SAA-C03 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

Amazon S3 Storage Lens with advanced activity metrics is the most operationally efficient answer because AWS documentation explicitly says you can use S3 Storage Lens advanced metrics to identify “cold” buckets, meaning buckets whose objects are no longer accessed or are accessed only rarely. That is exactly the requirement in the question. This avoids building a custom analytics workflow on top of CloudTrail or CloudWatch metrics. The standard S3 console does not provide the same purpose-built fleet-level analysis for cold-bucket identification. Athena and CloudTrail can be used for access analysis, but they require more setup, data handling, and query maintenance. S3 Storage Lens is the native managed feature for this storage-optimization scenario. (AWS Documentation)

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Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ( " green " ) of your production environment ( " blue " ) where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.

Reference Extract from AWS Documentation / Study Guide:

" Amazon RDS Blue/Green Deployments enable you to make changes to your database environment safely. You can perform schema updates and feature testing in a fully managed staging environment and switch over with minimal downtime, ensuring the highest availability. "

Source: AWS Certified Solutions Architect – Official Study Guide, Database and Migration section; Amazon RDS Blue/Green Deployments Documentation.

Background Jobs with Event Invocation Type:

The Event invocation type is asynchronous, meaning the Lambda function does not send an immediate result to the API Gateway and processes the request in the background. This is ideal for video encoding tasks that take time.

REST API vs. HTTP API:

REST APIs support advanced features like API keys, request validation, and throttling that HTTP APIs do not support fully.

Since the developer needs API keys and request validations, a REST API is the correct choice.

Integration with Lambda:

AWS Lambda integration is seamless with REST APIs, and using the Event invocation ensures asynchronous processing.

Incorrect Options Analysis:

Option A: HTTP API lacks full support for API keys and validation.

Option CandD: RequestResponse invocation type requires immediate responses, unsuitable for background jobs.

AWS DataSync is the best fit because it is a managed service designed for repeated data movement between supported storage systems, includingAmazon S3andAmazon EFS. AWS documents that DataSync tasks can be configured to transferonly data that has changedby using theCHANGEDtransfer mode , which matches the overwrite-if-changed requirement. That removes the need to build custom synchronization code with Lambda or EC2. DataSync also has native support for S3 locations and EFS locations, so it directly covers both destinations in the question. Option A is therefore the lowest-overhead and most maintainable solution.

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Amazon Aurora MySQL–compatible 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.

AWS recommends usingSystems Manager Session Managerinstead of direct SSH for administrative access because it provides browser-based or CLI-based access without bastion hosts and supports centralized audit logging. To allow private instances to reach Systems Manager without internet exposure, the VPC needs the appropriateinterface VPC endpoint. The managed instances also need the proper instance permissions, and AWS documentation points to theAmazonSSMManagedInstanceCorepolicy for that purpose. A gateway endpoint is not used for Session Manager, and attaching a user-facing Session Manager policy to the EC2 instances themselves is the wrong permission model. This makesB and Ethe most operationally efficient and secure combination. (AWS Documentation)

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Compute Savings Plansoffer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, usingOn-Demand Instancesensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS ' s cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

Amazon SQS is the AWS-recommended service for handling decoupled, scalable, ordered, durable message ingestion with potentially large workloads. It supports payloads up to 256 KB directly and up to several MB using S3-backed large-payload extensions.

Lambda functions can process messages from SQS with automatic scaling. ECS with the Fargate launch type provides a serverless container platform for the frontend, scaling based on demand without managing servers.

SNS (Options B and D) is not a queue, does not provide durable message buffering, and is not appropriate for variable, bursty workloads that may exceed backend throughput. Lambda@Edge (Options A and D) is unsuitable for backend order processing because it runs at CloudFront edge locations with strict limitations.

The critical requirement is immediate processing upon customer submission, with a typical compute duration of about 10 seconds. This is an ideal fit for a synchronous, request-driven, serverless pattern: API Gateway + AWS Lambda. API Gateway provides a managed HTTPS front door for the application, handles request validation/throttling if needed, and integrates directly with Lambda. Lambda is designed for short-lived computations, scales automatically with request volume, and can complete 10-second tasks well within typical Lambda execution limits.

Option A therefore meets the requirement with low latency and minimal operational overhead. When a customer submits data, API Gateway invokes the Lambda function immediately, and the function returns the calculated premium response. This preserves an interactive user experience and avoids provisioning or managing servers. It also handles bursts gracefully because Lambda concurrency can scale quickly, and API Gateway can absorb high request rates.

Option B is unsuitable because launching a Spot instance per upload introduces significant startup latency (instance launch time) and is not reliable due to Spot interruptions. Option C and D are not “immediate”: Transfer Family is for file transfers, and scheduled EKS jobs or Batch jobs introduce queuing and scheduling delays. While Batch can be event-driven, it is typically used for longer-running or batch-oriented workloads and still has job-start overhead compared to Lambda.

Therefore, A best matches the requirements for immediate execution, scalability, and low operational burden while maintaining responsive performance for customers.

