Practice Free SAA-C03 Exam Online Questions
A company is moving data from an on-premises data center to the AWS Cloud. The company must store all its data in an Amazon S3 bucket. To comply with regulations, the company must also ensure that the data will be protected against overwriting indefinitely.
Which solution will ensure that the data in the S3 bucket cannot be overwritten?
- A . Enable versioning for the S3 bucket. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to protect the data.
- B . Disable versioning for the S3 bucket. Configure S3 Object Lock for the S3 bucket with a retention period of 1 year.
- C . Enable versioning for the S3 bucket. Configure S3 Object Lock for the S3 bucket with a legal hold.
- D . Configure S3 Storage Lens for the S3 bucket. Use server-side encryption with customer-provided keys (SSE-C) to protect the data.
A
Explanation:
Versioning in S3 preserves every version of every object ― preventing permanent overwrites. This ensures compliance where data cannot be overwritten or lost. SSE-S3 ensures server-side encryption.
“When you enable versioning, Amazon S3 stores every version of every object. With versioning, you can preserve, retrieve, and restore every version of every object stored in an S3 bucket.” ― S3 Versioning
This satisfies regulatory requirements for protecting data from overwriting indefinitely.
Incorrect Options:
B: Object Lock with retention period is time-bound, not indefinite.
C: Legal hold blocks deletion but doesn’t directly prevent overwriting.
D: Storage Lens is analytics, not protection.
A company wants to restrict access to the content of its web application. The company needs to protect the content by using authorization techniques that are available on AWS. The company also wants to implement a serverless architecture for authorization and authentication that has low login latency.
The solution must integrate with the web application and serve web content globally. The application currently has a small user base, but the company expects the application’s user base to increase
Which solution will meet these requirements?
- A . Configure Amazon Cognito for authentication. Implement Lambda@Edge for authorization. Configure Amazon CloudFront to serve the web application globally
- B . Configure AWS Directory Service for Microsoft Active Directory for authentication. Implement AWS Lambda for authorization. Use an Application Load Balancer to serve the web application globally.
- C . Configure Amazon Cognito for authentication. Implement AWS Lambda for authorization Use Amazon S3 Transfer Acceleration to serve the web application globally.
- D . Configure AWS Directory Service for Microsoft Active Directory for authentication. Implement Lambda@Edge for authorization. Use AWS Elastic Beanstalk to serve the web application globally.
A
Explanation:
Amazon Cognito provides scalable, serverless authentication, and Lambda@Edgeis used for authorization, providing low-latency access control at the edge. Amazon CloudFront serves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.
Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.
Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.
Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can
handle global delivery efficiently.
AWS
Reference: Amazon Cognito
Lambda@Edge
A company runs an ecommerce application on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Amazon EC2 Auto Scaling group across multiple Availability Zones. The Auto Scaling group scales based on CPU utilization metrics. The ecommerce application stores the transaction data in a MySQL 8.0 database that is hosted on a large EC2 instance.
The database’s performance degrades quickly as application load increases. The application handles more read requests than write transactions. The company wants a solution that will automatically scale the database to meet the demand of unpredictable read workloads while maintaining high availability.
- A . Use Amazon Redshift with a single node for leader and compute functionality.
- B . Use Amazon RDS with a Single-AZ deployment. Configure Amazon RDS to add reader instances in a different Availability Zone.
- C . Use Amazon Aurora with a Multi-AZ deployment. Configure Aurora Auto Scaling with Aurora Replicas.
- D . Use Amazon ElastiCache (Memcached) with EC2 Spot Instances.
