SAA-C03（日语） Practice Exam — SAA-C03:Solutions Architect - Associate

The scenario requires the fastest possible aggregation of large volumes of data from global sites to a single central S3 bucket, with minimal operational complexity, and all sites have high-speed internet access. The suggested solution leverages native, fully managed S3 features that directly address all requirements. S3 Transfer Acceleration optimizes long-distance data transfers by routing traffic from global client sites through AWS CloudFront edge locations, using AWS's private low-latency global backbone network instead of the public internet to drastically reduce transfer latency for geographically dispersed users. Multipart uploads are purpose-built for large object transfers, splitting data into independent parts that can be uploaded in parallel to improve throughput, enable resumable uploads after partial failures, and efficiently handle the 500 GB daily data volume per site. This solution requires no intermediate infrastructure, cross-region configuration, or ongoing management tasks, so it meets both the speed and minimal operational complexity requirements. Option Analysis: A. Correct. S3 Transfer Acceleration is explicitly designed for fast global uploads to a central S3 bucket, eliminating the need for intermediate storage or replication workflows. Multipart uploads efficiently handle the large daily data volume, and the entire solution is fully managed with no additional operational overhead, perfectly aligning with both the speed and low complexity requirements of the scenario. B. Incorrect. While uploading to a local regional S3 bucket is fast for the site, S3 Cross-Region Replication is asynchronous, which introduces unpredictable latency before objects appear in the destination bucket. This solution also requires managing multiple regional S3 buckets, replication configuration, IAM permissions for replication, and post-replication object deletion, which significantly increases operational complexity compared to direct accelerated uploads. C. Incorrect. AWS Snowball Edge is an offline data transfer service intended for use cases with limited internet connectivity or datasets that are too large to transfer over the network in a reasonable timeframe. The scenario states all sites have high-speed internet, so using physical Snowball Edge devices daily introduces unnecessary shipping delays and extreme operational overhead, failing to meet the "as quickly as possible" requirement. D. Incorrect. This solution adds multiple unnecessary layers of infrastructure including EC2 instances, EBS volumes, snapshot scheduling, cross-region snapshot copies, and volume restores before data can be loaded into the target S3 bucket. This approach is extremely operationally complex, requires ongoing management of compute and storage resources, introduces significant transfer latency, and is far less efficient than native S3 transfer features. Key Concepts: 1. S3 Transfer Acceleration: A fully managed S3 feature that uses AWS edge locations to route transfer traffic over AWS's private high-speed global backbone, reducing latency for long-distance uploads and downloads to S3 buckets with no additional infrastructure management required for end users. 2. S3 Multipart Upload: A native S3 transfer capability that splits large objects into independent, parallel-uploadable parts, improving transfer throughput for large datasets, enabling resumable uploads after partial failures, and recommended for all objects larger than 100 MB. 3. S3 Cross-Region Replication (CRR): An asynchronous S3 feature that automatically copies objects between S3 buckets in different AWS Regions, designed for disaster recovery, compliance, and data locality use cases, but introduces replication latency and additional operational configuration overhead. References: Amazon S3 Transfer Acceleration, https://docs.aws.amazon.com/AmazonS3/latest/userguide/transfer-acceleration.html Uploading and copying objects using multipart upload, https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html

The scenario requires analysis of JSON formatted logs already stored in Amazon S3, with simple on-demand queries, minimal changes to existing architecture, and the lowest possible operational overhead. The correct answer uses Amazon Athena, which is a serverless, interactive query service that natively supports querying data directly in S3 without requiring data movement or dedicated infrastructure provisioning. This eliminates the need to modify the existing S3 storage layer, requires no ongoing cluster management, and supports standard SQL queries for ad-hoc analysis, perfectly aligning with all stated requirements. This maps directly to the SAA-C03 domain of designing cost-effective, high-performing, and low-overhead analytics architectures. Option Analysis: A. Incorrect. Amazon Redshift is a managed data warehouse service that requires