Free Amazon Web Services SAA-C03 Practice Exam with Questions & Answers | Set: 7

AWS recommends Auto Scaling with dynamic scaling policies to handle unpredictable workload spikes. Target tracking scaling policies automatically adjust capacity based on defined metrics such as average CPU utilization or request count per target. This approach ensures applications maintain responsiveness without overprovisioning. Scheduled scaling (options B and C) is only effective when traffic patterns are predictable, which is not the case here. Reserved Instances or Spot Instances also do not address sudden demand changes effectively. Replacing the ALB with an NLB (D) does not solve latency caused by EC2 instance capacity. Therefore, using EC2 instances in an Auto Scaling group with a target tracking scaling policy is the most effective and cost-efficient solution for unpredictable, short-lived traffic spikes.

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

AWS documentation states that EC2 Auto Scaling is the recommended, cost-effective, and fault-tolerant method to manage workloads with predictable or varying demand. Auto Scaling automatically adds instances during peak traffic and terminates them when demand is low, reducing compute cost while maintaining availability across multiple Availability Zones.

Reserved Instances (Option B) would force the company to pay for peak capacity all day, which is not cost-effective.

Stopping instances manually (Option C) or vertically scaling instances (Option D) reduces fault tolerance and increases operational overhead.

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DynamoDB export to Amazon S3 (via point-in-time exports or table backups) “does not consume read capacity” and has no impact on table performance. Once data is in S3, Amazon Redshift can ingest efficiently with COPY from S3, which is the standard, parallel, high-throughput load path with minimal management. Direct COPY from DynamoDB (B) performs a table scan that can consume provisioned throughput, risking throttling. Building Lambda pipelines (C) adds custom code and scheduling overhead. Athena federated queries (D) are ad hoc analytics, not optimized for bulk, recurring warehouse loads. Therefore, exporting to S3 and loading with Redshift COPY maintains DynamoDB throughput and minimizes operational burden.

Amazon S3 Intelligent-Tiering is designed for unknown or unpredictable access patterns, automatically moving objects to the most cost-effective tier without performance impact.

It is ideal when you do not know object size or access frequency.

S3 Standard (Option B) works but costs more.

S3 One Zone-IA and Glacier Deep Archive (Options D and A) are not appropriate because data is frequently accessed for processing and persists only 24 hours.

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This solution meets the company's requirements with minimal administrative overhead and ensures security and ease of management.

AWS AppConfig: AWS AppConfig is a service designed to manage application configuration in a secure and validated way. It allows you to deploy configurations safely and quickly without affecting the application's performance or availability.

AWS Secrets Manager: AWS Secrets Manager is specifically designed to manage, retrieve, and rotate credentials for databases and other services. It integrates seamlessly with AWS services like Amazon RDS, making it an ideal solution for securely storing and retrieving database credentials. Secrets Manager also provides automatic rotation of credentials, reducing the operational burden.

Why Not Other Options?:

Option B (AWS Lambda + Parameter Store): While AWS Lambda can be used for managing configurations and AWS Systems Manager Parameter Store can store credentials, this approach involves more manual setup and does not offer the same level of integrated management and security as AppConfig and Secrets Manager.

Option C (Encrypted S3 Configuration File): Storing configuration and credentials in S3 files involves more manual management and security considerations, increasing the administrative overhead.

Option D (AppConfig + RDS for credentials): RDS is not designed for storing application credentials; it's better suited for managing database instances and their configurations.

AWS References:

AWS AppConfig- Describes how to use AWS AppConfig for managing application configurations.

AWS Secrets Manager- Provides details on securely storing and retrieving credentials using AWS Secrets Manager.

AWS Global Accelerator reduces latency by directing users to the optimal Regional endpoint based on global network health and proximity. Amazon CloudFront caches static and dynamic content at edge locations for ultra-low latency access worldwide, improving performance and reducing server load.

The company wants to reduce end-to-end load time for its global customer base.AWS Global Acceleratorprovides a network optimization service that reduces latency by routing traffic to the nearest AWS edge locations, improving the user experience for globally distributed customers.

AWS Global Accelerator:

Global Acceleratorimproves the performance of your applications by routing traffic through AWS’s global network infrastructure. This reduces the number of hops and latency compared to using the public internet.

By creating astandard acceleratorand configuring the existing NLBs as target endpoints, Global Accelerator ensures that traffic from users around the world is routed to the nearest AWS edge location and then through optimized paths to the NLBs in each region. This significantly improves end-to-end load time for global customers.

Why Not the Other Options?:

Option A (ALBs instead of NLBs): ALBs are designed for HTTP/HTTPS traffic and provide layer 7 features, but they wouldn’t solve the latency issue for a global customer base. The key problem here is latency, and Global Accelerator is specifically designed to address that.

Option B (Route 53 weighted routing): Route 53 can route traffic to different regions, but it doesn’t optimize network performance. It simply balances traffic between endpoints without improving latency.

Option C (Additional NLBs in more regions): This could potentially improve latency but would require setting up infrastructure in multiple regions. Global Accelerator is a simpler and more efficient solution that leverages AWS’s existing global network.

