Free Practice Questions for Amazon Web Services SAA-C03 Exam

The issue described in the question, where incoming traffic seems to favor one EC2 instance, is often caused by session affinity (also known as sticky sessions) being enabled on the Application Load Balancer (ALB). When session affinity is enabled, the ALB routes requests from the same client to the same EC2 instance. This can cause an imbalance in traffic distribution, leading to performance bottlenecks on certain instances while others remain underutilized.

To resolve this issue, disabling session affinity ensures that the ALB distributes incoming traffic evenly across all EC2 instances, allowing better load distribution and reducing latency. The ALB will rely on its round-robin or least outstanding requests algorithm (depending on the configuration) to distribute traffic more evenly across instances.

Option B (Network Load Balancer): The NLB is designed for Layer 4 (TCP) traffic and low latency use cases, but it is not needed here as the problem is with load balancing logic at the application layer (Layer 7). The ALB is more appropriate for HTTP/HTTPS traffic.

Option C (Increase EC2 Instances): Adding more EC2 instances does not solve the root issue of uneven traffic distribution.

Option D (Health Check Frequency): Adjusting health check frequency won ' t address the imbalance caused by session affinity.

AWS References:

Application Load Balancer Sticky Sessions

The most secure way to grant an external vendor’s AWS-hosted automation tool access into a company AWS account is to use cross-account IAM role assumption. Option A follows the standard AWS pattern: the company creates an IAM role in its own account that has only the permissions the vendor needs, and the role’s trust policy allows the vendor’s IAM role (in the vendor’s AWS account) to assume it. This approach avoids creating long-lived credentials in the company account and supports least privilege, auditing, and easy revocation.

With role assumption, the vendor continues to authenticate in its own AWS account and uses AWS STS to obtain temporary credentials when accessing the company account. Temporary credentials reduce exposure compared to access keys because they expire automatically and can be constrained with session duration, external IDs (where appropriate), and conditions. The company retains full control over permissions through the role policy, and CloudTrail can record AssumeRole events and subsequent API activity for auditing.

Option B is less secure because it creates an IAM user in the company account and would typically require managing long-lived credentials for the vendor tool, increasing the risk of leakage and ongoing operational overhead. Option C is not valid because IAM users are not shared across accounts; you cannot “add” a user from another account into an IAM group in your account. Option D is incorrect because “AWS account” is not a typical IAM identity provider type for this purpose; cross-account access is done through role trust policies, not by creating an IdP with an AWS account ID and username.

Therefore, A is the most secure solution because it uses short-lived credentials, enforces least privilege, centralizes control in the company account, and provides strong auditability without issuing permanent access keys.

The performance issue occurs because reporting queries compete with production traffic on the same primary database instance. The best-practice AWS solution is to offload read-heavy workloads to a separate database endpoint.

Option C adds an Amazon RDS read replica, which asynchronously replicates data from the primary instance. By redirecting reporting queries to the replica endpoint, the primary database can focus on transactional workloads, significantly improving application performance and customer experience.

Read replicas are specifically designed for this use case: scaling read capacity and isolating reporting or analytics queries. This solution requires minimal changes to the reporting job (endpoint update only) and avoids overprovisioning the primary database.

Option A (RDS Proxy) improves connection management but does not reduce query load or isolate reporting traffic. Option B is invalid because Multi-AZ does not scale reads and is not configurable across three AZs for a single instance. Option D increases instance size but does not address the underlying contention between workloads and increases cost unnecessarily.

Therefore, C is the most efficient and scalable solution to minimize reporting impact while maintaining high performance and availability.

The correct answer isAbecause the legacy mainframe requires asynchronous RESTful APIand the new service takes approximately3 minutesto complete each request. Among the choices,Amazon API Gateway REST APIis the appropriate option for exposing a synchronous REST endpoint to the mainframe. API Gateway REST APIs support longer integration timeouts than HTTP APIs, which makes them more suitable for workloads that take an extended time to process. The REST API can invoke an AWS Lambda function to execute the stock price calculation and return the result synchronously.

Option B is incorrect becauseAPI Gateway HTTP APIshave a shorter integration timeout and are not the best fit for a request that takes around 3 minutes. Option C is incorrect becauseWebSocket APIsare not RESTful and do not meet the requirement for synchronous REST-based integration with the mainframe. Option D is incorrect becauseLambda function URLsdo not provide the same API management features and are not the best choice for a long-running synchronous enterprise integration requirement of this kind.

AWS design guidance favors API Gateway when exposing managed APIs to external consumers. In this case, theREST APIoption best matches the requirement for synchronous RESTful access while supporting the longer processing duration. It also provides a managed front door for authentication, throttling, and operational control if the company needs those features later.

One note of honesty: some exam versions treat the REST API timeout detail as the deciding factor here. That is the key reasonREST APIis preferred overHTTP APIfor this question.

For the relational database tier, Amazon RDS Multi-AZ provides high availability with minimal manual intervention. In Multi-AZ mode, RDS maintains a synchronous standby in a different Availability Zone and provides automatic failover during certain failure scenarios. This reduces operational burden because the service handles replication, health monitoring, and failover orchestration.

For the container-based application tier, Amazon ECS with the Fargate launch type minimizes operational overhead because it removes the need to provision, patch, and scale the underlying EC2 instances that host containers. With Fargate, AWS manages the compute infrastructure, and the team focuses on task definitions, scaling policies, and application configuration. This supports a highly available, load-balanced architecture because ECS services can run tasks across multiple Availability Zones behind an Application Load Balancer, and scaling is managed via ECS Service Auto Scaling.

Option B describes read replicas, which are primarily used for scaling reads and, depending on configuration, may not provide the same automated failover characteristics as Multi-AZ for high availability. Read replicas are not a direct substitute for Multi-AZ HA. Option C (self-managed Docker cluster on EC2) is operationally heavy: you must manage node capacity, patching, cluster lifecycle, and scheduling. Option E (ECS on EC2) is a valid container platform but still requires managing EC2 instances, AMIs, and scaling/patching, which increases manual intervention compared to Fargate.

Therefore, combining RDS Multi-AZ (A) for database resilience and ECS Fargate (D) for serverless container operations meets the HA requirement with the least manual effort.

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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To prevent loss of access to the AWS root user account due to a lost MFA device, AWS recommends enabling multiple MFA devices for the root user.

Multiple MFA Devices: AWS allows up to eight MFA devices (virtual, hardware, or security keys) to be associated with a single root user account. This redundancy ensures that if one device is lost, others can be used to authenticate and access the account.

Security Best Practices: Implementing multiple MFA devices enhances account security and provides resilience against potential access issues.

By proactively setting up multiple MFA devices, the company can maintain uninterrupted access to its critical AWS resources, even in the event of a lost or malfunctioning MFA device.

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn ' t traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it ' s over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn ' t meet the low-latency requirements of the use case.

The database workload has high variability and unpredictability, which makes fixed-capacity database solutions inefficient and costly. The company also requires MySQL compatibility without schema or application changes.

Amazon Aurora Serverless (MySQL-compatible) is specifically designed for this scenario. Option C automatically scales database capacity up or down based on workload demand, making it highly cost-effective for sporadic usage patterns. During peak periods, Aurora Serverless scales seamlessly to handle increased load, and during idle periods, capacity scales down, significantly reducing cost.

Option A (DynamoDB) is not suitable because it is a NoSQL service and would require significant application and schema changes. Option B (RDS for MySQL) uses fixed instance sizes and would require overprovisioning to handle peak loads, increasing cost. Option D introduces high operational overhead and does not provide automatic database scaling.

Therefore, C best meets the requirements by providing MySQL compatibility, serverless scaling, and cost optimization for variable workloads.

Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

" S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements. "

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

Because the application is public and serves global customers with dynamic IP addresses, the web tier must allow HTTPS traffic from0.0.0.0/0on port 443. For the database tier, the secure design is to allow MySQL access only from theweb tier’s security group, not from the internet. AWS RDS documentation explains that you can specify another VPC security group as the source for the DB security group, which allows application servers to connect without exposing the database publicly. That makes A the correct configuration. Options B and C do not work well for global dynamic users, and D exposes the database to the internet.

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Compute Savings Plansoffer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, usingOn-Demand Instancesensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS ' s cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

AWS Lake Formation provides fine-grained (column-level) access control for data stored in S3. Using a Lake Formation blueprint ensures database ingestion is automated and governed.

QuickSight can query Athena, and Athena honors Lake Formation permissions, enforcing column-level controls automatically.

Options A, B, and C rely on manual filtering or IAM policies, which cannot enforce column-level authorization for SQL queries.

Amazon Athenais a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handleApache Parquetfiles efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.

Key AWS features:

Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost-effective solution compared to Redshift or EMR for occasional queries.

Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.

AWS Documentation: Athena ' s compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity​​.

