取流量分布到多个副本来处理增加的读取流量，而无需扩展主数据库。与扩展实例或迁移到更复杂的数据库解决方案等替代方案相比，这种方法具有成本效益，并且可以最大限度地减少运营开销。

To improve fault tolerance and availability, enabling Multi-AZ deployment for Amazon RDS ensures automatic failover during database outages, and using Auto Scaling for the EC2 instances ensures the web tier can handle varying loads and recover from instance failures. Other options, while useful in certain contexts, do not directly address the scenario's requirements.

为了提高容错能力和可用性，为 Amazon RDS 启用多可用区部署可确保数据库中断期间的自动故障转移，并且对 EC2 实例使用 Auto Scaling 可确保 Web 层可以处理变化的负载并从实例故障中恢复。其他选项虽然在某些情况下有用，但不能直接满足场景的要求。

To achieve high availability and resilience to an Availability Zone failure, you should use an Auto Scaling group to ensure a minimum of two instances are running and deploy them across multiple Availability Zones. An Elastic Load Balancer is essential to distribute traffic across multiple instances and Availability Zones, ensuring traffic is routed to healthy instances even if one Availability Zone goes down.

为了实现高可用性和对可用区故障的恢复能力，您应该使用 Auto Scaling 组来确保至少有两个实例正在运行，并将它们部署在多个可用区中。 Elastic Load Balancer 对于跨多个实例和可用区分配流量至关重要，确保即使一个可用区出现故障，流量也能路由到运行状况良好的实例。

To ensure secure access to an S3 bucket by an ALB, it is best to use a VPC endpoint for S3. A bucket policy can then be configured to allow access only from this VPC endpoint, ensuring that traffic remains within the AWS network and is securely restricted to the load balancer. This approach avoids the challenges of dynamically changing IPs and aligns with AWS best practices for securing S3 access.

为了确保 ALB 对 S3 存储桶的安全访问，最好使用 S3 的 VPC 终端节点。然后，可以将存储桶策略配置为仅允许从此 VPC 终端节点进行访问，确保流量保留在 AWS 网络内并安全地限制在负载均衡器中。这种方法避免了动态更改 IP 的挑战，并符合保护 S3 访问的 AWS 最佳实践。

The correct answer is to move the uploaded files to Amazon S3 and use AWS Lambda to process them. This solution decouples the file processing workload from the web servers, reducing their CPU utilization and improving overall application performance. Additionally, it leverages serverless services, which are scalable and cost-effective, and requires minimal modifications to the existing architecture.

正确的答案是将上传的文件移动到 Amazon S3 并使用 AWS Lambda 来处理它们。该解决方案将文件处理工作负载与 Web 服务器分离，降低了 CPU 利用率并提高了整体应用程序性能。此外，它还利用可扩展且经济高效的无服务器服务，并且需要对现有架构进行最少的修改。

To track and allocate costs to different departments, you need a combination of cost allocation tags and consolidated billing. Cost allocation tags enable tagging of resources to categorize costs, while consolidated billing allows grouping of accounts for unified cost management. Other tools like AWS Budgets and CUR provide additional insights and monitoring but are not directly tied to cost allocation or grouping.

要跟踪成本并将其分配给不同部门，您需要成本分配标签和合并计费的组合。成本分配标签可以对资源进行标记以对成本进行分类，而合并计费则可以对帐户进行分组以进行统一的成本管理。 AWS Budgets 和 CUR 等其他工具提供额外的见解和监控，但不直接与成本分配或分组相关。

To meet the requirements of tracking project-specific costs across accounts and consolidating billing, you need to enable cost allocation tags for granular tracking and set up consolidated billing in AWS Organizations for centralized payment management. Additionally, the AWS Cost and Usage Report provides detailed insights into spending patterns across accounts, complementing these efforts for effective cost management.

