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

EC2 Auto Scaling warm pools allow you to pre-initialize instances, reducing the delay in scale-out events. This results in significantly faster response times during demand surges while remaining cost-effective compared to always running at peak capacity.

Amazon DynamoDB supports two read consistency models: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventual consistency, which means reads might not immediately reflect the most recent writes. This behavior explains why the application is not always receiving the latest data.

Because latency and performance are acceptable, the problem is not throughput or response time—it is data consistency. Therefore, the correct design change is to enable strongly consistent reads, which guarantee that reads always return the most up-to-date data after a successful write.

Option C is correct because strongly consistent reads ensure that the application sees the latest committed value for an item. This is critical for use cases such as inventory systems, user profiles, or transactional workflows where stale data can cause incorrect behavior.

Option A is invalid because DynamoDB does not support traditional read replicas. Option B does not affect consistency; GSIs are used to query data by alternative keys. Option D would worsen the problem because eventual consistency already causes stale reads.

Therefore, C is the appropriate recommendation to ensure correctness of returned data without changing the table schema or introducing additional complexity.

For bidirectional communication such as chat applications, Amazon API Gateway WebSocket API is the correct service. WebSocket APIs allow clients to establish long-lived connections and exchange messages with the backend Lambda functions in real time.

HTTP APIs and REST APIs are suitable for request-response models, not continuous two-way communication. CloudFront cannot maintain stateful WebSocket connections, so only Option C fits the requirements for a real-time, bidirectional application.

Detailed Explanation:

A. EventBridge Rule:Detects modifications to CloudFront distributions in real time and triggers the Lambda function for further action.

B. ALB + Config:Focuses on ALB security violations, not relevant for CloudFront logging changes.

C. Lambda + SNS:Provides real-time notifications about changes in logging configuration.

D. GuardDuty:Focuses on threat detection, not logging configuration changes.

E. API Gateway + WAF:Unrelated to CloudFront logging changes.

Amazon RDS Multi-AZ deployments provide automatic failover for relational databases such as MySQL, ensuring high availability and durability. The feature maintains synchronous replication between a primary DB instance and a standby in a separate Availability Zone. AWS guarantees that failover typically completes within minutes, ensuring an RTO and RPO of less than 2 minutes. Option A requires manual promotion of replicas, which cannot meet the strict RTO/RPO requirement. Option C depends on custom scripts and manual synchronization, introducing operational risk. Option D creates active-active EC2-based databases, which do not provide synchronous replication or automated failover. Therefore, Multi-AZ RDS (B) is the managed, resilient, and operationally efficient solution that meets the business requirements.

The question asks for the most cost savings while not affecting application performance, and it explicitly calls out both compute costs and storage costs increasing. The best answer is the option that optimizes both cost drivers without reducing capacity needed for peak performance. Option D accomplishes this by combining two well-established cost optimization mechanisms: a Compute Savings Plan for baseline compute usage and S3 Lifecycle policies for lower-cost storage of older receipt images.

A Compute Savings Plan is best applied to the steady, always-needed portion of compute capacity. In an Auto Scaling group, there is typically a predictable minimum footprint that runs continuously. Purchasing a Savings Plan for that baseline delivers substantial discounts compared with On-Demand pricing while keeping performance unchanged. For traffic spikes, continuing to use On-Demand Instances is operationally simple and preserves scaling behavior and customer experience. This avoids the risk that reducing minimum capacity could introduce slower scale-out or insufficient resources during sudden load.

On the storage side, raw receipt images are stored in Amazon S3, and many applications access the newest objects most frequently. Using S3 Lifecycle policies to transition objects after 30 days to lower-cost tiers (such as infrequent access or archival tiers as appropriate for retrieval needs) reduces storage spend without changing how new uploads are handled or processed. It also keeps the application fast for recent receipts, where performance typically matters most.

Options A and B move storage to EFS, which is generally more expensive than S3 for object storage patterns and does not align with “stored as objects.” Option C reduces the maximum Auto Scaling capacity, which can directly harm performance during demand surges. Therefore, D provides the largest cost reduction while preserving performance and scaling characteristics.

The workload described is event-driven, low volume, and sporadic, making serverless architecture the most cost-effective choice. AWS Lambda, when triggered by S3 event notifications, runs only when new photos are uploaded and incurs cost only for execution time.

