Free Amazon Web Services SAA-C03 Practice Exam with Questions & Answers | Set: 6

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

This solution meets the requirements most cost-effectively because it enables the company to migrate its on-premises NFS data store to AWS without changing the existing applications or workflows. AWS Storage Gateway is a hybrid cloud storage service that provides seamless and secure integration between on-premises and AWS storage. Amazon S3 File Gateway is a type of AWS Storage Gateway that provides a file interface to Amazon S3, with local caching for low-latency access. By setting up an Amazon S3 File Gateway, the company can store and retrieve files as objects in Amazon S3 using standard file protocols such as NFS. The company can also use an Amazon S3 Lifecycle policy to automatically transition the data to the appropriate storage class based on the frequency of access and the cost of storage. For example, the company can use S3 Standard for frequently accessed data, S3 Standard-Infrequent Access (S3 Standard-IA) or S3 One Zone-Infrequent Access (S3 One Zone-IA) for less frequently accessed data, and S3 Glacier or S3 Glacier Deep Archive for long-term archival data.

Option A is not a valid solution because AWS Storage Gateway Volume Gateway is a type of AWS Storage Gateway that provides a block interface to Amazon S3, with local caching for low-latency access. Volume Gateway is not suitable for migrating an NFS data store, as it requires attaching the volumes to EC2 instances or on-premises servers using the iSCSI protocol. Option C is not a valid solution because Amazon Elastic File System (Amazon EFS) is a fully managed elastic NFS file system that is designed for workloads that require high availability, scalability, and performance. Amazon EFS Standard-Infrequent Access (Standard-IA) is a storage class within Amazon EFS that is optimized for infrequently accessed files, with a lower price per GB and a higher price per access. Using Amazon EFS Standard-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. Option D is not a valid solution because Amazon EFS One Zone-Infrequent Access (One Zone-IA) is a storage class within Amazon EFS that isoptimized for infrequently accessed files that do not require the availability and durability of Amazon EFS Standard or Standard-IA. Amazon EFS One Zone-IA stores data in a single Availability Zone, which reduces the cost by 47% compared to Amazon EFS Standard-IA, but also increases the risk of data loss in the event of an Availability Zone failure. Using Amazon EFS One Zone-IA for migrating an NFS data store would not be cost-effective, as it would incur higher access charges and require additional configuration to enable lifecycle management. It would also compromise the availability and durability of the data.

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: Senior leadership wants a custom dashboard displaying NAT gateway costs daily, starting from the beginning of the current month.

Analysis of Options:

QuickSight with DataSync: While QuickSight is suitable for dashboards, DataSync is not designed for querying and analyzing data reports.

QuickSight with Athena: QuickSight can visualize data queried by Athena, which is designed to analyze data directly from S3.

CloudWatch with DataSync: CloudWatch is primarily for monitoring metrics, not for creating detailed cost analysis dashboards.

CloudWatch with Athena: Similarly, using CloudWatch with Athena does not align well with the requirement for a visual dashboard.

Best Solution for Visualization and Querying:

Amazon QuickSight with Athena: This combination allows for powerful data visualization and querying capabilities. QuickSight can create dynamic dashboards, while Athena efficiently queries the cost and usage report data stored in S3.

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 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 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: The company needs to enable communication between VPCs across multiple AWS Regions with minimal administrative effort.

Analysis of Options:

VPC Peering: Managing multiple VPC peering connections across regions is complex and difficult to scale, leading to significant administrative overhead.

AWS Direct Connect Gateways: Primarily used for creating private connections between AWS and on-premises environments, not for inter-VPC communication across regions.

AWS Transit Gateway: Simplifies VPC interconnections within a region and supports Transit Gateway peering for cross-region connectivity, reducing administrative complexity.

AWS PrivateLink: Used for accessing AWS services and third-party services over a private connection, not for inter-VPC communication.

Best Solution:

AWS Transit Gateway with Transit Gateway Peering: This option provides a scalable and efficient solution for managing VPC communications both within a single region and across multiple regions with minimal administrative overhead.

Requirement Analysis: The company needs to scale throughput for uploading large files to S3 and downloading them to EC2 instances.

S3 Multipart Uploads: This method allows for the parallel upload of parts of a file, improving upload efficiency and reliability.

Parallel Fetching: Fetching multiple byte-ranges in parallel from S3 improves download performance.

Implementation:

For uploads, use the S3 multipart upload API to upload files in parallel.

For downloads, use the S3 API to request multiple byte-ranges concurrently.

Conclusion: These solutions effectively scale throughput and improve the performance of both uploads and downloads.

References

S3 Multipart Upload:Amazon S3 Multipart Upload

Parallel Fetching:S3 Byte-Range Fetches

Understanding the Requirements: The company needs to optimize costs and has the flexibility to change EC2 instance types and families frequently (every 2-3 months).

Savings Plans Overview: Savings Plans offer significant savings over On-Demand pricing, with the flexibility to use any instance type and family within a region.

No Upfront Compute Savings Plan: This plan allows for cost optimization without any upfront payment, offering flexibility to change instance types and families.

Term Selection: A 1-year term is appropriate for balancing cost savings and flexibility given the frequent changes in instance types.

Conclusion: A No Upfront Compute Savings Plan for a 1-year term provides the needed flexibility and cost savings without the commitment and inflexibility of Reserved Instances.