为了满足跨账户跟踪项目特定成本并合并计费的需求，您需要启用成本分配标签进行精细跟踪，并在 AWS Organizations 中设置合并计费以进行集中支付管理。此外，AWS 成本和使用情况报告提供了跨账户支出模式的详细见解，补充了这些有效成本管理的努力。

To effectively manage and allocate costs across projects and teams within a multi-account setup, you should enable cost allocation tags to track spending, use AWS Cost Explorer for cost and usage analysis, and enable consolidated billing in AWS Organizations to streamline billing data for all member accounts. These features together provide a comprehensive solution for monitoring and managing costs in an AWS environment.

要在多账户设置中跨项目和团队有效管理和分配成本，您应该启用成本分配标签来跟踪支出，使用 AWS Cost Explorer 进行成本和使用情况分析，并在 AWS Organizations 中启用合并账单以简化所有成员账户的账单数据。这些功能共同提供了用于监控和管理 AWS 环境中的成本的全面解决方案。

To track costs by department, the finance team should enable cost allocation tags and use AWS Cost Explorer to analyze costs based on these tags. Consolidated billing under AWS Organizations allows a unified view of costs across multiple accounts, which is essential for accurate tracking. While AWS Budgets, Cost Anomaly Detection, and RI sharing are valuable cost management tools, they do not directly address the requirement of categorizing and tracking costs by department.

要按部门跟踪成本，财务团队应启用成本分配标签并使用 AWS Cost Explorer 根据这些标签分析成本。 AWS Organizations 下的合并计费允许跨多个帐户统一查看成本，这对于准确跟踪至关重要。虽然 AWS 预算、成本异常检测和 RI 共享是有价值的成本管理工具，但它们并不能直接满足按部门分类和跟踪成本的要求。

To meet the company's requirements, they should use AWS Budgets to set specific spending limits and receive alerts when those limits are approached or exceeded. For analyzing historical spending trends, Cost Explorer is the best choice as it provides visualizations of historical data. Alternatively, the AWS Cost and Usage Report can provide granular cost and usage details, which can complement budget planning efforts.

为了满足公司的要求，他们应该使用 AWS Budgets 来设置特定的支出限额，并在接近或超过这些限额时收到警报。对于分析历史支出趋势，Cost Explorer 是最佳选择，因为它提供历史数据的可视化。或者，AWS 成本和使用情况报告可以提供详细的成本和使用情况详细信息，这可以补充预算规划工作。

To meet the requirements, the company should use multiple AWS cost management tools. AWS Cost Explorer is ideal for forecasting costs and analyzing usage patterns. AWS Budgets can be used to set up alerts when spending exceeds a defined threshold for cost control. Additionally, the AWS Cost and Usage Report provides granular data for in-depth analysis, complementing the use of Cost Explorer.

为了满足要求，公司应该使用多种 AWS 成本管理工具。 AWS Cost Explorer 非常适合预测成本和分析使用模式。 AWS 预算可用于在支出超过定义的成本控制阈值时设置警报。此外，AWS 成本和使用情况报告还提供用于深入分析的精细数据，对 Cost Explorer 的使用进行了补充。

To monitor AWS spending and set alerts when costs exceed a budget, AWS Budgets is the ideal solution. To analyze historical usage and predict future costs, Cost Explorer is the appropriate tool. Together, these tools address both the company's requirements.

要监控 AWS 支出并在成本超出预算时设置警报，AWS Budgets 是理想的解决方案。要分析历史使用情况并预测未来成本，Cost Explorer 是合适的工具。这些工具共同满足了公司的需求。

To effectively monitor and manage cloud spending, the company should use AWS Cost Explorer for analyzing historical cost trends, AWS Budgets for setting alerts when spending exceeds thresholds, and AWS Cost and Usage Report for obtaining detailed data on their AWS costs and usage. These tools together address all the requirements of the finance team.