C
Explanation:
Amazon Aurora MySQLCcompatible offers a distributed, fault-tolerant storage system with Multi-AZ high availability and supports Aurora Replicas that “share the same underlying storage” for low-latency reads. Aurora provides Aurora Auto Scaling to “automatically add or remove Aurora Replicas based on load,” ideal for unpredictable, read-heavy workloads. This architecture offloads reads from the writer and maintains HA through automatic failover. Amazon RDS Single-AZ (B) lacks HA. Redshift (A) is a data warehouse, not a transactional DB. ElastiCache (D) can reduce read pressure but does not provide durable read replicas or automatic HA scaling at the database tier. Aurora’s design directly addresses the requirement to automatically scale reads while maintaining availability, matching Well-Architected guidance to use managed, elastic services for variable demand.
Reference: Amazon Aurora User Guide ― “Aurora Replicas,” “Aurora Auto Scaling,” “High availability and durability”; AWS Well-Architected Framework ― Performance Efficiency, Reliability (managed, elastic databases).
A company is developing a SaaS solution for customers. The solution runs on Amazon EC2 instances that have Amazon Elastic Block Store (Amazon EBS) volumes attached.
Within the SaaS application, customers can request how much storage they need. The application needs to allocate the amount of block storage each customer requests.
A solutions architect must design an operationally efficient solution that meets the storage scaling requirement.
Which solution will meet these requirements MOST cost-effectively?
- A . Migrate the data from the EBS volumes to an Amazon S3 bucket. Use the Amazon S3 Standard
storage class. - B . Migrate the data from the EBS volumes to an Amazon Elastic File System (Amazon EFS) file system. Use the EFS Standard storage class. Invoke an AWS Lambda function to increase the EFS volume capacity based on user input.
- C . Migrate the data from the EBS volumes to an Amazon FSx for Windows File Server file system.
Invoke an AWS Lambda function to increase the capacity of the file system based on user input. - D . Invoke an AWS Lambda function to increase the size of EBS volumes based on user input by using EBS Elastic Volumes.
D
Explanation:
EBS Elastic Volumes allow you to dynamically increase storage size, adjust performance, and change volume types without downtime, supporting operational efficiency and scalability for SaaS applications that need to allocate varying storage amounts to customers.
Migrating from EBS to S3 (Option A) is not suitable since S3 is object storage, not block storage, and does not support block-level I/O required by many applications. EFS (Option B) and FSx (Option C) are shared file systems, which might add unnecessary complexity and cost, especially if the application depends on block storage semantics.
Using Lambda to automate Elastic Volumes resizing provides cost efficiency by allocating resources on demand and reduces operational overhead, aligning with AWS operational excellence and cost optimization best practices.
Reference: AWS Well-Architected Framework ― Operational Excellence and Cost Optimization Pillars (https: //d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf) Amazon EBS Elastic Volumes (https: //docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-expand-volume.html)
AWS Lambda Overview (https: //docs.aws.amazon.com/lambda/latest/dg/welcome.html)
A company hosts a web application on an on-premises server that processes incoming requests.
Processing time for each request varies from 5 minutes to 20 minutes.
The number of requests is growing. The company wants to move the application to AWS. The company wants to update the architecture to scale automatically.
- A . Convert the application to a microservices architecture that uses containers. Use Amazon Elastic Container Service (Amazon ECS) with the AWS Fargate launch type to run the containerized web application. Configure Service Auto Scaling. Use an Application Load Balancer to distribute incoming requests.
- B . Create an Amazon EC2 instance that has sufficient CPU and RAM capacity to run the application. Create metrics to track usage. Create alarms to notify the company when usage exceeds a specified threshold. Replace the EC2 instance with a larger instance size in the same family when usage is too high.
- C . Refactor the web application to use multiple AWS Lambda functions. Use an Amazon API Gateway REST API as an entry point to the Lambda functions.
- D . Refactor the web application to use a single AWS Lambda function. Use an Amazon API Gateway HTTP API as an entry point to the Lambda function.
A
Explanation:
AWS Fargate runs containers without managing servers, and ECS Service Auto Scaling adjusts the number of tasks based on demand. Behind an Application Load Balancer, ECS services elastically scale out to handle concurrent long-running requests. This fits workloads with processing times of 5C 20 minutes per request. Lambda (C, D) has a maximum 15-minute timeout, so a portion of requests would fail or require complex refactoring and orchestration.
