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

EC2 Image Builder is the AWS-managed service specifically designed to automate the creation, patching, hardening, and testing of AMIs.

For this scenario, best practice is to:

Use EC2 Image Builder pipelines to generate new golden AMIs whenever features are deployed or on a schedule.

Include build components such as update-linux to automatically apply OS security patches and updates during image creation.

Deploy the new AMIs through existing Auto Scaling groups, ensuring that every newly launched instance is already patched and compliant.

This approach:

Prevents OS vulnerabilities from being introduced at deployment time.

Scales across dozens of applications because AMI pipelines are reusable and automated.

Minimizes manual effort and reduces drift between instances.

Amazon Inspector (Option A) identifies vulnerabilities but does not itself patch AMIs.

AWS Backup (Option B) is for data protection, not vulnerability management.

Patch Manager (Option C) patches running instances, but the requirement is to ensure vulnerabilities are not introduced with new AMIs; golden image pipelines with EC2 Image Builder are the recommended solution.

AWS Certificate Manager (ACM) issues and automatically renews public TLS certificates used by Amazon CloudFront. With DNS validation, ACM creates CNAME records that prove domain control; once validated, renewals occur automatically without manual approval, providing the lowest operational effort. For CloudFront, ACM certificates must be in the US East (N. Virginia) Region. CloudFront security policies (A) configure protocol/cipher requirements, not certificate issuance. OAC (B) secures origin access and is unrelated to certificate management. Email validation (D) requires mailbox approvals and operational handling at issuance/renewal, which is less efficient than DNS validation. Thus, ACM with DNS validation delivers automated creation and renewal with minimal overhead.

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 company is already running Kubernetes, soAmazon EKSminimizes application-platform changes while moving to AWS. The application also usesAMQP, and AWS documentation states thatAmazon MQ for RabbitMQsupports AMQP 0-9-1, which preserves the messaging model better than moving to SQS. ECS and Lambda would require a bigger platform or protocol change. Running directly on EC2 adds more operational burden than using managed Kubernetes. Therefore, EKS plus Amazon MQ is the least-overhead migration path that preserves the application’s container and messaging patterns. (docs.aws.amazon.com)

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For an application requiring TCP communication and high availability:

Network Load Balancer (NLB)is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling groupensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

Amazon Kinesis Data Firehose (now known as Amazon Data Firehose) supports near-real-time streaming, with built-in support to:

Invoke AWS Lambda functions for transformation.

Store data directly into Amazon S3 in desired formats like Apache Parquet.

“You can use Data Firehose to transform the data before delivering it, by invoking a Lambda function. You can convert to formats such as JSON or Apache Parquet before storing it into S3.”

— Amazon Data Firehose Developer Guide

Why not the others?

B: Kinesis Data Streams requires more operational overhead than Firehose.

C: DataSync is for file movement—not stream processing.

D: SQS isn’t designed for streaming and transformation pipelines.

Amazon API Gatewayprovides a scalable and reusable solution for interacting with DynamoDB without requiring direct access by developers. By setting up a REST API with a POST methodthat integrates with DynamoDB ' sPutItemaction, developers can submit data (such as user ratings) to the DynamoDB table through API Gateway, without having to directly interact with the database. This solution is serverless and minimizes operational overhead.

Option A: Using ALB with Lambda adds complexity and is less efficient for this use case.

Option B: While using Lambda is possible, API Gateway provides a more scalable, reusable interface.

Option C: SQS with Lambda introduces unnecessary components for a simple put operation.

AWS References:

Amazon API Gateway with DynamoDB

Amazon EventBridge supports routing application-generated events to AWS Lambda targets. The Lambda function can process and insert events into a DynamoDB table. This is a standard design pattern for connecting event-driven applications with DynamoDB. Option A confuses DynamoDB Streams (which streams changes from DynamoDB) with EventBridge (which is for event routing). Options C and D reference partner event buses, which are intended for SaaS integrations, not custom application events. Therefore, the correct and simplest solution is to use a custom EventBridge event bus with a Lambda target (B).

To break down consolidated billing costs by “product line” using tags, the company must use cost allocation tags and ensure those tags are activated for billing at the payer/management level. Because the teams have already applied user-defined tags to the compute resources across accounts, the correct approach is to (1) use those user-defined tags in the billing tools and (2) activate them so they appear in cost and usage reporting for consolidated billing.

B is required because cost reporting by product line depends on selecting the user-defined cost allocation tag key (for example, ProductLine=...) in AWS billing and cost management tooling (such as Cost Explorer reports). AWS-generated tags are not what teams typically use for chargeback by business dimension, and the scenario explicitly says teams tagged resources (i.e., user-defined).

E is required because in an AWS Organizations consolidated billing setup, cost allocation tag activation is handled centrally from the Organizations management account so that the tags can be used consistently in consolidated cost views and reporting. Activating tags centrally ensures the billing system recognizes and includes those tag dimensions in cost allocation data.