为了有效监控和管理云支出，公司应使用 AWS Cost Explorer 来分析历史成本趋势，使用 AWS Budgets 在支出超过阈值时设置警报，并使用 AWS Cost and Use Report 来获取有关其 AWS 成本和使用情况的详细数据。这些工具共同满足财务团队的所有要求。

In this scenario, the company needs a solution that improves performance and ensures cache freshness when product details are updated. A write-through caching strategy with Amazon ElastiCache is the best approach because it updates the cache immediately whenever the database is updated, ensuring data consistency and reducing latency for API requests. Other options either do not ensure cache freshness or do not provide the same level of performance improvement.

在这种场景下，该公司需要一种解决方案，能够提高性能并在产品详细信息更新时确保缓存新鲜度。使用 Amazon ElastiCache 的直写式缓存策略是最好的方法，因为它会在数据库更新时立即更新缓存，从而确保数据一致性并减少 API 请求的延迟。其他选项要么不能确保缓存新鲜度，要么不能提供相同级别的性能改进。

The best caching strategy for this scenario is to use Amazon ElastiCache with a Redis cluster, as it provides a fast, in-memory data store that can handle high read traffic while reducing the load on the backend database. This option is cost-effective and reliable, aligning with the company's need to optimize performance for read-heavy queries. Other options either require specific conditions (like DynamoDB for DAX), involve unnecessary complexity (custom application caching), or are unsuitable for the use case (S3 for dynamic data).

此场景的最佳缓存策略是将 Amazon ElastiCache 与 Redis 集群结合使用，因为它提供了快速的内存数据存储，可以处理高读取流量，同时减少后端数据库的负载。此选项经济高效且可靠，符合公司优化读取密集型查询性能的需求。其他选项要么需要特定条件（例如用于 DAX 的 DynamoDB），要么涉及不必要的复杂性（自定义应用程序缓存），要么不适合用例（用于动态数据的 S3）。

Amazon ElastiCache with Redis is the most appropriate solution in this scenario because it provides an in-memory caching layer that can store frequently accessed data, significantly reducing database load and query latency. This is a common strategy for improving application performance when dealing with repeated database queries.

Amazon ElastiCache 搭配 Redis 是这种场景下最合适的解决方案，因为它提供了内存缓存层，可以存储经常访问的数据，显着减少数据库负载和查询延迟。这是处理重复的数据库查询时提高应用程序性能的常见策略。

To meet the requirement of automatically invalidating the cache when data in the database changes, the most appropriate solution is to use Amazon ElastiCache with DynamoDB Streams. DynamoDB Streams can capture database changes in real-time, and you can use them to trigger cache invalidation logic, ensuring the cache remains up-to-date with the database. Other options, such as write-through or lazy-loading strategies, do not provide real-time invalidation for changes made directly to the database.

为了满足数据库中的数据发生变化时自动使缓存失效的需求，最合适的解决方案是将 Amazon ElastiCache 与 DynamoDB Streams 结合使用。 DynamoDB Streams 可以实时捕获数据库更改，您可以使用它们来触发缓存失效逻辑，确保缓存与数据库保持最新。其他选项（例如直写或延迟加载策略）不为直接对数据库所做的更改提供实时失效。

To enforce a 7-year retention policy with no manual intervention and ensure the data cannot be deleted before the retention period ends, Amazon S3 Object Lock is the appropriate solution. Compliance mode provides the highest level of protection, as even AWS account administrators cannot delete the data. Governance mode offers retention enforcement but allows account administrators to override the lock if necessary.

要在无需人工干预的情况下执行 7 年保留策略，并确保数据在保留期结束前无法被删除，Amazon S3 对象锁是合适的解决方案。合规模式提供最高级别的保护，因为即使是 AWS 账户管理员也无法删除数据。治理模式提供保留强制，但允许帐户管理员在必要时覆盖锁定。

S3 Lifecycle policies are specifically designed to manage the lifecycle of objects in S3. By transitioning objects to lower-cost storage classes like S3 Glacier and automatically deleting them after the retention period, you can optimize costs and meet compliance requirements without manual intervention or custom code.