loading data from S3 into Redshift storage to run queries. This adds unnecessary data movement, requires provisioning and managing Redshift clusters, and introduces significant operational overhead compared to querying data in place. It also requires changes to the existing architecture to facilitate data loading, so it does not meet the minimal changes and low overhead requirements. B. Incorrect. The logs are already stored in S3, so ingesting them into Amazon CloudWatch Logs requires additional data transfer and integration work, which violates the minimal architecture change requirement. While CloudWatch Logs supports querying logs, it is designed for real-time log monitoring rather than ad-hoc analysis of existing S3-resident logs, and the process adds unneeded operational overhead. C. Correct. Amazon Athena directly queries structured and semi-structured data including JSON stored in S3 with no data movement required. It is fully serverless, so there is no infrastructure to provision, manage, or maintain, delivering the lowest operational overhead. It supports standard SQL for on-demand queries, requires zero changes to the existing S3 log storage architecture, and aligns perfectly with all scenario requirements. D. Incorrect. Using AWS Glue to catalog logs and a transient EMR Spark cluster adds unnecessary operational complexity, including configuring Glue crawlers, managing EMR cluster provisioning and termination, and maintaining Spark query jobs. This solution has significantly higher operational overhead than using Athena directly, so it does not meet the least operational overhead requirement. Key Concepts: 1. Amazon Athena Core Capabilities: Athena is a serverless interactive query service that lets users analyze data directly in Amazon S3 using standard SQL, with no infrastructure to manage, pay-per-query pricing, and native support for common formats including JSON, CSV, Parquet, and ORC. This is a core SAA-C03 topic for ad-hoc analytics use cases. 2. In-Place Analytics for S3: AWS analytics services prioritize in-place processing of S3-resident data to eliminate unnecessary data movement, reduce operational overhead, and minimize changes to existing storage architectures, which is a key design principle for low-overhead analytics solutions tested in the SAA-C03 exam. 3. Operational Overhead Optimization: For serverless use cases, eliminating the need to provision, scale, and manage compute infrastructure drastically reduces operational overhead, which is a core evaluation criteria for architecture selection in the SAA-C03 certification. References: What is Amazon Athena?, https://docs.aws.amazon.com/athena/latest/ug/what-is.html Big Data Analytics Options on AWS, https://docs.aws.amazon.com/whitepapers/latest/big-data-analytics-options/welcome.html

The scenario requires restricting S3 bucket access exclusively to users from accounts within an AWS Organization, with minimal operational overhead. The core requirement is a solution that automatically applies to all current and future accounts in the organization without ongoing administrative work. The aws:PrincipalOrgID global condition key is purpose-built for this use case, as it validates that any requesting principal (user, role, federated identity) belongs to the specified organization ID, regardless of which account in the organization they are from. This solution requires only a one-time update to the S3 bucket policy, with no subsequent changes needed as accounts are added, removed, or modified in the organization, directly delivering the lowest operational overhead while meeting the access control requirement. Option Analysis: A. Correct. The aws:PrincipalOrgID global IAM condition key checks if the requesting principal is associated with an account that is a member of the specified AWS Organization ID. This configuration requires a single, one-time addition to the S3 bucket policy, and automatically applies to all existing and future accounts in the organization with no ongoing administrative effort. This perfectly aligns with the requirement for least operational overhead while restricting access only to organization users. B. Incorrect. Creating an OU per department adds unnecessary upfront administrative work, and the aws:PrincipalOrgPaths condition key is designed to restrict access to specific OUs rather than the entire organization. This solution requires ongoing management of OU structure and policy updates if OUs are modified, resulting in higher operational overhead than Option A, and is overly complex for the requirement to grant access to all organization accounts. C. Incorrect. Monitoring CloudTrail events for organization account changes and updating the bucket policy accordingly requires ongoing operational effort, including maintaining CloudTrail monitoring, event processing, and policy update workflows (either manual or automated). This approach introduces significant