Analysis:

The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.

Why Option A is Correct:

API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.

AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.

Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.

Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.

Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.

Why Other Options Are Not Ideal:

Option B:

Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.

Option C:

EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.

Option D:

Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.

AWS References:

Amazon API Gateway:AWS Documentation - API Gateway

AWS Lambda:AWS Documentation - Lambda

Amazon SQS:AWS Documentation - SQS

Amazon Aurora:AWS Documentation - Aurora

Workload Discovery on AWS is a tool that automatically discovers AWS resources across multiple accounts and Regions and builds visual architecture diagrams that show resource relationships. It is specifically designed to help teams understand and document existing workloads with minimal manual effort.

This service provides multi-account visibility, relationship mapping, and exportable diagrams, which directly satisfies the need to “build and map the relationship details of the various workloads” with the least operational overhead.

Systems Manager Inventory (Option A) focuses on collecting configuration and inventory data (primarily for instances and some resources) and does not generate architecture diagrams or full relationship views.

Step Functions (Option B) and X-Ray (Option D) would require custom collection and manual diagramming and are not purpose-built for environment discovery and mapping.

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

The requirement to “have access to the operating system” means the compute layer must be Amazon EC2 (or containers on EC2). Managed runtimes such as Lambda and Fargate do not provide OS-level access.

The requirement for a “low-latency NoSQL database” maps directly to Amazon DynamoDB, which is a fully managed NoSQL key-value and document database that provides single-digit millisecond latency at any scale.

Because the application runs in a private subnet, AWS best practice is to access DynamoDB privately via a VPC endpoint (gateway endpoint for DynamoDB). This avoids traversing the public internet and simplifies security.

An instance profile (EC2 role) is the recommended method to grant EC2 instances permission to access DynamoDB without hardcoding credentials.

Why the other options are not correct:

B: Lambda does not provide OS access, which violates the security requirement.

C: Fargate does not provide OS access, and Aurora PostgreSQL is a relational database, not NoSQL.

D: S3 + Athena is an analytics pattern, not a low-latency NoSQL database solution; query latency is much higher and not suitable for OLTP-style app storage.

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​​.

Amazon Aurora Serverless v2 provides cost-effective, on-demand, and auto-scaling database capacity. You pay only for actual usage, making it ideal for development and test environments that are not used continuously. It supports PostgreSQL and is suitable for variable and short-duration workloads, minimizing costs when databases are idle.

AWS Documentation Extract:

“Amazon Aurora Serverless v2 automatically adjusts database capacity based on application needs, ideal for development and test environments with intermittent usage.”

(Source: Aurora Serverless v2 documentation)

B: RDS instances run and accrue charges even when idle.

C, D: Aurora clusters/global databases are overkill and incur higher costs for dev/test.

The delay is happening during heavy stream-driven updates, so the two most relevant actions are increasing write throughput and tuning stream consumption. DynamoDB Streams invokes Lambda in batches, and batch size affects how efficiently records are processed. Increasing write capacity also helps when the target table is receiving more update traffic during peak periods. A global secondary index on Orders does not solve the stream-processing bottleneck, and increasing read capacity is unrelated because the problem is delayed updates, not throttled reads. Increasing Lambda timeout only helps if the function is timing out, which is not the main throughput optimization here. Therefore, the most practical improvements are better batching behavior and more write capacity.

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The requirement issecure at-least-once processing, not exactly-once processing. Amazon SQSstandard queuesprovide at-least-once delivery, which matches the requirement and is generally more cost-effective than FIFO queues. When Lambda is configured with an encrypted SQS queue that usesSSE-KMS, AWS requires the Lambda execution role to have thekms:Decryptpermission so Lambda can poll and process messages successfully. FIFO queues add ordering and deduplication features that are not required here, so they are not the most economical choice. Option A is therefore the best fit because it combines the correct delivery model, queue encryption with AWS KMS, and the necessary IAM permission for Lambda.

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Aservice control policyis the correct organization-wide control because SCPs define the maximum available permissions across member accounts. AWS documentation also notes that in SCPs you can use the global condition keyaws:PrincipalArnin theConditionelement to identify specific principals, including roles. That makes option D the correct pattern: deny modification of Lambda functions for everyone except the designated security-team role. An SCP that denies all changes with no exception would block the security team too. The Principal element is not the right way to express this exception in an SCP for this use case. Therefore, the correct answer is an SCP with an exception in the Condition clause for the security team role.

AWS recommends using anIAM role attached to the EC2 instanceso the application can obtaintemporary credentialsautomatically instead of storing long-term access keys in code or configuration. That directly follows AWS security guidance for workloads running on EC2. The role should grant only the specific S3 permissions the application needs, which satisfies the principle of least privilege. Administrative access is too broad, and IAM users with access keys are less secure because they require credential distribution and rotation. The best-practice design is therefore to attach an IAM role with only the required S3 actions to the instance profile and let the SDK retrieve temporary credentials from the instance metadata service.

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Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.