Free Practice Questions for Amazon Web Services SAA-C03 Exam

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

Understanding the Requirement: The company needs to redesign the architecture to be highly available and use AWS managed solutions for hosting a web application with static content, PHP application, and Redis for user sessions.

Analysis of Options:

AWS Elastic Beanstalk: Suitable for simplifying deployment but may not provide the desired flexibility and control for complex architectures.

AWS Lambda and API Gateway: Not ideal for hosting a stateful PHP application and handling static content.Adding complexity without significant benefit.

EC2 instance with ElastiCache and S3: Provides some high availability but involves managing EC2 instances, which increases operational overhead.

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This solution leverages fully managed AWS services for each component, ensuring high availability and scalability.

Best Solution:

CloudFront with S3, ALB, ECS with Fargate, and ElastiCache: This combination of services meets the requirements for a highly available and managed solution, ensuring optimal performance and minimal operational overhead.

Understanding the Requirement: The company needs to collect transaction data in real time, avoid data duplication, and perform additional processing without managing infrastructure.

Analysis of Options:

SQS FIFO Queue with Lambda: Ensures data is processed in order and prevents duplication.Lambda handles processing without the need to manage servers.

SQS FIFO Queue with AWS Batch: While this ensures no duplicates, it introduces additional complexity and management overhead with AWS Batch.

Kinesis Data Streams with AWS Batch and EC2: Involves more components and infrastructure management, which is against the requirement of not wanting to manage infrastructure.

Step Functions with Lambda and EC2: Involves setting up multiple services and still requires managing EC2 instances, increasing complexity.

Best Solution:

SQS FIFO Queue with Lambda: This combination ensures real-time data processing, prevents duplication, and minimizes infrastructure management, meeting all requirements efficiently.

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

Understanding the Requirement: The application experiences slow performance at the start of peak hours, but normalizes after a few hours. The goal is to ensure proper performance at the beginning of peak hours.

Analysis of Options:

Application Load Balancer: Ensures proper traffic distribution but does not address the need to have sufficient instances running at the start of peak hours.

Dynamic Scaling Policy Based on Memory or CPU Utilization: While dynamic scaling reacts to usage metrics, it may not preemptively scale in anticipation of peak hours, leading to delays as new instances are launched and become available.

Scheduled Scaling Policy: This allows the Auto Scaling group to launch instances ahead of time, ensuring that enough instances are available and ready to handle the increased load right at the start of peak hours.

Best Solution:

Scheduled Scaling Policy: This approach ensures that new instances are launched and ready before peak hours begin, addressing the slow performance issue at the start of peak periods.

Understanding the Requirement: The university wants to centralize management and automate backups for its AWS services (EC2, RDS, and DynamoDB), reducing reliance on custom scripts.

Analysis of Options:

Third-party backup software with AWS Storage Gateway: This solution introduces external dependencies and adds complexity compared to using native AWS services.

AWS Backup: Provides a centralized, fully managed service to automate and manage backups across various AWS services, including EC2, RDS, and DynamoDB.

AWS Config: Primarily used for compliance and configuration monitoring, not for backup management.

AWS Systems Manager State Manager: Useful for configuration management but not specifically designed for managing backups.

Best Solution:

AWS Backup: This service offers the necessary functionality to centralize and automate backups, providing a streamlined and integrated solution with minimal effort.

Understanding the Requirement: EC2 instances need to upload data to S3 without using the public internet, and on-premises servers need to consume this data.

Analysis of Options:

Interface VPC Endpoint for EC2: Not relevant for accessing S3.

Gateway VPC Endpoint for S3 and Direct Connect: Provides private connectivity from EC2 instances to S3 and from on-premises to AWS, ensuring compliance with the requirement to avoid public internet.

Transit Gateway and Site-to-Site VPN: Adds unnecessary complexity and does not provide the same level of performance as Direct Connect.

Proxy EC2 Instances with NAT Gateways: Increases complexity and costs compared to a direct connection using VPC endpoints and Direct Connect.

Best Solution:

Gateway VPC Endpoint for S3 and Direct Connect: This solution ensures secure, private data transfer both within AWS and between on-premises and AWS, meeting the compliance requirements effectively.

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

Understanding the Requirement: The company wants to optimize costs and reduce operational overhead for an RDS for PostgreSQL database that only needs to be active during business hours on weekdays.

Analysis of Options:

Instance Scheduler on AWS: Allows for automated start and stop schedules based on specified times, ideal for resources only needed during certain hours. This directly optimizes costs by running the database only when needed.

Turn off automatic backups and create weekly snapshots: Does not address the requirement of reducing operational overhead and optimizing runtime costs.

Custom Lambda function: This could work but adds unnecessary complexity compared to using the Instance Scheduler.

All Upfront Reserved DB Instances: While this reduces costs, it does not optimize for usage patterns that require the database only during specific hours.

Best Solution:

Instance Scheduler on AWS: This option effectively manages the database runtime based on the specified schedule, reducing costs and operational overhead.

Understanding the Requirement: The mobile app captures and uploads raw video clips to S3, but users experience buffering and playback issues due to the large size of these videos.

Analysis of Options:

Amazon CloudFront: A content delivery network (CDN) that can cache and deliver content globally with low latency. It helps reduce buffering by delivering content from edge locations closer to the users.

AWS DataSync: Primarily used for data transfer and replication across AWS Regions, which does not directly address the video size and buffering issue.