References

AWS Savings Plans:AWS Savings Plans

AWS Cost Management Documentation:AWS Cost Management

Understanding the Requirement: The company needs to map its IT infrastructure to identify and enforce security policies, with the ability to quickly query and identify security risks.

Analysis of Options:

Amazon RDS: While suitable for relational data, it is not optimized for handling complex relationships and querying those relationships, which is essential for an IT infrastructure map.

Amazon Neptune: A graph database service designed for handling highly connected data. It uses SPARQL to query graph data efficiently, making it ideal for mapping IT infrastructure and identifying relationships that pose security risks.

Amazon Redshift: A data warehouse solution optimized for complex queries on large datasets but not specifically for graph data.

Amazon DynamoDB: A NoSQL database that uses PartiQL for querying, but it is not optimized for complex relationships in graph data.

Best Option for Mapping and Querying IT Infrastructure:

Amazon Neptune provides the most suitable solution with the least operational overhead. It is purpose-built for graph data and enables efficient querying of complex relationships to identify security risks.

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

 This solution meets the requirements with the least operational overhead because it uses AWS services that are fully managed and scalable. The EventBridge rule can detect the DeleteKey operation from the AWS KMS API and trigger the Systems Manager Automation runbook, which can execute a predefined workflow to cancel the key deletion. The EventBridge rule can also publish an SNS message to the topic that sends an email notification to the administrators. This way, the company can prevent the accidental deletion of KMS keys and notify the administrators of any attempts to delete them.

Option A is not a valid solution because AWS Config rules are used to evaluate the configuration of AWS resources, not to cancel the deletion of KMS keys. Option B is not a valid solution because it requires creating and maintaining a custom Lambda function that has logic to prevent KMS key deletion, which adds operational overhead. Option D is not a valid solution because it only notifies the administrators of the DeleteKey operation, but does not cancel it.

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 company needs to store a copy of all new photos in the us-east-1 Region from an S3 bucket in the us-west-1 Region.

Analysis of Options:

Cross-Region Replication: Automatically replicates objects across regions with minimal operational effort once configured.

CORS Configuration: Used for allowing resources on a web page to be requested from another domain, not for replication.

S3 Lifecycle Rule: Manages the transition of objects between storage classes within the same bucket, not for cross-region replication.

S3 Event Notifications with Lambda: Requires additional configuration and management compared to Cross-Region Replication.

Best Solution:

S3 Cross-Region Replication: This solution provides an automated and efficient way to replicate objects to another region, meeting the requirement with the least operational effort.

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

AWS Compute Optimizerprovides detailed recommendations for optimizingAmazon EBS volumes, including insights into underutilized volumes, inefficient configurations, and potential cost savings. It analyzes usage patterns and provides recommendations based on historical data, making it the ideal tool for this use case.

Option A (Amazon Inspector): Amazon Inspector is for security assessments, not for cost optimization.

Option B (Systems Manager): Systems Manager does not specifically provide EBS optimization recommendations.

Option C (CloudWatch): CloudWatch metrics help monitor usage but do not offer optimization recommendations like Compute Optimizer.

AWS References:

AWS Compute Optimizer

Requirement Analysis: The application must process each credit card data validation request at least once, securely and cost-effectively.

SQS FIFO Queues: Ensures that each message is processed exactly once and in the exact order sent.

AWS Lambda: Using Lambda for event-driven processing ensures scalability and cost-efficiency.

SSE-SQS: Provides encryption at rest using SQS-managed keys, simplifying encryption management.

Implementation:

Set up SQS FIFO queues as the event source for Lambda.

Enable SSE-SQS for encryption.

Ensure the Lambda execution role has the necessary permissions to use the encryption keys.

Conclusion: This combination meets the requirements of security, exact-once processing, and cost-effectiveness.

References

Amazon SQS:Amazon SQS Documentation

AWS Lambda with SQS:Using AWS Lambda with Amazon SQS

Amazon Athena provides a cost-effective, serverless way to query data without affecting the performance of DynamoDB. By using the Athena DynamoDB connector, the company can perform the necessary SQL queries without consuming read capacity on the DynamoDB table, which is essential for minimizing impact on provisioned throughput.

Key benefits:

Minimal Impact on Provisioned Capacity: Athena queries do not directly impact DynamoDB’s read capacity, making it ideal for running analytics without affecting the customer-facing workloads.

Cost-Effective: Athena is a serverless solution, meaning you pay only for the queries you run, making it highly cost-effective compared to running a dedicated cluster like Amazon EMR or Redshift.

AWS Documentation: The use of Athena to query DynamoDB through its connector aligns with AWS's best practices for performance efficiency and cost optimization​​.

Amazon RDSMulti-AZ DB cluster deploymentensures high availability by automatically replicating data across multiple Availability Zones (AZs), and it supports failover in case of a failure in one AZ. This setup also provides increased capacity for read workloads by allowing read scaling with reader instances in different AZs. This solution offers the most operational efficiency with minimal manual intervention.

Option A (DynamoDB): DynamoDB is not suitable for a relational database workload, which requires a PostgreSQL engine.

Option B (RDS with Multi-AZ): While this provides high availability, it doesn't offer read scaling capabilities.

Option D (Cross-Region Read Replicas): This adds complexity and is not necessary if the requirement is high availability within a single region.

AWS References:

Amazon RDS Multi-AZ DB Cluster