Pass the Amazon Web Services AWS Certified Associate SAA-C03 Questions and answers with CertsForce

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, usingAWS Batchis the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

A. API Gateway + DynamoDB:Provides high scalability, low latency, and seamless integration with Amazon Comprehend for NLP tasks.

B. HTTP API + Translate + ElastiCache:Translate is not relevant for NLP tasks like sentiment analysis or entity recognition. ElastiCache is unsuitable for permanent storage.

C. SQS + EC2 + RDS:Increases complexity and operational overhead. RDS may not scale effectively for high concurrent loads.

D. WebSocket API + Textract:Textract is irrelevant for NLP tasks. WebSocket API is not the optimal choice for this use case.

This solution meets the requirements of providing encryption at both the network and session layers while also allowing for controlled access between on-premises systems and AWS resources.

AWS Site-to-Site VPN: This service allows you to establish a secure and encrypted connection between your on-premises network and AWS VPC over the internet or via AWS Direct Connect. The VPN encrypts data at the network layer (IPsec) as it travels between the corporate network and AWS.

Routing and Security Controls: By configuring route table entries, you can ensure that only the traffic intended for AWS resources is directed to the VPC. Additionally, by setting up security groups and network ACLs, you can further restrict and control which traffic is allowed to communicate with the instances within your VPC. This approach provides the necessary security to prevent unrestricted access, aligning with the company’s security policies.

Why Not Other Options?:

Option A (AWS Direct Connect): While Direct Connect provides a private connection, it does not inherently provide encryption. Additional steps would be required to encrypt traffic, and it doesn’t address the session layer encryption.

Option B (IAM policies for Console access): This option does not meet the requirement for network-level encryption and security between the corporate network and the VPC.

Option D (AWS Transit Gateway): Although Transit Gateway can help in managing multiple connections, it doesn’t directly provide encryption at the network layer. You would still need to configure a VPN or use other methods for encryption.

AWS References:

AWS Site-to-Site VPN- Overview of AWS Site-to-Site VPN capabilities, including encryption.

Security Groups and Network ACLs- Information on configuring security groups and network ACLs to control traffic.

For steady, predictable EC2 usage with potential changes in instance families over time, AWS recommends Savings Plans. Compute Savings Plans “apply to any EC2 instance regardless of region, instance family, operating system, or tenancy,” and also apply to AWS Fargate and AWS Lambda, delivering the most flexibility over a multi-year horizon. A 3-year term provides the highest discount among Savings Plans for long-lived workloads. EC2 Instance Savings Plans are limited to a chosen instance family in a region; as needs evolve (e.g., size or family changes), discounts may not fully apply. Spot Instances are not appropriate for long, interruption-sensitive jobs because Spot capacity can be reclaimed with short notice. Therefore, a Compute Savings Plan (3-year) best matches cost optimization with flexibility for growth and changes.

AWS Secrets Manager is the managed service for securely storing, rotating, and retrieving database credentials. When you store Aurora credentials in Secrets Manager and enable automatic rotation, Secrets Manager updates the credentials in both the database and the stored secret.

Each Lambda function version or alias can call Secrets Manager at runtime to retrieve the current secret value, so even older deployed Lambda versions that use an alias will always obtain the most recent credentials without redeployment.

Why others are not suitable:

B: Embeds credentials in code, requiring redeployment on every rotation and violating security best practices.

C: Environment variables are version-specific; old aliases would continue using outdated values unless you redeploy or change them.

D: Parameter Store can store and rotate secrets but is less integrated for database credential rotation than Secrets Manager; Secrets Manager is the purpose-built minimal-overhead choice here.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

To analyze the performance of the web application with granularity of no more than 2 minutes, enablingdetailed monitoringon EC2 instances is the best solution. By default, CloudWatch provides metrics at a 5-minute interval. Enabling detailed monitoring allows you to collect metrics at 1-minute intervals, which will give you the level of granularity you need to analyze performance during peak traffic.

Amazon CloudWatch metrics can then be used to analyze CPU utilization, memory usage, disk I/O, and network throughput, among other performance-related metrics, at the desired granularity.

Option A: Sending CloudWatch logs to Redshift for analysis is unnecessary and overcomplicated for simple performance analysis, which can be done using CloudWatch metrics alone.

Option C: Fetching EC2 logs via Lambda adds complexity, and CloudWatch metrics already provide the required data for performance analysis.

Option D: Sending logs to S3 and using Redshift for analysis is also more complex than necessary for simple performance monitoring.

AWS References:

Monitoring Amazon EC2 with CloudWatch

Amazon CloudWatch Detailed Monitoring

To optimize costs for both Amazon EC2 and AWS Fargate, the best option is a Compute Savings Plan because it offers flexibility across instance families, Regions, and compute options including EC2, AWS Fargate, and AWS Lambda.

Unlike EC2 Instance Savings Plans, which apply only to specific instance families, Compute Savings Plans apply across multiple services.

Since the company does not want to commit to multi-year contracts or large upfront payments, the 1-year No Upfront Compute Savings Plan provides the greatest flexibility with no upfront capital commitment, while still offering cost savings over On-Demand pricing.

This option also aligns with cost-optimization best practices by allowing for scalability and service mix flexibility.

???? Reference:

AWS Compute Savings Plans

AWS Pricing Models

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.

This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.

Amazon GuardDuty includes RDS Protection that “monitors and profiles access to your Amazon Aurora and Amazon RDS databases” to detect threats such as suspicious login attempts, brute-force activity, and anomalous authentication patterns. GuardDuty can be enabled organization-wide in AWS Organizations with a delegated administrator to centralize findings for all member accounts, minimizing operational overhead. Findings include context like source IP, user, and DB instance, and integrate with Amazon EventBridge for alerting and automated response. SCPs (A) enforce or deny API permissions but do not provide detection/analytics. Exporting general logs (C) requires building and maintaining custom parsing/analytics pipelines. CloudTrail (D) records AWS control-plane API calls and does not log database-level login attempts. Therefore, enabling GuardDuty RDS Protection across the org provides the most operationally efficient, managed detection of abnormal failed and incomplete login attempts.

Storage Gateway Volume Gateway (Cached Volumes): This configuration allows you to store your primary data in Amazon S3 while retaining frequently accessed data locally in a cache for low-latency access.

Low-Latency Access: Frequently accessed data is cached locally on-premises, providing low-latency access while the less frequently accessed data is stored cost-effectively in Amazon S3.

Implementation:

Deploy a Storage Gateway appliance on-premises or in a virtual environment.

Configure it as a volume gateway with cached volumes.

Create volumes and configure your applications to use these volumes.

Minimal Infrastructure Changes: This solution integrates seamlessly with existing on-premises infrastructure, requiring minimal changes and reducing dependency on on-premises storage servers.

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.

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.

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family's structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying