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

AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution.

From AWS Documentation:

“AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status.”

(Source: AWS Backup Audit Manager User Guide)

Why D is correct:

Ensures centralized visibility into all backup jobs.

Verifies encryption status automatically.

Generates ready-to-use reports with minimal operational overhead.

Complies with regulatory requirements for data protection.

Why others are incorrect:

A & C: Custom Lambda automation increases maintenance effort.

B: Manual checking is operationally inefficient and error-prone.

AWS Lambda is not suitable for long-running jobs that can take up to 20 minutes, as Lambda has a maximum execution duration of 15 minutes.

Amazon ECS with AWS Fargate allows you to run containers without managing EC2 instances, providing a scalable and highly available environment. You can scale Fargate tasks to handle large and parallel video processing jobs. Amazon Aurora is a highly available, managed relational database. S3 Intelligent-Tiering is cost-effective for storing large video files with variable access patterns.

AWS Documentation Extract:

" AWS Fargate lets you run containers without managing servers or clusters, providing a highly available and scalable compute environment. Fargate is suitable for running data processing workloads that require long-running compute tasks. "

(Source: AWS Fargate documentation, Use Cases)

A: Lambda is not suitable for long-running (over 15 min) or heavy compute jobs.

C: Amazon EMR is optimized for big data analytics, not specific video processing. FSx for Lustre and Kinesis are not best fit for this use case.

D: EKS with EC2 adds operational overhead and RDS in a single AZ is not highly available; Glacier Deep Archive is not suitable for frequently accessed video files.

This solution provides encryption at rest and in transit with the least operational overhead while adhering to the company’s security policies.

Encryption at Rest: Amazon RDS for MySQL can be configured to encrypt data at rest by using AWS Key Management Service (KMS) managed keys. This encryption is applied automatically to all data stored on disk, including backups, read replicas, and snapshots. This solution requires minimal operational overhead because AWS manages the encryption and key management process.

Encryption in Transit: AWS Certificate Manager (ACM) allows you to provision, manage, and deploy SSL/TLS certificates seamlessly. These certificates can be used to encrypt data in transit by configuring the MySQL instance to use SSL/TLS for connections. This setup ensures thatdata is encrypted between the application and the database, protecting it from interception during transmission.

Why Not Other Options?:

Option B (IPsec tunnels): While IPsec tunnels encrypt data in transit, they are more complex to manage and require additional configuration and maintenance, leading to higher operational overhead.

Option C (Third-party application-level encryption): Implementing application-level encryption adds complexity, requires code changes, and increases operational overhead.

Option D (VPN for encryption): A VPN solution for encrypting data in transit is unnecessary and adds additional complexity without providing any benefit over SSL/TLS, which is simpler to implement and manage.

AWS References:

Amazon RDS Encryption- Information on how to configure and use encryption for Amazon RDS.

AWS Certificate Manager (ACM)- Details on using ACM to manage SSL/TLS certificates for securing data in transit.

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3,Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.

Why Other Options Are Incorrect:

Option B:EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.

Option C:While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.

Option D:Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.

Key AWS Features Used:

AWS Lambda:Real-time serverless processing with integration capabilities for Aurora and S3.

Amazon S3:Cost-effective storage for enriched data.

Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

AWS Documentation References:

AWS Lambda Function Overview

Amazon Redshift Spectrum

Processing Streaming Data with Kinesis Data Streams

The most resilient and scalable architecture for handling millions of high-resolution images with frequent updates is to store the binary images in Amazon S3 and store their metadata or reference (geographic code and S3 URL) in Amazon DynamoDB.

From AWS Documentation:

“Store large objects like images in Amazon S3 and use Amazon DynamoDB to store metadata and references. This design pattern is scalable, highly available, and cost-effective.”

(Source: AWS Architecture Blog – Best Practices for Handling Large Objects)

Why B is correct:

Amazon S3 is designed for storing large volumes of binary data (images).

DynamoDB provides low-latency reads/writes using the geographic code as the partition key.

Highly available, serverless, and auto-scaling, suitable for disaster scenarios with bursts of activity.

Reduces pressure on the database layer by separating metadata from image storage.

Why the others are incorrect:

Option A and D: Storing images directly in RDS is expensive, unscalable, and not optimal for binary storage.

Option C: DynamoDB is suitable, but storing actual binary image data in DynamoDB is not best practice due to item size limits (400 KB) and performance concerns.

For applications requiring high network throughput and low latency between EC2 instances, AWS recommends using a Cluster Placement Group:

Cluster Placement Group: Packs instances close together inside an Availability Zone, enabling workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication.

Enhanced Networking: Selecting instance types that support enhanced networking provides higher bandwidth, higher packet-per-second (PPS) performance, and consistently lower inter-instance latencies.

By combining a Cluster Placement Group with enhanced networking-enabled instance types, the company can meet the stringent performance requirements of its real-time industrial application.

Amazon SQS provides durable, decoupled message storage for distributed systems. Using SQS as a job queue enables each EC2 instance to process a message independently. Scaling the Auto Scaling group based on the SQS queue length ensures parallelism and elasticity, aligning compute resources with workload volume.

The correct answer isCbecause storing large binary objects such as documents directly in a relational database increases database size, raises storage cost, and can degrade performance over time.Amazon S3is a more appropriate service for storing large unstructured objects like documents, images, and media files. The application can store the actual document files in S3 and keep only the related metadata, object keys, and references in the existing Oracle database.

This design is more cost-effective because S3 storage is generally less expensive than expanding high-performance relational database storage for BLOB content. It also improves database performance because the database no longer needs to manage the overhead of storing and retrieving large document payloads. The database can focus on transactional and relational data, which is the workload it is designed to handle best.

S3 also provides high durability, scalability, and resilience. These characteristics satisfy the requirement for a highly available and resilient solution while avoiding unnecessary expansion of the RDS storage layer. Keeping metadata in the existing database minimizes the application changes compared with a full database migration.

Option A is incorrect because Magnetic storage would significantly reduce performance and is not appropriate for this workload. Option B would improve performance, but it would be much more expensive and does not address the root cause of storing large BLOBs in a relational database. Option D is incorrect because moving the relational Oracle workload to DynamoDB would require a major redesign and is not the most cost-effective solution for the stated problem.

The best architectural pattern is to useAmazon S3 for document storageand the relational database for metadata and transactional records.

The optimal solution for low-latency global caching with disaster recovery and cross-Region replication is to use Amazon ElastiCache Global Datastore for Redis OSS.

A Global Datastore enables fully managed cross-Region replication and supports automatic failover by promoting read replica clusters in another Region.

ElastiCache ensures encryption in-transit and at-rest, meeting compliance and security requirements.

It ' s a fully managed AWS-native feature, reducing operational effort compared to setting up DMS-based or snapshot-based replication manually.

Other options (A, C, D):

Require manual setup and management (e.g., custom DMS pipelines, snapshots).

Do not offer real-time replication or failover without manual intervention.

???? Reference:

ElastiCache Global Datastore for Redis

Amazon RDS Proxy helps manage and pool database connections from serverless compute like AWS Lambda, significantly reducing the stress on the database during unpredictable traffic surges. It improves scalability and resiliency by efficiently managing connections, protecting the database from being overwhelmed, and enabling failover handling.

Option A is the most cost-effective and operationally efficient approach to handling unpredictable surges and improving resilience without requiring major application changes.

Option B involves a migration to DynamoDB, which is a significant architectural change and costlier initially. Option C is manual connection cleanup, insufficient for unpredictable surges. Option D increases resources but does not solve connection storm problems efficiently and is more costly.

Amazon SQS is the best messaging layer here because it providesdurable bufferingand decouples microservices so they can scale independently while absorbing bursts without losing data. AWS recommends SQS for decoupled architectures where producers and consumers operate at different speeds. For compute,AWS Fargatereduces operational overhead because AWS manages the underlying servers, patching, and capacity layer. API Gateway and WebSockets are not the primary answer for internal asynchronous microservice communication, and SNS is fanout-oriented rather than the most natural queueing model for durable work processing between services. Therefore, SQS plus ECS on Fargate is the most appropriate low-overhead design.

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The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays.Option D, using anAmazon EC2 Auto Scaling target tracking policybased on theApproximateNumberOfMessagesin the SQS queue, is the best solution.

Auto Scaling with Target Tracking:

Target tracking policiesdynamically scale out or in based on a specific metric. For this use case, you can monitor theApproximateNumberOfMessagesin the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn’t build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiencesunpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn’t resolve the issue of the processing tier’s overloaded EC2 instances.

AWS References:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

Aurora global database is specifically designed for cross-Region disaster recovery and low-latency global reads.

It provides:

Physical storage-level replication between Regions, typically with lag of under 1 second, easily satisfying RPO 1 minute.

Fast cross-Region failover capabilities, often within minutes or less, which meets the RTO 5 minutes requirement.

You configure the primary Region as the writer cluster and additional Regions (such as us-west-1) as secondary clusters with reader endpoints. In a disaster, you promote the secondary to be the new writer.

Why others are not correct:

A: Standard cross-Region read replicas for Aurora PostgreSQL do not provide automatic multi-Region failover with the same guarantees as Aurora global database and may not meet strict RTO/RPO requirements.

C: Logical replication and custom EventBridge failover is complex, error-prone, and higher overhead compared to the managed global database feature.

