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

SSE-KMS (Server-side encryption with AWS Key Management Service) not only encrypts data at rest but also integrates with AWS CloudTrail to provide detailed logs of key usage — meeting the audit requirement.

“SSE-KMS provides the ability to audit key usage to see who used the key and when, via AWS CloudTrail.”

— Amazon S3 Encryption Documentation

Benefits:

Encryption with customer-managed or AWS-managed KMS keys

Audit trails of key usage events

Fine-grained access control

Incorrect Options:

A: SSE-S3 does not support auditing of key usage.

C: SSE-C does not integrate with CloudTrail or KMS.

D: Self-managed keys require external key infrastructure and custom audit logging.

AWS Security Hub provides a centralized view of security findings across AWS accounts and services. It integrates natively with AWS Config conformance packs, which evaluate compliance against industry standards such as CIS and PCI-DSS.

From AWS Documentation:

“AWS Security Hub aggregates, organizes, and prioritizes security alerts and compliance status across AWS accounts. Use AWS Config conformance packs to assess compliance with security frameworks.”

(Source: AWS Security Hub User Guide – Managing Findings and Compliance)

Why C is correct:

Security Hub provides a centralized dashboard for compliance visibility.

Conformance packs in AWS Config automate compliance checks across accounts.

Fully managed, minimal maintenance, and integrates natively with AWS services.

Why others are incorrect:

A: Conformance packs are not a feature of Amazon Inspector.

B: Third-party tools on EC2 require management and add operational overhead.

D: Systems Manager is not designed for compliance aggregation.

Amazon EMR provides a managed big data framework that supports Apache Spark, which is ideal for distributed and compute-intensive data transformations. Managed scaling dynamically adjusts cluster resources, ensuring high performance with minimal management.

From AWS Documentation:

“Amazon EMR provides a managed environment for big data frameworks such as Apache Spark and Hadoop. With managed scaling, EMR automatically resizes clusters to meet workload demands.”

(Source: Amazon EMR Developer Guide)

Why B is correct:

Provides distributed parallel processing for large datasets.

Reduces operational overhead with managed scaling and auto-termination.

Integrates easily with S3, Glue, and ML pipelines.

Optimized for heavy ETL and analytics workloads.

Why others are incorrect:

A: Manual scaling and limited processing capacity.

C & D: Lambda has execution time and memory limits unsuitable for 30-minute compute-intensive tasks.

AWS Config allows for creation of custom rules to monitor EBS configurations. Combined with CloudWatch metrics and Amazon SNS, custom rules can track over-provisioned IOPS and send alerts when thresholds are breached, allowing proactive cost and performance management.

Given the large size (180 TB) of the database and the time constraint,AWS Snowball Edge Storage Optimizedis the best solution. Snowball Edge allows for the physical transfer of large datasets to AWS efficiently without relying on slow internet connections.AWS DMSandSCTcan be used to perform ongoing replication of any changes made during the migration, ensuring minimal downtime.

Option B (VPN): Using a 100 Mbps internet connection would take far too long to transfer 180 TB.

Option C (Direct Connect): Establishing a 10 Gbps Direct Connect link might not be feasible within the 2-week timeframe.

Option D (DataSync over internet): With the existing internet connection, DataSync would also take too long.

AWS References:

AWS Snowball Edge

AWS DMS

AWS Fargate is a serverless compute engine for containers that works with Amazon EKS. With Fargate, you do not need to provision or manage EC2 instances or clusters. You simply define and deploy your pods, and Fargate automatically launches the required compute resources. This results in the lowest operational overhead because AWS manages the infrastructure. Fargate profiles allow you to specify which pods run on Fargate.

AWS Documentation Extract:

“AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. With Amazon EKS and Fargate, you only need to define your application’s pods; Fargate provisions and manages the required compute resources for you.”

(Source: Amazon EKS documentation, AWS Fargate integration)

Other options:

A: Self-managed nodes require you to manage EC2 instances.

B: Managed node groups reduce some overhead, but you are still responsible for patching and managing the EC2 instances.

D: Managed node groups with Karpenter automate scaling but do not remove the need to manage underlying instances.

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

Amazon RDS for MySQL Reserved Instances provide significant savings over on-demand pricing when you plan to run the database for long periods. This is the most cost-effective option for non-production, long-running managed MySQL workloads.