Option B relies on vertical scaling and manual intervention, leaving capacity idle and failing to scale automatically with spikes. By containerizing the app and running on ECS with Fargate, the company gains automatic scaling, isolation per task, and no instance management, ensuring reliable throughput as request volume grows while keeping operations minimal.
A company runs a monolithic application in its on-premises data center. The company used Java/Tomcat to build the application. The application uses Microsoft SQL Server as a database. The company wants to migrate the application to AWS.
Which solution will meet this requirement with the LEAST operational overhead?
- A . Use AWS App2Container to containerize the application. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS). Deploy the database to Amazon RDS for SQL Server. Configure a Multi-AZ deployment.
- B . Containerize the application and deploy the application on a self-managed Kubernetes cluster on an Amazon EC2 instance. Deploy the database on a separate EC2 instance. Set up Microsoft SQL Server Always On availability groups.
- C . Deploy the frontend of the web application as a website on Amazon S3. Use Amazon DynamoDB for the database tier.
- D . Use AWS App2Container to containerize the application. Deploy the application on Amazon Elastic Kubernetes Service (Amazon EKS). Use Amazon DynamoDB for the database tier.
A
Explanation:
AWS App2Container is a command-line tool that helps containerize existing Java and .NET applications running on-premises or on virtual machines, with minimal refactoring. By using Amazon EKS, the company benefits from a managed Kubernetes service, which significantly reduces the operational overhead compared to managing Kubernetes on EC2.
Amazon RDS for SQL Server provides a fully managed SQL Server database engine with automated backups, patching, and high availability through Multi-AZ deployments. This eliminates the need for the company to manage database infrastructure and software manually.
Overall, option A provides the most streamlined and managed approach for both the application and database layers with the least operational effort.
An ecommerce company hosts an API that handles sales requests. The company hosts the API frontend on Amazon EC2 instances that run behind an Application Load Balancer (ALB). The company hosts the API backend on EC2 instances that perform the transactions. The backend tiers are loosely coupled by an Amazon Simple Queue Service (Amazon SQS) queue.
The company anticipates a significant increase in request volume during a new product launch event.
The company wants to ensure that the API can handle increased loads successfully.
Options:
- A . Double the number of frontend and backend EC2 instances to handle the increased traffic during the product launch event. Create a dead-letter queue to retain unprocessed sales requests when the demand exceeds the system capacity.
- B . Place the frontend EC2 instances into an Auto Scaling group. Create an Auto Scaling policy to launch new instances to handle the incoming network traffic.
- C . Place the frontend EC2 instances into an Auto Scaling group. Add an Amazon ElastiCache cluster in front of the ALB to reduce the amount of traffic the API needs to handle.
- D . Place the frontend and backend EC2 instances into separate Auto Scaling groups. Create a policy for the frontend Auto Scaling group to launch instances based on incoming network traffic. Create a policy for the backend Auto Scaling group to launch instances based on the SQS queue backlog.
D
Explanation:
Comprehensive and Detailed
To handle increased loads effectively, it’s essential to implement Auto Scaling for both frontend and backend tiers:
Frontend Auto Scaling Group: Scaling based on incoming network traffic ensures that the application can handle increased user requests.
Backend Auto Scaling Group: Scaling based on the Amazon SQS queue backlog ensures that the backend can process messages as they arrive, preventing delays.
This approach allows each tier to scale independently based on its specific load, ensuring optimal resource utilization and performance.
Reference: Tutorial: Set up a scaled and load-balanced application Scaling policy based on Amazon SQSAWS Documentation
A company runs several custom applications on Amazon EC2 instances. Each team within the company manages its own set of applications and backups. To comply with regulations, the company must be able to report on the status of backups and ensure that backups are encrypted.