overhead, risk of access gaps if updates are delayed, and is far less efficient than using the native PrincipalOrgID condition key. D. Incorrect. Tagging every user across all organization accounts that requires access imposes extreme ongoing operational overhead, as tags must be applied consistently to all new users, updated when user access changes, and enforced across all accounts in the organization. This approach is error-prone, does not automatically cover new users or accounts, and results in far higher administrative effort than Option A. Key Concepts: 1. IAM Global Condition Keys for AWS Organizations: These native IAM condition keys (including aws:PrincipalOrgID) enable access control based on principal membership in an AWS Organization, eliminating the need to explicitly list individual account IDs or principals in resource policies, and automatically adjusting to changes in organization account membership. 2. S3 Bucket Policies: Resource-based access policies for Amazon S3 that apply directly to buckets and their objects, supporting cross-account access control and IAM condition keys to granularly restrict access without modifying IAM policies in individual AWS accounts across the organization. 3. AWS Organizations Multi-Account Access Optimization: Integration between AWS Organizations and IAM simplifies cross-account access management at scale, reducing operational overhead by replacing manual policy updates for account changes with native, dynamic access validation based on organization membership. References: IAM JSON Policy Elements: Condition Keys, https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_condition-keys.html#condition-keys-principalorgid Amazon S3 User Guide: Bucket policy examples for AWS Organizations, https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-bucket-policies-orgs.html

This scenario requires private network layer connectivity between an EC2 instance hosted in a VPC and Amazon S3, with no internet routing permitted for S3 traffic. This aligns with the SAA-C03 core domains of Designing Secure Architectures and Designing High-Performing Architectures, as the solution must eliminate public internet exposure for S3 traffic while maintaining reliable, low-latency connectivity. Gateway VPC endpoints are purpose-built for this exact use case for Amazon S3 and Amazon DynamoDB, as they route all traffic between the VPC and the target AWS service entirely over the AWS private backbone. No internet gateway, NAT device, or public IP address is required for the EC2 instance, directly meeting the requirement of S3 access without internet connectivity. Option Analysis: A. Correct. Gateway VPC endpoints for Amazon S3 are free, highly available virtual devices that enable private connectivity from a VPC to S3. When provisioned, you add a route to your VPC route tables pointing to S3's public IP prefix list via the gateway endpoint, so all S3 traffic from the VPC stays on the AWS private network, with no need for internet access. This exactly fulfills all requirements in the scenario. B. Incorrect. Streaming logs to CloudWatch Logs first does not resolve the core network connectivity requirement: the EC2 instance would still need private or public connectivity to CloudWatch Logs to stream logs, and this approach modifies the existing application workflow instead of providing direct private connectivity to S3 as required. C. Incorrect. An instance profile is an IAM identity that grants authorization permissions for the EC2 instance to call AWS service APIs including S3, but it does not provide network connectivity to S3. Even with a correctly configured instance profile, the EC2 instance cannot reach S3 if there is no valid network route, so this does not address the core requirement. D. Incorrect. While it is technically possible to front S3 with an API Gateway API using PrivateLink, this is an unnecessarily complex, costly, and non-standard solution. AWS natively supports direct private connectivity to S3 via gateway VPC endpoints, so this option is not appropriate for the scenario. Key Concepts: 1. VPC Endpoint Types: Gateway endpoints are free, route-table integrated endpoints supported for Amazon S3 and Amazon DynamoDB, enabling private access from a VPC to these services without public internet routing. Interface endpoints (powered by AWS PrivateLink) are another endpoint type for most other AWS services, with per-hour and per-GB costs, that use elastic network interfaces deployed in VPC subnets. 2. Network vs Authorization Controls: Access to AWS services requires two separate layers of control: network connectivity (governed by route tables, VPC endpoints, security groups, and network ACLs) and authorization (governed by IAM policies and resource policies). IAM permissions alone do not provide network reachability, which is the core requirement in this scenario. 3. Private AWS Service Access: Workloads deployed in private VPC subnets with no internet gateway route can access supported AWS services using VPC endpoints, ensuring traffic never traverses the public internet, reducing attack surface and improving network performance. References: 1. AWS VPC User Guide: Gateway VPC endpoints, https://docs.aws.amazon.com/vpc/latest/privatelink/gateway-endpoints.html 2. Amazon S3 User Guide: Types of VPC endpoints for Amazon S3, https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html#types-of-vpc-endpoints-for-s3