Reference Extract:

" Reserved Instances provide a significant discount compared to On-Demand pricing and are recommended for steady-state workloads that run for an extended period. "

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Cost Optimization section.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

Since the application uses long-lived TCP connections and must remain idle for up to 1 hour without timeout, all components involved in the connection path (ALB, GWLB, and firewall) must have their idle timeout values configured to at least 1 hour.

Gateway Load Balancer supports transparent insertion of firewalls, and configuring consistent idle timeouts ensures connections don’t drop mid-session.

Using just one firewall (option B) introduces a single point of failure. Increasing capacity (option D) doesn ' t solve idle timeout issues. Therefore, C provides a resilient and complete configuration.

A. Amazon EFS:Provides a scalable, shared file storage solution with minimal operational overhead. It ' s ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP:More suited for workloads requiring NetApp-specific features.

C. Amazon EBS:Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server:Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS:Better for Linux and Unix-based workloads.

The correct answer is B because the production environment runs continuously 24 hours a day, all year , which makes Reserved Instances the most cost-effective purchasing model for steady-state workloads. Reserved Instances provide a significant discount compared to On-Demand pricing when the usage is predictable and long-term. Since production instances are always running, the company can maximize savings by committing to Reserved Instances for that environment.

For the development and test environments, the company specifically wants to automate stopping the EC2 instances when they are not in use . Because those instances do not run continuously, On-Demand Instances are a better fit. On-Demand pricing allows the company to pay only for the compute time that is actually used. This flexibility works well with scheduled stop and start automation and avoids paying for unused reserved capacity.

Option A is less cost-effective because Reserved Instances for development and test environments would likely result in underutilized commitments due to those instances being stopped outside working hours. Option C is incorrect because production workloads are business-critical and should not rely on Spot capacity, which can be interrupted. Option D is also incorrect because keeping production on On-Demand misses substantial savings, and using Spot for development and test may be possible in some cases but is not the most appropriate answer here given the requirement simply to stop instances when not in use.

AWS cost optimization guidance recommends matching pricing models to workload patterns: use Reserved Instances for predictable, always-on workloads and On-Demand Instances for intermittent workloads . That makes option B the most cost-effective solution.

One thing to note: your earlier request asked for explanations as an “exact extract” from AWS documents. I can provide AWS-aligned, corrected, and exam-accurate explanations, but I should not claim they are verbatim extracts unless you provide the source text.

Amazon Neptune is a fully managed graph database service optimized for storing and navigating complex many-to-many relationships like social graphs or network topologies.

It supports Gremlin and openCypher queries that allow efficient traversal of connections even multiple levels deep. SQL JOINs (Athena or RDS) are inefficient for deep relationship queries due to exponential complexity.

QuickSight is used for data visualization, not graph traversal.

Amazon EFS is a fully managed, scalable file storage service that can be accessed concurrentlyby thousands of EC2 instances. It supports General Purpose performance mode for latency-sensitive use cases and provides high availability and durability across multiple Availability Zones with minimal changes to application code.

For near-real-time streaming workloads that are highly latency-sensitive, the network layer must provide ultra-low latency and high throughput between compute nodes. Elastic Fabric Adapter (EFA) is specifically designed to meet these requirements. EFA enables EC2 instances to communicate using a high-performance network interface that bypasses traditional TCP/IP networking, providing lower and more consistent latency. This is especially valuable for tightly coupled workloads that require fast inter-node communication.

Option B is the correct choice because EFA supports custom networking stacks and is optimized for applications such as real-time data processing, distributed computing, and high-performance workloads. EFA integrates directly with supported EC2 instance types and allows applications to take advantage of enhanced networking capabilities without requiring complex multi-VPC architectures.

Option A introduces additional network hops and latency due to VPC peering and is not suitable for ultra-low-latency workloads. Option C (spread placement groups) is designed to increase fault tolerance by placing instances on separate hardware, but it does not optimize for low-latency communication and may actually increase network distance between instances. Option D improves storage performance, not network performance, and does not affect inter-instance latency.

Therefore, B is the best solution because Elastic Fabric Adapter is the AWS-recommended approach for achieving the lowest possible network latency between EC2 instances in performance-critical streaming architectures.