Which solution will meet these requirements with the LEAST effort?
- A . Create an AWS Lambda function that processes AWS Config events. Configure the Lambda function to query AWS Config for backup-related data and to generate daily reports.
- B . Check the backup status of the EC2 instances daily by reviewing the backup configurations in AWS Backup and Amazon Elastic Block Store (Amazon EBS) snapshots.
- C . Use an AWS Lambda function to query Amazon EBS snapshots, Amazon RDS snapshots, and AWS Backup jobs. Configure the Lambda function to process and report on the data. Schedule the function to run daily.
- D . Use AWS Config and AWS Backup Audit Manager to ensure compliance. Review generated reports daily.
D
Explanation:
AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution. From AWS Documentation:
“AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status.”
(Source: AWS Backup Audit Manager User Guide)
Why D is correct:
Ensures centralized visibility into all backup jobs.
Verifies encryption status automatically.
Generates ready-to-use reports with minimal operational overhead.
Complies with regulatory requirements for data protection.
Why others are incorrect:
A & C: Custom Lambda automation increases maintenance effort.
B: Manual checking is operationally inefficient and error-prone.
Reference: AWS Backup Audit Manager User Guide
AWS Config Documentation C “Compliance and Monitoring” AWS Well-Architected Framework C Operational Excellence Pillar
A company deploys an application on Amazon EC2 Spot Instances. The company observes frequent unavailability issues that affect the application’s output. The application instances all use the same instance type in a single Availability Zone. The application architecture does not require the use of any specific instance family.
The company needs a solution to improve the availability of the application.
Which combination of steps will meet this requirement MOST cost-effectively? (Select THREE.)
- A . Create an EC2 Auto Scaling group that includes a mix of Spot Instances and a base number of On-Demand Instances.
- B . Create EC2 Capacity Reservations.
- C . Use the lowest price allocation strategy for Spot Instances.
- D . Specify similarly sized instance types and Availability Zones for the Spot Instances.
- E . Use a different instance type for the web application.
- F . Use the price capacity optimized strategy for Spot Instances.
A, D, F
Explanation:
AWS Spot best practices recommend diversifying capacity across multiple instance types and Availability Zones and using the capacity-optimized (price-capacity-optimized) allocation strategy to choose pools with the deepest capacity for higher availability. Adding a small On-Demand base in the Auto Scaling group maintains steady, uninterrupted baseline processing while keeping costs low and absorbing Spot interruptions.
Option C (lowest price) increases interruption risk. Capacity Reservations (B) target On-Demand capacity guarantees and add cost, not needed for Spot-based elasticity.
Option E is redundant; diversification is already achieved with (D). This combination maximizes resiliency of Spot workloads while preserving strong cost efficiency and aligns with AWS guidance for fault-tolerant, stateless applications on Spot.
A company has an ordering application that stores customer information in Amazon RDS for MySQL. During regular business hours, employees run one-time queries for reporting purposes. Timeouts are occurring during order processing because the reporting queries are taking a long time to run. The company needs to eliminate the timeouts without preventing employees from performing queries.
- A . Create a read replica. Move reporting queries to the read replica.
- B . Create a read replica. Distribute the ordering application to the primary DB instance and the read replica.
- C . Migrate the ordering application to Amazon DynamoDB with on-demand capacity.
- D . Schedule the reporting queries for non-peak hours.
A
Explanation:
Comprehensive and Detailed
Amazon RDS for MySQL supports the creation of read replicas, which are read-only copies of the primary database instance. By offloading read-heavy operations, such as reporting queries, to a read replica:
Performance Improvement: The primary DB instance is relieved from the additional load, reducing the likelihood of timeouts during order processing.
Data Consistency: Read replicas use asynchronous replication, ensuring that they have up-to-date data for accurate reporting.
Scalability: Multiple read replicas can be created to handle increased read traffic.
This approach allows employees to continue running necessary reports without impacting the performance of the ordering application.