Option C uses S3 event notifications to invoke a Lambda function whenever a new object is created. The Lambda function generates a thumbnail and uploads it to a second S3 bucket. This solution requires no servers, no scheduling, and no idle compute costs, making it extremely cost-efficient for 150 uploads per day.

Options A and B involve running long-lived compute resources that would remain idle most of the time, resulting in unnecessary cost. Option D is invalid because S3 Storage Lens is designed for storage analytics and reporting, not event-driven processing.

Therefore, C is the most cost-effective and operationally efficient solution.

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

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.

The correct answer is D because the application runs on AWS Fargate On-Demand capacity , cannot tolerate interruptions, and the company wants to optimize costs while keeping the application operational. A Compute Savings Plan is designed to reduce costs for consistent compute usage across AWS services, including AWS Fargate , without requiring changes to the application architecture or using interruptible capacity.

A Savings Plan provides discounted pricing in exchange for a usage commitment over a period of time. This is ideal for workloads that must continue running without interruption because the application can remain on Fargate On-Demand , which avoids the termination risk associated with Spot capacity. The company gets cost savings while preserving the reliability characteristics of the current deployment model.

Option A is incorrect because On-Demand Capacity Reservations are used to reserve EC2 capacity in a specific Availability Zone and do not apply as the main cost optimization mechanism for Fargate tasks. Option B is incorrect because Convertible Reserved Instances apply to Amazon EC2, not Fargate. Option C is incorrect because Fargate Spot can be interrupted and therefore does not meet the requirement that the application cannot tolerate sudden interruptions.

AWS cost optimization guidance recommends selecting discount models that align with workload needs. For predictable, steady serverless container usage on Fargate that must remain highly available, Compute Savings Plans offer the best balance of lower cost and uninterrupted operation. Therefore, purchasing a Compute Savings Plan is the most appropriate solution.

Amazon Athena enables serverless SQL queries directly on S3 objects, eliminating the need to download or preprocess data. EMR adds operational overhead, and API Gateway/ElastiCache are not data lake query engines.

AWS Step Functions provides a low-code, fully managed workflow service with a visual interface to orchestrate AWS Glue jobs in sequence. Step Functions integrates natively with AWS Glue and can be triggered by Amazon EventBridge based on events or schedules.

AWS Documentation Extract:

“AWS Step Functions makes it easy to coordinate multiple AWS services into serverless workflows so you can build and update apps quickly. Step Functions provides visual workflows and integrates with AWS Glue for ETL orchestration.”

(Source: AWS Step Functions documentation)

A: Manual scripting and dashboards do not provide a low-code or integrated visual workflow.

C: QuickSight is for BI, not workflow visualization.

D: ECS adds unnecessary complexity.

Amazon SQS is a fully managed message queuing service that reliably decouples and scales microservices, distributed systems, and serverless applications. By using SQS, microservices can communicate asynchronously, handle bursts of traffic, and avoid data loss by buffering messages until they are processed. Deploying the services on ECS with AWS Fargate further reduces operational overhead by removing the need to manage servers, allowing independent scaling of each microservice.

Reference Extract:

" Amazon SQS decouples application components and enables message durability and scaling. AWS Fargate removes the need to manage infrastructure, supporting independent scaling and minimal operational overhead. "

Source: AWS Certified Solutions Architect – Official Study Guide, Microservices and Messaging section.

AWS Lambda supports functions up to 10 GB of memory and 15 minutes execution time. Each invocation here requires only 1 GB of memory and finishes in 30 seconds, making it an ideal fit for Lambda. Lambda is cost-effective for event-driven, short-duration workloads and requires no infrastructure management. AWS Glue and EMR are better suited for large-scale ETL or distributed processing, which is unnecessary and more costly for this workload.

AWS Documentation Extract:

“AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume. Lambda supports up to 10 GB memory and 15 minutes per invocation.”

(Source: AWS Lambda documentation)

B, D: AWS Glue is intended for ETL on larger datasets or batch jobs, usually with higher operational overhead and cost.

C: EMR is for large-scale distributed processing and is not cost-effective for single, fast, memory-bound jobs.