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

By exporting AWS Cost and Usage Reports (CUR) to Amazon S3 and analyzing them with Amazon QuickSight, companies can generate interactive visual dashboards. This solution is fully AWS-managed, requires no third-party tools, and integrates deeply with AWS cost data.

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.

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.

Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.

Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.

Amazon GuardDuty detects cryptocurrency mining and sends findings to Amazon EventBridge. You can use EventBridge to trigger an automated Lambda function to isolate EC2 instances (such as by removing security group access or stopping/isolating the instance).

AWS Documentation Extract:

"Amazon GuardDuty findings can be sent to Amazon EventBridge, which enables you to trigger an automated response using AWS Lambda."

(Source: AWS GuardDuty documentation)

B, C, D: Security Hub, Inspector, and Config are not directly used for this detection-to-isolation workflow.

To optimize storage costs, migrating data from Amazon EFS to Amazon S3 with the Intelligent-Tiering storage class is a cost-effective solution.

Amazon S3 Intelligent-Tiering: This storage class automatically moves data between frequent, infrequent, and archive access tiers based on access patterns, reducing storage costs without impacting performance.

Cost Savings: By leveraging Intelligent-Tiering, the company can achieve significant cost savings, especially for data with unpredictable access patterns.

Application Update: The application must be updated to interact with Amazon S3 APIs for storing and retrieving files, ensuring seamless integration with the new storage solution.

This approach provides a scalable, durable, and cost-effective storage solution, aligning with the company's optimization goals.

Amazon S3 with Amazon CloudFront:

Amazon S3 provides highly scalable and durable storage for petabytes of data.

Amazon CloudFront, as a content delivery network (CDN), caches frequently accessed data at edge locations to reduce latency. This combination is ideal for storing and accessing engineering drawings.

Incorrect Options Analysis:

Option B: Amazon S3 Glacier Deep Archive is for long-term archival storage, not frequent access.

Option C: Amazon EBS is unsuitable for large-scale, multi-user data access and does not support caching directly.

Option D: AWS Storage Gateway is for hybrid cloud storage, which is unnecessary for a fully cloud-based architecture.

Lambda supports mounting an Amazon EFS file system inside your function to store larger dependencies beyond the 250 MB layer quota.

EFS automatically encrypts data in transit using TLS.

“You can configure your Lambda function to mount an Amazon EFS file system, enabling your function to access large amounts of data or large dependencies.”

“Amazon EFS automatically encrypts all data at rest and in transit.”

— Lambda with Amazon EFS

This is the least operational overhead approach.

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.

Using Amazon SQS decouples the API from the database, allowing the system to handle write bursts without data loss. The queue buffers incoming requests, and AWS Lambda processes them asynchronously, writing to the RDS instance at a sustainable rate.

From AWS Documentation:

“Amazon SQS acts as a buffer between components that produce and consume data, allowing each to operate independently. You can use AWS Lambda to process messages from SQS and write them to other services such as Amazon RDS.”

(Source: Amazon SQS Developer Guide)

Why C is correct:

SQS absorbs write surges and ensures durable message retention.

Lambda scales automatically with message volume and reduces DB connections.

Prevents timeout errors by smoothing traffic spikes.

Why others are incorrect:

A: Scaling RDS vertically doesn’t address connection spikes.

B: Multi-AZ is for availability, not write throughput.

D: SNS is for fan-out message delivery, not queue-based buffering.

Amazon S3 Intelligent-Tiering automatically moves objects between frequent and infrequent access tiers based on access patterns, which minimizes cost without performance impact. Storing photo metadata in Amazon DynamoDB provides fast and scalable lookup by user or tag.

From AWS Documentation:

“The S3 Intelligent-Tiering storage class automatically optimizes storage costs by moving data between frequent and infrequent access tiers when access patterns change.”

(Source: Amazon S3 User Guide – Intelligent-Tiering)

Why B is correct:

S3 Intelligent-Tiering optimizes storage automatically for cost without lifecycle management overhead.

DynamoDB stores small metadata items and S3 URLs, enabling efficient queries and photo lookups.

This architecture scales globally, integrates seamlessly with applications, and minimizes operational cost.