AWS References:

AWS Global Accelerator

By usingAWS Global Acceleratorwith the existing NLBs, the company can optimize global traffic routing and improve the customer experience by minimizing latency. Therefore,Option Dis the correct answer.

AWS Application Migration Service (AWS MGN) is the recommended tool for a lift-and-shift strategy, especially for time-sensitive migrations. It automates the replication of on-premises VMs to AWS, minimizing the effort required for migration and testing.

Key steps:

Replication with AWS MGN: The AWS Replication Agent is installed on the VMs to continuously replicate data to AWS, allowing you to manage migration easily.

Testing and Cutover: Initial replication allows for testing in AWS before performing the final cutover, ensuring that the migration process is smooth and data integrity is maintained.

AWS Documentation: AWS MGN is recommended for migrating virtual machines to the cloud with minimal downtime and disruption​​.

Using a Lambda function as part of the Kinesis Data Firehose pipeline allows for real-time detection and masking of PII before data is written to S3. This ensures that PII is never stored in its raw form in the data lake.

Option B:Amazon Macie can scan and classify data but does not provide in-line PII masking for data ingestion.

Option C:Server-side encryption secures data but does not mask PII.

Option D:CloudHSM is unnecessary for PII masking and adds complexity without addressing the requirements.

AWS Documentation References:

Using Lambda with Kinesis Data Firehose

To improve the performance of the primary RDS PostgreSQL database during business hours and reduce the load, the best solution is to create aread replicain the same region (us-east-1). This will offload the read-heavy operations (like generating reports) to the replica, reducing the burden on the primary instance, which improves overall performance. Additionally, read replicas provide near real-time replication, making them ideal for real-time reporting use cases.

Option A (cross-Region read replica): This adds unnecessary latency for real-time reporting and increased costs due to cross-region data transfer.

Option B (Multi-AZ): Multi-AZ deployments are for high availability and disaster recovery but won’t offload the read traffic, as the standby database cannot serve read requests.

Option C (AWS DMS replication): This adds complexity and is not as cost-effective as using an RDS read replica for the same region.

AWS References:

Amazon RDS Read Replicas

Amazon RDS Performance Best Practices

Amazon RDS Proxy is a fully managed database proxy for RDS that makes applications more scalable, more resilient to database failures, and more secure. RDS Proxy pools and shares database connections, allowing applications to open and close connections as needed without overwhelming the database. This is the recommended solution when the application cannot be modified to use connection pooling itself.

AWS Documentation Extract:

"Amazon RDS Proxy helps manage database connections to improve application scalability and performance. It pools connections and shares them among application clients, which can mitigate issues caused by opening and closing many database connections."

(Source: Amazon RDS Proxy documentation)

A: Upgrading the instance does not solve connection inefficiency and can be cost-ineffective.

B: Increasing IOPS only helps if storage is a bottleneck, not if connections are the issue.

D: Multi-AZ improves availability, not connection management.

Comprehensive and Detailed Explanation (paraphrased from AWS Solutions Architect guidance):

Amazon Route 53 latency-based routing is specifically designed to route users to the endpoint that provides the lowest latency, based on actual latency measurements between AWS Regions and users’ networks. This directly supports the goal of optimizing load times (performance).

For non-AWS endpoints (like an on-premises data center), Route 53 lets you create a latency routing record and associate it with the AWS Region that is closest to that endpoint. In this case, the on-premises data center is near us-west-1, so you associate that latency record with the us-west-1 Region.

Users for whom the eu-central-1 Region has lower latency will be routed to the AWS website in eu-central-1.

Users for whom the us-west-1–associated endpoint has lower latency will be routed to the on-premises website.

This matches AWS best practices for high-performance, low-latency architectures when you have multiple endpoints in different geographic locations, including a mix of AWS and on-premises resources.

Why the other options are not correct:

Option A (Failover routing):

Failover routing is designed for high availability and disaster recovery (active–passive), not for optimizing latency.

All traffic goes to the primary endpoint (eu-central-1) unless it is unhealthy. That does not choose the lowest-latency endpoint for each user, so it does not meet the “optimize load times as much as possible” requirement.

Option B (IP-based routing):

IP-based / geo-based policies are about directing users based on location information (IP/geo), not measured latency.

You would have to manually decide which IP ranges get which endpoint, which does not guarantee that each user goes to the lowest-latency endpoint. It’s less precise and less dynamic than latency-based routing.

Option D (Weighted routing):

Weighted routing splits traffic according to fixed weights (e.g., 50/50), regardless of user location or latency.

This is useful for testing, blue/green deployment, or gradual traffic shifting, but not for performance optimization per user.

Therefore, the only option that directly aligns with AWS guidance for performance optimization and latency-based traffic steering across Regions and on-premises endpoints is:

✅ C. Create a DNS record with a latency-based routing policy… associate the on-premises endpoint with us-west-1.

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

A. Amazon EFS:Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP:More suited for workloads requiring NetApp-specific features.

C. Amazon EBS:Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server:Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS:Better for Linux and Unix-based workloads.