Free Practice Questions for Amazon Web Services SAA-C03 Exam

AWS documentation states that Amazon Macie is the managed service designed to automatically identify and classify sensitive data stored in Amazon S3, including PII and healthcare-related identifiers.

Storing logs in Amazon S3 provides scalable, durable storage, and Amazon Athena can directly query JSON data stored in S3 using SQL.

Amazon Inspector (Option D) is for vulnerability scanning and does not identify sensitive data. DynamoDB with KMS (Option C) cannot detect sensitive information. EBS (Option B) requires custom tooling and does not support serverless SQL querying.

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The ALB must be in a public subnet to receive internet traffic. The EC2 instances and the RDS database should be in private subnets to prevent direct internet access, minimizing the attack surface. This aligns with AWS security best practices for web application architectures.

Reference Extract:

"Internet-facing ALBs should be placed in public subnets; EC2 instances and RDS databases should be in private subnets to restrict direct internet access."

Source: AWS Certified Solutions Architect – Official Study Guide, Network Security and Design section.

Aurora blue/green deployments are specifically designed for safe schema changes, zero-downtime updates, and production isolation.

The staging (green) environment can receive schema changes without affecting production (blue). After validation, you perform a fast, minimally disruptive switchover that updates production.

Read replicas (Option B) do not allow schema changes. Creating an independent staging cluster (Option A) does not provide automated, low-downtime cutover. DynamoDB (Option D) is not compatible with MySQL schemas.

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Option Aallows importing your own encryption keys into AWS KMS, ensuring control over key material.

Option Duses S3 Glacier with SSE-C, where the customer controls the encryption keys, meeting compliance needs.

Option Buses AWS-managed key material, violating the requirement for key material control.

Option C and Eare not fully compliant with the control requirement.

AWS WAF allows web applications to protect themselves from common web exploits and vulnerabilities. Using AWS managed rule groups ensures protection against known attack patterns, such as SQL injection and cross-site scripting. Associating AWS WAF with the ALB provides application-layer security and real-time threat mitigation.

For Amazon RDS for PostgreSQL, AWS provides IAM database authentication. With this feature, applications do not use stored long-term usernames and passwords. Instead, they use temporary authentication tokens that are generated by AWS and validated by the RDS database.

The AWS best practice pattern is:

Attach an IAM role to the EC2 instances (instance profile).

Grant that role the necessary permissions (for example, rds-db:connect) to the specific RDS database user.

The application running on the EC2 instance uses the role’s temporary credentials to call the RDS token-generation API and obtain a short-lived authentication token.

The application then uses this token as the password when connecting to RDS for PostgreSQL.

This removes the need to store long-term credentials in the application or on the instance and uses IAM roles with temporary credentials, aligning with the security requirement.

Option A still relies on stored credentials (even if in Secrets Manager), which are long-lived and rotated but not token-based per-connection IAM authentication.

Option B uses static passwords and IP-based access, which does not meet the “no long-term credentials” requirement.

Option C stores long-term IAM user keys on the instances, which is explicitly against best practices and does not directly integrate with RDS authentication.

Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven’t been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).

“Amazon EFS Lifecycle Management automatically moves files that haven’t been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files.”

— Amazon EFS Documentation

Key Points:

EFS IA is ideal for files larger than 128 KB and accessed less frequently.

It’s seamless — no code or tools needed.

Meets requirement for cost optimization and high availability.

Incorrect Options:

A: File Gateway adds unnecessary complexity and does not use EFS.

B: Storing files locally breaks shared access and resiliency.

D: Glacier Deep Archive is cold storage — not "readily available."

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.

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.

Using an Application Load Balancer (ALB) allows for integration with AWS WAF to inspect all incoming traffic. Running the application on Amazon ECS provides a scalable and managed container orchestration service. Utilizing Amazon Aurora Serverless for the PostgreSQL database offers automatic scaling based on application demand, which is cost-effective for workloads with variable traffic patterns, such as higher traffic on weekdays and lower traffic on weekends.

AWS Transit Gateway simplifies network connectivity by acting as a hub that can connect VPCs and on-premises networks through VPN or Direct Connect. It provides scalability and reduces administrative overhead by eliminating the need to manage complex peering relationships as the number of accounts and VPCs grows.

AmazonKinesis Data Streamsis the best choice for this scenario:

Message throughput: Kinesis Data Streams supports high throughput with enhanced fan-out and dedicated throughput for consumers.

Large message size: Supports message sizes up to 1 MB, meeting the 500 KB requirement.

Message retention: Data streams can retain messages for up to 365 days.

Strict ordering: Guarantees message ordering within shards.

Why Other Options Are Not Ideal:

Option B:

While SQS FIFO supports strict ordering, SNS topics do not. SNS also does not natively support message retention or strict ordering across consumers.Does not meet requirements.

Option C:

EventBridge does not provide strict ordering guarantees or message retention beyond 24 hours.Does not meet requirements.

Option D:

SNS topics with Data Firehose are not designed for use cases requiring strict ordering or long message retention.Does not meet requirements.

AWS References:

Amazon Kinesis Data Streams:AWS Documentation - Kinesis Data Streams

AWS Messaging Services Comparison:AWS Documentation - Messaging Services

Using API Gateway with API keys provides secure access control. Amazon SQS allows asynchronous decoupling between frontend and backend, ensuring that backend processing can scale independently. AWS Lambda reading from SQS ensures scalable, event-driven processing with minimal operational management. This architecture is resilient and decoupled.

Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization.

High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on-premises performance data. Memory optimized instances provide the necessary memory capacity and performance.

Instance Types:

For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.

For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.

Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.

Amazon EFS provides a shared, elastic, low-latency file system that can be mounted concurrently by many EC2 instances across multiple Availability Zones, delivering strong read-after-write consistency so all instances see updates almost immediately. This is the standard pattern for CMS-style workloads that require shared, up-to-date assets with minimal lag. Syncing local copies from S3 (C) introduces polling windows and eventual consistency delays; hourly sync is not near-real time. Copying from a “newest instance” (A) is brittle and not scalable. EBS volumes/snapshots (D) are single-instance, single-AZ block devices and not designed for multi-writer sharing across instances/AZs. EFS’s multi-AZ design and POSIX semantics provide the simplest, most reliable solution with the least operational overhead.