Comprehensive and Detailed Step-by-Step Explanation:

To meet the requirements of copying only relevant data as soon as possible and receiving notifications upon completion, the following steps are recommended:

Generate a Manifest File (Option A):

Action:Modify the on-premises data generation process to create a manifest file at the end of each data generation cycle. This manifest should list the names of the objects that need to be copied to Amazon S3.

Implementation:Develop a custom script that runs after data generation. This script compiles the list of relevant data files into a manifest file and uploads it to a designated S3 bucket.

Justification:Using a manifest allows AWS DataSync to transfer only the specified files, reducing unnecessary data transfer and associated costs.

docs.aws.amazon.com

Automate DataSync Task Execution (Option D):

Action:Set up an S3 Event Notification to trigger an AWS Lambda function whenever a new manifest file is uploaded to the S3 bucket.

Implementation:Configure the Lambda function to invoke the DataSync task by calling the StartTaskExecution API action, specifying the manifest file. This ensures that only the files listed in the manifest are copied from on-premises storage to Amazon S3.

Justification:This automation ensures timely data transfer as soon as relevant data is available, minimizing delays and manual intervention.

docs.aws.amazon.com

Set Up Completion Notifications (Option E):

Action:Create an Amazon SNS topic to handle notifications. Then, establish an Amazon EventBridge rule that monitors the DataSync task execution status and sends an email notification to the SNS topic when the status changes to SUCCESS or ERROR.

Implementation:Configure EventBridge to capture state changes of the DataSync task. When a task completes successfully or encounters an error, EventBridge triggers a notification to the SNS topic, which then sends an email to the subscribed recipients.

Justification:This setup provides immediate feedback on the data transfer process, allowing the analytics team to act promptly based on the success or failure of the data copy operation.

docs.aws.amazon.com

The correct solution isMulti-AZ deploymentbecause this is the Amazon RDS feature designed for high availability and fast failover. AWS prescriptive guidance shows Multi-AZ deployment as an in-Region DR option with an approximateRTO of 1–2 minutes, which is comfortably within the less-than-5-minute requirement. Read replicas are mainly for scaling reads, while snapshots and CloudFormation are not fast failover mechanisms. For a critical database workload that must remain highly available with minimal recovery time, Multi-AZ is the standard AWS-managed answer.

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Amazon S3 is the strongest storage choice because it is designed for highly durable object storage, and CloudFront can use an S3 bucket as its origin to deliver content globally. AWS Backup documentation also states that for Amazon S3 you can createcontinuous backupsand restore data to a point in time, which reduces the risk of data loss more effectively than simple daily backups. The EC2-based options introduce more infrastructure management and weaker durability characteristics than S3 for this content-storage pattern. Lambda ephemeral storage is temporary and is not intended as a durable content repository. For globally distributed campaign assets that must stay highly available and protected with the strongest managed backup posture among the options,S3 + CloudFront + AWS Backup continuous backupsis the best design. (AWS Documentation)

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Amazon FSx for Lustre is a high-performance parallel file system designed for machine learning and HPC that can be linked to Amazon S3 via Data Repository Associations (DRA). With DRA, you can import S3 objects as files (lazy or preloaded) and export results back to S3, providing very high throughput and low-latency POSIX access on the training cluster. This S3-native integration minimizes data loading overhead and accelerates training cycles at scale. EFS (A) is general-purpose and lower throughput per TB than Lustre. EBS (C) requires manual copy and does not scale as a shared parallel filesystem. DataSync alone (D) is a transfer tool, not a high-throughput training filesystem. FSx for Lustre with S3 DRA best satisfies scalability, throughput, and native S3 integration for rapid ML training.

AWS Step Functions is designed to coordinate multiple AWS services into serverless workflows, handling errors, retries, and complex logic. By configuring Amazon S3 Event Notifications to trigger an Amazon EventBridge rule, you can automatically start a Step Functions workflow when a new object is uploaded to S3. Step Functions can manage retries for failed uploads and orchestrate calls to various AWS services with minimal operational effort.

AWS Documentation Extract:

“You can use Amazon EventBridge to initiate AWS Step Functions workflows in response to events from S3 buckets. Step Functions can orchestrate the sequence of AWS service calls and automatically handle retries and errors, making it ideal for automating and managing complex workflows.”

(Source: AWS Step Functions documentation, Using Step Functions with EventBridge and S3)

Other options:

A: Requires manual implementation of orchestration and error handling.

B & C: Involve additional infrastructure management and custom logic for workflow orchestration and error handling, increasing operational effort.