The root cause of the reported issue is siloed, instance-specific storage: each EC2 instance uses a dedicated, AZ-local EBS volume that is only accessible to that single instance. When the Application Load Balancer routes user requests to different EC2 instances across the two AZs, users only see the subset of documents stored on the EBS volume attached to the instance they are currently connected to. The suggested answer C resolves this by replacing the siloed EBS volumes with a shared, multi-AZ accessible storage layer. Amazon EFS is a fully managed elastic network file system that supports concurrent read/write access from EC2 instances across multiple Availability Zones in the same VPC. Migrating all existing documents from both EBS volumes to EFS and updating the application to save new uploads to EFS ensures every EC2 instance behind the ALB has access to the full, consistent set of user documents, eliminating the inconsistent view users experience. This solution also preserves and improves the existing scalability and availability goals, as EFS automatically scales storage capacity without manual intervention, and is designed for 99.99% availability across multiple AZs. Option Analysis: A. Copying data to both EBS volumes only resolves the issue for existing documents, but does not address new user uploads. Any new document saved to one instance's EBS volume will not automatically sync to the other volume, so users will continue to see inconsistent document sets when routed to different instances. This approach requires building and maintaining a custom sync mechanism that is error-prone, not scalable, and adds unnecessary operational overhead, so it is incorrect. B. The Application Load Balancer does not have native visibility into the contents of backend instance storage, so it cannot intelligently route users to the specific instance that holds their documents. While sticky sessions could route a single user to the same instance repeatedly, this breaks high availability: if the assigned instance fails, the user will lose access to all documents stored on that instance's EBS volume. This does not resolve the underlying siloed storage issue, so it is incorrect. C. This option is correct. Amazon EFS is purpose-built for shared file storage across multiple EC2 instances in different AZs. Migrating all existing documents to EFS and updating the application to use EFS for new storage ensures every EC2 instance accesses the same single source of truth for user documents, so users see all their documents regardless of which instance the ALB routes them to. This solution is fully managed, highly available, and scales automatically to meet storage and throughput demands, aligning with the company's scalability and availability goals. D. Application Load Balancers are designed to route each individual client request to a single target instance, not to send the same request to multiple instances and aggregate responses. This approach is technically unfeasible with standard ALB functionality, would introduce significant latency and complexity, and does not resolve the root cause of siloed storage, so it is incorrect. Key Concepts: 1. Amazon EFS Core Characteristics: Amazon EFS is a fully managed, elastic NFS file system that supports concurrent read/write access from thousands of EC2 instances across multiple Availability Zones in the same region, making it ideal for shared storage workloads that require consistent access from multiple compute resources. 2. Amazon EBS Core Characteristics: Amazon EBS is AZ-specific block storage that is by default attachable to only one EC2 instance at a time. It is not designed for cross-instance or cross-AZ shared file storage, making it unsuitable for use cases where multiple instances need access to the same set of unstructured data like user uploads. 3. Application Load Balancer Routing Limitations: ALBs operate at Layer 7 of the OSI model and route requests based on request attributes such as path, host headers, or cookies, but do not have visibility into backend storage contents, and cannot route a single request to multiple target instances simultaneously. References: What is Amazon Elastic File System?, https://docs.aws.amazon.com/efs/latest/ug/whatisefs.html AWS Storage Services Overview: Block, File, and Object Storage, https://docs.aws.amazon.com/whitepapers/latest/aws-storage-services-overview/block-file-and-object-storage.html