S3 生命周期策略专门用于管理 S3 中对象的生命周期。通过将对象转换为 S3 Glacier 等成本较低的存储类并在保留期后自动删除它们，您可以优化成本并满足合规性要求，而无需手动干预或自定义代码。

To meet the regulatory requirement of 7 years of data retention with immediate access, S3 Intelligent-Tiering provides a cost-effective solution. It automatically optimizes storage costs over time while ensuring that the logs are immediately accessible. Configuring a lifecycle policy to delete the data after 7 years ensures compliance with the retention policy.

为了满足数据保留 7 年并可立即访问的监管要求，S3 智能分层提供了一种经济高效的解决方案。随着时间的推移，它会自动优化存储成本，同时确保可以立即访问日志。配置生命周期策略以在 7 年后删除数据可确保符合保留策略。

For long-term retention of logs at the lowest cost, S3 Glacier Deep Archive is the optimal storage class. Using a Lifecycle policy automates data deletion after 5 years, ensuring compliance with retention policies. Additionally, enabling S3 Object Lock in Compliance mode ensures that the logs cannot be accidentally deleted during the retention period, providing the required protection.

为了以最低的成本长期保留日志，S3 Glacier Deep Archive 是最佳的存储类别。使用生命周期策略可在 5 年后自动删除数据，确保遵守保留策略。此外，在合规模式下启用 S3 对象锁定可确保日志在保留期内不会被意外删除，从而提供所需的保护。

The key requirements in this scenario are predictable performance, the ability to scale storage and throughput independently, and support for steady workloads with occasional traffic spikes. Amazon DynamoDB with auto-scaling enabled addresses these needs effectively. It allows automatic scaling of read and write throughput based on demand and supports seamless handling of traffic fluctuations. Other options either do not meet these requirements or are designed for different use cases.

此场景中的关键要求是可预测的性能、独立扩展存储和吞吐量的能力以及支持偶尔出现流量峰值的稳定工作负载。启用自动缩放功能的 Amazon DynamoDB 可以有效满足这些需求。它允许根据需求自动扩展读写吞吐量，并支持无缝处理流量波动。其他选项要么不满足这些要求，要么是针对不同的用例而设计的。

On-demand capacity mode is specifically designed for use cases with unpredictable traffic patterns, as it eliminates the need to manually manage capacity or set up Auto Scaling policies. It adjusts capacity automatically to handle sudden spikes in traffic, ensuring consistent performance without throttling errors.

按需容量模式专为流量模式不可预测的用例而设计，因为它无需手动管理容量或设置 Auto Scaling 策略。它会自动调整容量以处理突然的流量峰值，确保一致的性能而不会出现限制错误。

DynamoDB On-Demand capacity mode is the best choice for workloads with unpredictable and highly variable traffic patterns, as it eliminates the need for manual capacity planning or pre-provisioning of capacity units. It automatically scales up or down based on traffic, optimizing both performance and cost-efficiency.

DynamoDB 按需容量模式是具有不可预测且高度可变流量模式的工作负载的最佳选择，因为它消除了手动容量规划或预先配置容量单位的需要。它根据流量自动扩展或缩小，从而优化性能和成本效率。

Database capacity planning involves evaluating key factors like read and write traffic, expected storage size, and concurrent connections to ensure the database can handle the workload. Additionally, selecting the appropriate storage type (e.g., Provisioned IOPS for high I/O performance) is crucial for optimal database performance. Considerations like availability zones are important for durability and availability but do not directly impact capacity planning.

数据库容量规划涉及评估读写流量、预期存储大小和并发连接等关键因素，以确保数据库能够处理工作负载。此外，选择适当的存储类型（例如，实现高 I/O 性能的预配置 IOPS）对于优化数据库性能至关重要。可用性区域等考虑因素对于持久性和可用性很重要，但不会直接影响容量规划。