効果的Amazon SAA-C03|100%合格率のSAA-C03英語版試験|試験の準備方法AWS Certified Solutions Architect - Associate赤本合格率

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Amazon SAA-C03 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Design Resilient Architectures | 26% | - Design decoupled and loosely coupled systems
- 1. Serverless and managed service integration
- 2. Messaging and queue services
- 3. Event-driven architectures
- Design highly available and fault-tolerant architectures
- 1. Replication and disaster recovery strategies
- 2. Regions, Availability Zones, and edge locations
- 3. Load balancing and auto scaling
- Design for reliability and recovery
- 1. Backup and restore mechanisms
- 2. Failover and failback processes
|
| Design Secure Architectures | 30% | - Design secure access to AWS resources
- 1. Multi-account access and control
- 2. Security best practices and least privilege
- 3. Shared responsibility model
- 4. Identity and access management (IAM, IAM Identity Center)
- Design for security and threat detection
- 1. Incident response and security automation
- 2. Monitoring and logging services
- Design secure workloads and applications
- 1. Data protection and encryption (at rest/in transit)
- 2. Network security (VPC, security groups, NACLs)
- 3. Compliance and governance controls
|
| Design High-Performing Architectures | 24% | - Design high-performance storage solutions
- 1. Storage performance and durability
- 2. Caching and content delivery
- 3. Storage types (object, block, file)
- Design high-performance networking solutions
- 1. Latency reduction and throughput optimization
- 2. Network architecture and connectivity
- 3. Global acceleration and routing
- Design high-performance compute solutions
- 1. Compute options (EC2, Lambda, ECS, EKS)
- 2. Instance types and sizing
- 3. Scaling and performance optimization
|
| Design Cost-Optimized Architectures | 20% | - Identify cost-effective storage solutions
- 1. Data retention and archiving
- 2. Storage classes and lifecycle policies
- Design cost-effective compute and network solutions
- 1. Data transfer cost optimization
- 2. Right-sizing and reserved instances
- 3. Serverless and pay-as-you-go models
- Control and monitor costs
- 1. Resource tagging and allocation
- 2. Cost management tools and alerts
|
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Amazon AWS Certified Solutions Architect - Associate 認定 SAA-C03 試験問題 (Q219-Q224):
質問 # 219
A financial services company must retain log data for 1 year. The company stores log files in an Amazon S3 bucket and wants to prevent any user from deleting or overwriting the log files during this period. The data must remain available for read-only requests. Which solution will meet this requirement?
- A. Enable S3 Versioning on the bucket. Use Object Lock in compliance mode with a 1-year retention period.
- B. Enable S3 Versioning on the bucket. Create an S3 Lifecycle Configuration rule to move objects to Amazon S3 Glacier Flexible Retrieval after 1 year.
- C. Enable S3 Transfer Acceleration on the bucket. Create an S3 Lifecycle Configuration rule to move objects to Amazon S3 Glacier Flexible Retrieval after 1 year.
- D. Create an AWS Lambda function to programmatically check the timestamp of S3 data and to move the data to Amazon S3 Glacier Deep Archive if the data is older than 1 year.
正解:A
解説:
To ensure that log files are immutable and cannot be deleted or overwritten for a specified retention period, Amazon S3 offers Object Lock in Compliance Mode. When enabled:
Compliance Mode: Ensures that a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account, for the duration of the retention period.
AWS Documentation S3 Versioning: Must be enabled on the bucket to use Object Lock. This allows multiple versions of an object to be stored, ensuring that previous versions are preserved and protected. Amazon Web Services, Inc.
By enabling S3 Versioning and applying Object Lock in Compliance Mode with a 1-year retention period, the company can meet regulatory requirements to retain log data securely and prevent any modifications or deletions during that time.
質問 # 220
A company uses an organization in AWS Organizations to manage AWS accounts that contain applications. The company sets up a dedicated monitoring member account in the organization. The company wants to query and visualize observability data across the accounts by using Amazon CloudWatch.
Which solution will meet these requirements?
- A. Enable CloudWatch cross-account observability for the monitoring account. Deploy an AWS CloudFormation template provided by the monitoring account in each AWS account to share the data with the monitoring account.
- B. Set up service control policies (SCPs) to provide access to CloudWatch in the monitoring account under the Organizations root organizational unit (OU).
- C. Create a new IAM user in the monitoring account. Create cross-account IAM policies in each AWS account. Attach the IAM policies to the new IAM user.