Amazon Elastic Transcoder: A media transcoding service that can convert raw video files into formats and resolutions more suitable for streaming, reducing the size and improving playback performance.

EC2 Instances in Local Zones: While this could provide content delivery and caching, it involves more operational overhead compared to using CloudFront.

EC2 Instances for Transcoding: Involves setting up and maintaining infrastructure, leading to higher operational overhead compared to using Elastic Transcoder.

Best Combination of Solutions:

Deploy Amazon CloudFront: This optimizes the performance by caching content at edge locations, reducing latency and buffering for users.

Use Amazon Elastic Transcoder: This reduces the file size and converts videos into formats better suited for streaming on mobile devices.

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS:Amazon SQS Documentation

Understanding the Requirement: The company needs to ensure clients use a TLS certificate when accessing the website and automate the creation and renewal of TLS certificates.

Analysis of Options:

CloudFront Security Policy: Not applicable for creating certificates.

CloudFront Origin Access Control (OAC): Controls access to origins, not relevant for TLS certificate creation.

AWS Certificate Manager (ACM) with DNS Validation: Provides automated certificate management, including creation and renewal, with minimal manual intervention. DNS validation is automated and does not require manual intervention as email validation does.

AWS Certificate Manager (ACM) with Email Validation: Requires manual intervention to approve validation emails, which increases operational effort.

Best Solution:

AWS Certificate Manager (ACM) with DNS Validation: Ensures automated and efficient certificate management with the least operational effort.

Requirement Analysis: The database experiences peak traffic multiple times a day but has wasted capacity during non-peak hours. The goal is to reduce costs with minimal operational effort.

Aurora Serverless Overview: Aurora Serverless automatically adjusts database capacity based on current demand, scaling up during peak times and scaling down during non-peak times.

Cost Efficiency: Aurora Serverless charges only for the capacity used, which is more cost-effective than provisioning for peak traffic.

Operational Efficiency: Aurora Serverless eliminates the need for manual scaling or scheduling Lambda functions for capacity management.

Implementation: Migrate the database from the provisioned Aurora PostgreSQL cluster to an Aurora Serverless cluster.

References

Amazon Aurora Serverless:Aurora Serverless Documentation

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

Understanding the Requirement: The company wants to cost-optimize their workload that uses EC2, Lambda, Fargate, and SageMaker, covering the most services with the fewest savings plans.

Analysis of Options:

EC2 Instance Savings Plan: Limited to EC2 and SageMaker, missing coverage for Lambda and Fargate.

Compute Savings Plan: Provides the most flexibility, covering a broad range of compute services, including EC2, Lambda, Fargate, and SageMaker.

SageMaker Savings Plan: Specifically for SageMaker, missing coverage for EC2, Lambda, and Fargate.

Combination of plans: The Compute Savings Plan is versatile and can be combined to cover different services efficiently.

Best Solution:

Compute Savings Plan for EC2, Lambda, and SageMaker: Covers the primary compute services efficiently.

Compute Savings Plan for Lambda, Fargate, and EC2: Covers the remaining services, ensuring broad coverage with minimal plans.

Requirement Analysis: The Lambda function in the administrator account needs to process messages from an SNS topic in the production account.

IAM Policy for SNS Topic: Allows the Lambda function to subscribe and be invoked by the SNS topic.

SQS Queue for Buffering: Using an SQS queue provides reliable message delivery and buffering between SNS and Lambda, ensuring all messages are processed.

Implementation:

Create an SQS queue in the administrator account.

Set an IAM policy to allow the Lambda function to subscribe to and be invoked by the SNS topic.

Configure the SNS topic to send messages to the SQS queue.

Set up the SQS queue to trigger the Lambda function.

Conclusion: This solution ensures reliable message delivery and processing with appropriate permissions.

References

Amazon SNS:Amazon SNS Documentation

Amazon SQS:Amazon SQS Documentation

AWS Lambda:AWS Lambda Documentation

Requirement Analysis: The goal is to prevent accidental deletion of EBS snapshots while avoiding indefinite retention.

Recycle Bin for EBS Snapshots: AWS Recycle Bin allows for retention rules that prevent immediate deletion of snapshots, providing a safety net against accidental deletions.

Retention Rule: A 7-day retention rule ensures snapshots are not permanently deleted immediately, giving time to recover from accidental deletions.

Implementation:

Enable Recycle Bin in your AWS account.

Create a retention rule that specifies a 7-day period for EBS snapshots.

Apply this rule to all EBS snapshots.

Conclusion: This solution provides an automated way to prevent data loss from accidental deletions with minimal development effort.

References

AWS Recycle Bin:AWS Recycle Bin Documentation

Understanding the Requirement: The company needs logs to be highly available for 30 days for frequent analysis, retained for an additional 60 days for backup, and deleted after 90 days.

Analysis of Options:

Transition to S3 Standard after 30 days: Keeps logs in the same high-availability storage, not cost-effective.

Transition to S3 Standard-IA, then Glacier Flexible Retrieval after 90 days: Adds unnecessary cost and complexity since objects need to be accessible for only 30 days and then retained for 60 days.

Transition to Glacier Flexible Retrieval after 30 days: Not suitable for frequent access required in the first 30 days.

Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: Provides cost-effective storage for infrequently accessed logs after the initial 30-day period, then moves to the cheapest long-term storage before deletion.

Best Solution:

Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: This solution meets the requirements for high availability, cost-effective storage for backup, and scheduled deletion with the least cost.