Option C is not relevant to billing breakdown; Resource Groups is for organizing and operating on resources, not for consolidated billing allocation. Option D is unnecessary in this scenario’s intended best practice because activation for consolidated billing reporting is done from the management account to standardize reporting. In short, you tag resources (already done), then activate the tag for billing and use it in billing reports to see cost by product line across accounts and Regions.

Therefore, B and E together meet the requirement with the correct billing-focused steps.

Amazon SQS with a 2-day retention ensures the data lives just as long as needed. EventBridge Pipes allow direct integration between event producers and consumers, with optional filtering and transformation. AWS Step Functions supports manual approval steps, which fits the workflow requirement perfectly.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirements are straightforward: expose the Lambda microservice through an HTTPS endpoint and authenticate calls using IAM. Lambda function URLs are a built-in feature that provides a dedicated HTTPS endpoint for a Lambda function without requiring API Gateway, ALB, or CloudFront. When configured with the authentication type AWS_IAM, the endpoint requires requests to be signed with AWS Signature Version 4 and authorized by IAM policies. This directly satisfies the “must use IAM to authenticate calls” requirement with the least architectural complexity.

Option A can also secure an endpoint with IAM, but it proposes using a Lambda authorizer, which is typically used for custom authorizers (JWT/OAuth/Cognito/external identity). For IAM authentication in API Gateway, you generally use IAM authorization on the method, not an authorizer function. Also, API Gateway REST APIs introduce additional service configuration and per-request costs when a simpler managed option exists that meets the requirements.

Options C and D are not appropriate. Lambda@Edge and CloudFront Functions run at CloudFront edge locations with different programming and deployment models; they are designed for CDN request/response manipulation, not as the primary mechanism to expose a regional Lambda microservice endpoint with IAM authentication. CloudFront Functions in particular is for lightweight JavaScript at the edge and does not provide a native “AWS_IAM authentication type” for invoking an origin Lambda as a microservice endpoint.

Therefore, B is the cleanest and most secure fit: a native HTTPS endpoint backed by Lambda, protected with IAM-based SigV4 authentication.

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

The best multi-Region DR design isAurora global databasefor the database layer andS3 Cross-Region Replicationfor object storage. AWS documentation states that Aurora global database automatically synchronizes changes from a primary cluster to secondary clusters in other AWS Regions, providing multi-Region resiliency and fast recovery from Region-level outages. AWS also documents thatS3 CRRcopies objects to a destination bucket in another AWS Region, which is the standard way to protect S3 data across Regions. Same-Region options do not satisfy the multi-Region requirement, and the backup-based alternatives are less aligned with minimizing potential data loss than continuous cross-Region replication. Therefore,Aurora global database plus S3 CRRis the correct answer. (AWS Documentation)

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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.

Creating a listener rule on theApplication Load Balancer (ALB)to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

An Amazon S3 gateway VPC endpoint provides private connectivity between resources in a VPC and Amazon S3. Traffic is routed through the AWS network by using entries that the endpoint adds to selected route tables, so the EC2 instances do not require public IP addresses, an internet gateway, or a NAT gateway to reach S3. The S3 bucket policy can restrict access by using the endpoint identifier, ensuring that requests are accepted only when they originate through the approved VPC endpoint. An instance profile controls authorization but does not determine whether network traffic uses public addressing. A NAT gateway provides outbound connectivity through a public IP address and therefore does not satisfy the requirement. Long-term IAM user access keys on EC2 instances are also contrary to AWS security best practices.

Requirement Analysis: Need secure, direct connectivity from an on-premises client to an RDS cluster, accessible only when in the office.

VPC with Private Subnets: Ensures the RDS cluster is not publicly accessible, enhancing security.

Site-to-Site VPN: Provides secure, encrypted connection between on-premises office and AWS VPC.

Implementation:

Create a VPC with two private subnets.

Launch the RDS cluster in the private subnets.

Set up a Site-to-Site VPN connection with a customer gateway in the office.

Conclusion: This setup ensures secure and direct connectivity with minimal exposure, meeting the requirement for secure access from the office.

References

AWS Site-to-Site VPN:AWS Site-to-Site VPN Documentation

Amazon RDS:Amazon RDS Documentation

A. VPC peering:Creates a fully meshed architecture, which is complex to manage for multiple VPCs.

B. Transit gateway:Simplifies network management by connecting multiple VPCs and on-premises networks via a central hub.

C. PrivateLink:Restricts communication to the application endpoint but may not allow full VPC connectivity.

D. ALB with internet exposure:Not secure or specific to private network communication.

Amazon S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves data between frequent and infrequent access tiers based on usage. This tier offers immediate access to all objects, regardless of which tier they are stored in, while optimizing storage costs. S3 Intelligent-Tiering also provides the same high durability, availability, and security as other S3 storage classes and supports access from external resources using standard S3 APIs. Lifecycle policies and Glacier classes are more suitable for archival when infrequent access is predictable, but retrieval from Glacier classes is not immediate and incurs extra charges and delays.

Reference Extract from AWS Documentation / Study Guide:

" S3 Intelligent-Tiering is designed to optimize costs by automatically moving data between two access tiers when access patterns change. Data is always available and immediately accessible, making it ideal for unknown or unpredictable access patterns. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Storage Classes section.