- D. Configure a new IAM user in the monitoring account. In each AWS account, configure an IAM policy to have access to query and visualize the CloudWatch data in the account. Attach the new IAM policy to the new IAM user.
正解:A
解説:
This solution meets the requirements because it allows the monitoring account to query and visualize observability data across the accounts by using CloudWatch. CloudWatch cross-account observability is a feature that enables a central monitoring account to view and interact with observability data shared by other accounts. To enable cross-account observability, the monitoring account needs to configure the types of data to be shared (metrics, logs, and traces) and the source accounts to be linked. The source accounts can be specified by account IDs, organization IDs, or organization paths. To share the data with the monitoring account, the source accounts need to deploy an AWS CloudFormation template provided by the monitoring account. This template creates an observability link resource that represents the link between the source account and the monitoring account. The template also creates a sink resource that represents an attachment point in the monitoring account. The source accounts can share theirobservability data with the sink in the monitoring account. The monitoring account can then use the CloudWatch console, API, or CLI to search, analyze, and correlate the observability data across the accounts. Reference: CloudWatch cross-account observability, Setting up CloudWatch cross-account observability, [Observability Access Manager API Reference]
質問 # 221
A company is migrating a daily Microsoft Windows batch job from the company's on-premises environment to AWS. The current batch job runs for up to 1 hour. The company wants to modernize the batch job process for the cloud environment.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Fargate to deploy the Windows batch job as a container. Use AWS Batch to manage the batch job processing.
- B. Create a fleet of Amazon EC2 instances in an Auto Scaling group to handle the Windows batch job processing.
- C. Implement an AWS Lambda function to process the Windows batch job. Use an Amazon EventBridge rule to invoke the Lambda function.
- D. Use Amazon Elastic Kubernetes Service (Amazon EKS) on Amazon EC2 instances to orchestrate Windows containers for the batch job processing.
正解:A
解説:
AWS Batch supports Windows-based jobs and automates provisioning and scaling of compute environments. Paired with AWS Fargate, it removes the need to manage infrastructure. This solution requires the least operational overhead and is cloud-native, providing flexibility and scalability.
Reference:
質問 # 222
A development team runs monthly resource-intensive tests on its general purpose Amazon RDS for MySQL DB instance with Performance Insights enabled. The testing lasts for 48 hours once a month and is the only process that uses the database. The team wants to reduce the cost of running the tests without reducing the compute and memory attributes of the DB instance.
Which solution meets these requirements MOST cost-effectively?
- A. Stop the DB instance when tests are completed. Restart the DB instance when required.
- B. Create a snapshot when tests are completed. Terminate the DB instance and restore the snapshot when required.
- C. Modify the DB instance to a low-capacity instance when tests are completed. Modify the DB instance again when required.
- D. Use an Auto Scaling policy with the DB instance to automatically scale when tests are completed.
正解:A
解説:
To reduce the cost of running the tests without reducing the compute and memory attributes of the Amazon RDS for MySQL DB instance, the development team can stop the instance when tests are completed and restart it when required. Stopping the DB instance when not in use can help save costs because customers are only charged for storage while the DB instance is stopped. During this time, automated backups and automated DB instance maintenance are suspended. When the instance is restarted, it retains the same configurations, security groups, and DB parameter groups as when it was stopped.
Reference:
Amazon RDS Documentation: Stopping and Starting a DB instance (https://docs.aws.amazon.com
/AmazonRDS/latest/UserGuide/USER_StopInstance.html)
質問 # 223
A company wants to implement a data lake in the AWS Cloud. The company must ensure that only specific teams have access to sensitive data in the data lake. The company must have row- level access control for the data lake. Which solution meet these requirements?
- A. Use AWS Glue Catalog to store the data. Use AWS Glue DataBrew for data governance and access control.
- B. Use Amazon RDS to store the data. Use IAM roles and permissions for data governance and access control.
- C. Use Amazon S3 to store the data. Use AWS Lake Formation for data governance and access control.
- D. Use Amazon Redshift to store the data. Use IAM roles and permissions for data governance and access control.
正解:C
解説:
A). RDS: Suitable for relational databases but does not provide native support for data lakes or row-level access.
B). Redshift: Primarily for analytics, not designed for large-scale data lake governance.
C). S3 + Lake Formation: Provides native support for data lakes with granular access control, including row-level permissions.
D). Glue Catalog + DataBrew: Focused on data preparation and metadata management, not row- level access control.
質問 # 224
......
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