D: Restoring from snapshots plus cross-Region replication is too slow to meet RTO 5 minutes and certainly cannot maintain RPO 1 minute.

AWS Budgetsis the native service for creating budget thresholds and sending notifications whenactualorforecastedcosts exceed those thresholds. AWS documentation states that Budgets can send alerts toAmazon SNSand supports both actual and forecasted alerts without requiring custom code. Cost Explorer is still useful for analyzing and monitoring cost trends, but Budgets is the correct alerting tool. The other options introduce unnecessary Lambda logic or misuse other services. Since the question specifically says the company does not want to build a custom solution, a budget with SNS alerts is the most direct and least operationally heavy answer.

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AWS Key Management Service (AWS KMS) supports multi-Region keys, which are managed customer master keys that can be replicated to multiple Regions and treated as a single logical key. This allows applications in different Regions to encrypt and decrypt data using equivalent keys while AWS handles key replication, durability, and availability.

KMS is a fully managed key service, so the company does not need to operate hardware or manage low-level key storage. Tags combined with attribute-based access control (ABAC) let you enforce fine-grained access to keys by using IAM policies and condition keys, giving centralized and flexible access control with low operational overhead.

CloudHSM (Options C and D) requires managing HSM clusters, scaling, backups, and manual key operations, which significantly increases operational burden. Importing key material in KMS (Option B) adds lifecycle complexity and is unnecessary when native multi-Region keys exist.

Kinesis Data Streams is the right ingestion service for real-time streaming data, and AWS Lambda is the least-operations compute option for processing and transforming records from the stream. AWS documents that Lambda can process events from stream sources such as Kinesis without infrastructure management. Storing the transformed data in DynamoDB provides durable, scalable storage for later analysis. The EC2 option introduces server management overhead. SQS is useful for queued work, but the question specifically centers on streaming data. SNS with ElastiCache is also not a durable analysis store design. Therefore, Kinesis Data Streams plus Lambda plus DynamoDB is the lowest-overhead architecture among the choices.

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AWS Backup is a fully managed backup service that can create backup plans for EC2 instances, including both instance configurations and attached EBS volumes, with scheduled and cross-Region copy capabilities. By adding the EC2 instances to the resource assignment in the backup plan, AWS Backup automatically backs up all configurations and attached EBS volumes, and can copy backups to a secondary Region for disaster recovery, providing the highest operational efficiency with the least manual effort.

AWS Documentation Extract:

“AWS Backup provides fully managed backup for EC2 instances and attached EBS volumes, with scheduling, retention, and cross-Region copy built in. By adding the EC2 instance as a resource, the backup includes both configuration and attached volumes.”

(Source: AWS Backup documentation)

A, D: Custom Lambda scripts increase operational overhead and are not as integrated or robust as AWS Backup.

C: Assigning only EBS volumes does not include the EC2 instance configuration, which is needed for full recovery.

Comprehensive and Detailed Explanation (from AWS Solutions Architect documentation):

Amazon DynamoDB is a fully managed NoSQL database engineered for extremely high throughput and millisecond-level response times at any scale. It can automatically scale to support millions of requests per second and requires no management of infrastructure, storage provisioning, or server maintenance.

AWS documentation specifically highlights DynamoDB as the optimal solution for ecommerce workloads that require consistent low-latency performance, massive scalability, and near-zero operational overhead.

Aurora and RDS cannot support millions of requests per second, and Redshift is a data warehouse not intended for transactional workloads.

Amazon OpenSearch Service is the AWS-native service for real-time search, analytics, and visualization. Ingesting streaming data with Amazon Kinesis Data Streams ensures scalable and reliable data ingestion. OpenSearch supports indexing and querying the data in near real time. QuickSight can then be integrated for visualization and reporting. Options A and B involve batch processing and are not optimized for real-time search. Option C (EKS + DynamoDB) is not a fit for search workloads, as DynamoDB does not support advanced text search. Option D provides the closest AWS-native equivalent to the company’s existing search-and-visualization architecture.

Amazon EFS is a regional, elastic file system that “scales to petabytes” and can be accessed from “thousands of compute instances” concurrently. You can mount EFS across VPCs and across AWS accounts by providing network connectivity (e.g., VPC peering) to the EFS mount targets and allowing NFS (TCP 2049) in the mount target security group, optionally using EFS access points and a file system policy for cross-account access control. VPC peering has no hourly charge and minimal operational overhead, and EFS automatically scales as files are added—meeting the scalability and cost goals.

Option A duplicates data, incurs DataSync and extra storage costs, and adds sync lag.

Option C adds an extra Lambda hop and complexity without exposing a shared filesystem to the primary function.

Option D is impractical: Lambda layers are immutable artifacts with tight size limits (up to 50 MB compressed/250 MB uncompressed) and are not suited for dynamic, growing file sets.