Why other options are incorrect:

A & D: DynamoDB is not intended for storing binary objects like images.

C: Lifecycle rules are static and don’t adapt dynamically to unpredictable access patterns.

The least operational overhead approach is to use AWS Directory Service with AWS Managed Microsoft AD and establish a trust relationship with the existing on-premises Microsoft Active Directory. This pattern lets employees continue using their existing corporate credentials while AWS operates the directory infrastructure (patching, monitoring, availability, and domain controller management), reducing the burden compared to building and maintaining custom federation components. With a trust in place, identities and group memberships from the on-premises directory can be used for authentication and authorization workflows in AWS.

To provide role-based access to AWS resources and the AWS Management Console, users should assume IAM roles that grant permissions based on job function. IAM roles with appropriate trust policies enable temporary credentials and least-privilege access without creating long-lived IAM users for every employee. This aligns with AWS best practices of centralized identity management and using roles for access control.

Option B is not a standard direct integration pattern: IAM does not “LDAP bind” directly to on-premises AD for console access. Option C can work (custom identity broker + STS) but increases operational overhead because the company must build, secure, operate, and maintain the broker, including availability, updates, and incident response. Option D is not the typical solution for employee console federation from AD; Amazon Cognito is primarily aimed at customer identity and application sign-in, not as the primary mechanism for workforce console access to AWS accounts.

Therefore, A provides the required federation with the smallest operational footprint while supporting role-based access control.

Protecting an application from layer 7 (application layer) DDoS attacks is best achieved by using AWS WAF (Web Application Firewall), which provides customizable protection against common web exploits including DDoS attacks at the application layer. AWS WAF supports managed rule groups maintained by AWS, which offer robust, tested protections against OWASP top 10 vulnerabilities and common attack patterns without requiring extensive manual rule creation.

While AWS Shield Standard provides basic network-layer DDoS protection automatically at no additional charge, it does not offer application-layer filtering capabilities. Therefore, option A alone is insufficient.

Option B, involving only custom rules, requires significant operational overhead and expertise, whereas AWS managed rules offer a turnkey solution with ongoing updates from AWS security teams.

Option D, using CloudFront in front of the ALB, can provide additional protection benefits such as caching and geographic restrictions, but the question specifically asks for protecting against layer 7 DDoS on the ALB directly. CloudFront plus WAF is a valid enhanced solution, but the direct and recommended answer in AWS official documents is to use AWS WAF managed rules directly with ALB for application-level protection.

AWS Lambda SnapStart is designed to improve the cold start performance of Java Lambda functions by initializing the function, taking a snapshot of the execution environment, and then reusing that snapshot for subsequent invocations. However, some code (such as code that generates unique IDs or session-specific data) should run during each invocation, not during the snapshot process. By using a pre-snapshot hook and moving the unique ID generation into the handler, you ensure that non-deterministic or per-invocation code is executed correctly, while the rest of the initialization benefits from SnapStart. This delivers the lowest latency and cost, as you do not need to pay for provisioned concurrency.

AWS Documentation Extract:

"Lambda SnapStart is ideal for Java functions with long cold starts due to heavy initialization. Move non-deterministic code such as unique ID generation to the handler, and use pre-snapshot hooks to customize what gets snapshotted. SnapStart works only for published versions, not $LATEST."

(Source: AWS Lambda documentation, Using SnapStart for Java Functions)

Other options:

A: SnapStart is not supported for the $LATEST version; must be a published version.

B & C: Provisioned concurrency removes cold starts but is more expensive than SnapStart for most workloads.

C: You must use SnapStart on published versions, but provisioned concurrency is not required for SnapStart and adds cost.

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policieshelp standardize tags across accounts, andSCPsenforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

If the SQS visibility timeout is set shorter than the time it takes for the application to process and delete the message, the message becomes visible to other consumers and can be processed again, resulting in duplicate processing. This is a common cause of duplicate messages when using SQS.

Reference Extract from AWS Documentation / Study Guide:

"If the visibility timeout for a message is set shorter than the time it takes to process the message, the message becomes visible again and can be received and processed again, resulting in duplicate processing."

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