SAA-C03 Amazon Web Services AWS Certified Solutions Architect - Associate (SAA-C03) Free Practice Exam Questions (2026 Updated)

The correct answer isAbecause the application is alightweight data processing toolthat always completes within3 minutes, which fits well within the execution model ofAWS Lambda. Lambda is a fully managed compute service that eliminates the need to provision or manage servers, clusters, operating systems, or container hosts. Since the company is willing to make any required changes to the image, packaging the workload as aLambda container imageis the option with the least operational overhead.

Lambda is well suited for short-lived, event-driven, or on-demand processing jobs. It automatically scales, charges only for execution time, and removes nearly all infrastructure administration. Compared with container orchestration services, Lambda requires less setup, less monitoring of infrastructure, and no task or cluster capacity planning.

Option B is incorrect becauseAmazon EKS with Fargatestill requires Kubernetes management constructs and introduces more operational complexity than Lambda. Option C is simpler than EKS, butAmazon ECS with Fargatestill involves managing task definitions, services, and networking, which is more overhead than Lambda for a brief processing job. Option D is incorrect because using a custom AMI with AWS Batch introduces more compute management and is unnecessary for a very short, lightweight workload.

AWS guidance favorsserverless serviceswhen the workload is short-lived and infrastructure management should be minimized. Because this job always completes quickly and the company wants the least operational overhead,AWS Lambda with a container imageis the best solution.

The job runs for about 1 hour and must not be interrupted, so Lambda is not suitable because AWS Lambda has a maximum execution time of 15 minutes. Fargate Spot is also not appropriate because the workload explicitly cannot tolerate interruption. The most cost-effective remaining design is to run the Python script on an EC2 On-Demand instance only during the nightly processing window and automate instance start/stop around the schedule. That avoids paying for compute all day while preserving uninterrupted execution during the job. The Step Functions Express option is not the natural fit for a single long-running Python job of this type. Therefore, an EC2 instance scheduled to run only when needed is the most economical answer among the choices.

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The most secure solution for allowing EC2 instances to access an S3 bucket is by usingIAM roles. An IAM role can be created with an access policy that grants the required permissions (e.g., to read and write to the S3 bucket). The IAM role is then associated with the EC2 instances through anIAM instance profile.

By associating the role with the instances, the EC2 instances can securely assume the role and receive temporary credentials via the instance metadata service. This avoids the need to store credentials (such as access keys) on the instances or within the application, enhancing security and reducing the risk of credentials being exposed.

AWS CloudFormation can be used to automate the creation of the entire infrastructure, including EC2 instances, IAM roles, and associated policies.

AWS References:

IAM Roles for EC2 Instancesoutlines the use of IAM roles for secure access to AWS services.

AWS CloudFormation User Guidedetails how to create and manage resources using CloudFormation templates.

Why the other options are incorrect:

A. Save IAM access key in UserData: This is insecure because it involves storing long-term credentials in the instance user data, which can be exposed.

B. Store access keys in S3: This is also insecure, as it involves managing and distributing long-term credentials, which should be avoided.

D. Retrieve access keys via a script: This approach is unnecessarily complex and less secure than using IAM roles, which provide temporary credentials automatically.

The best combination isAmazon Athenafor SQL-based analysis andAmazon QuickSightfor visualization. AWS documentation includes Athena guidance for creating tables over sampleCloudFront log datastored in Amazon S3, which makes Athena a direct fit for querying those logs without infrastructure management. AWS also describes QuickSight as a scalable business intelligence service for building dashboards and visualizations from query results. Glue is primarily for data cataloging and ETL, not end-user visualization. DynamoDB is not a SQL analytics engine for S3 log files. For advanced analysis of CloudFront logs and dashboard-style visualizations, Athena plus QuickSight is the correct AWS-native pattern. (AWS Documentation)

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Amazon FSx for Lustre is the correct storage service for high-throughput training workloads that must feed multiple GPU instances quickly. SageMaker can use FSx for Lustre as a training data source to reduce data-loading time and improve training throughput. FSx for Lustre also integrates with Amazon S3 through data repository associations, allowing the large image dataset to be linked from S3 and presented through a high-performance Lustre file system. Standard S3 file mode can create data-loading bottlenecks for very large training jobs. Fast file mode improves S3 access, but the requirement for tens of Gbps and avoiding expensive GPU idle time points more strongly to FSx for Lustre. Glacier Flexible Retrieval is inappropriate for active training data.

Amazon Kinesis is the right streaming service because it ingests and processes data in real time. For the user-data store, Amazon DynamoDB is the correct database because AWS documents that DynamoDB is a serverless, fully managed, distributed NoSQL database with single-digit millisecond performance at any scale. That directly matches the durability, low-latency, and unknown-scale requirements in the question. RDS is not the most natural fit for unpredictable massive serverless scaling here, while CloudFront and Global Accelerator solve delivery and network-edge problems rather than real-time event ingestion plus low-latency persistence. For a serverless analytics application with streaming game data and highly scalable user storage, Kinesis and DynamoDB are the best pair.

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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

AWS documentation states that the correct and least-privilege method for cross-account SNS-to-SQS integration is:

• Add specific SNS topic ARNs to the SQS queue policy.

• Allow only those topics to publish messages to the queue.

• Ensure SNS has permission to publish to the specific queue ARN.

This ensures strict scoping and adheres to least privilege.

Options A and D grant overly broad permissions. Option B allows publishing to any queue, which violates least privilege.

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

Routing requests to the “appropriate microservices” implies Layer 7 (HTTP/HTTPS) routing such as host-based or path-based rules (for example, /customers to one service and /orders to another). In EKS, the most direct and cost-efficient way to provide this style of routing is to use Kubernetes Ingress with the AWS Load Balancer Controller to provision an Application Load Balancer (ALB). An ALB natively supports HTTP/HTTPS request routing features (paths, hosts, headers), which map cleanly to microservices patterns and reduce the need for additional infrastructure components.

A Network Load Balancer (NLB) (Option A) is primarily Layer 4 and is best for TCP/UDP/TLS pass-through and ultra-high performance. It does not provide the same native HTTP path-based routing behavior required to steer requests between microservices based on URL structure, so it often forces you to add another routing layer (like an in-cluster ingress gateway), which can increase cost and operational complexity.

Option C is not an appropriate request routing mechanism; Lambda is compute, not an ingress/routing layer for EKS. Option D (API Gateway) can front services, but for simple microservice routing into EKS it typically adds per-request costs and additional configuration compared with using an ALB that is purpose-built for HTTP load balancing and routing. API Gateway is a great fit for API management features (throttling, usage plans, keys, transformations), but those are not requirements here.

Therefore, B is the most cost-effective and architecturally aligned solution for HTTP routing across EKS-hosted microservices using managed AWS-native ingress integration.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Key Requirements:

Host the frontend on AWS as a static website.

Protect the application from common web vulnerabilities.

Minimal operational overhead.

Analysis of Options:

Option A:

Hosting the frontend on EC2 with an ALB introduces unnecessary complexity for serving static content.

AWS WAF rules can protect the ALB, but managing EC2 instances adds operational overhead.

Incorrect Approach:High operational complexity for a simple static website.

Option B:

Amazon CloudFront:Acts as a global CDN, reducing latency and protecting against DDoS attacks.

Multiple Origins:Allows static content to be served from S3 while routing API traffic to the on-premises backend.

AWS WAF:Integrates with CloudFront to provide web application protection.

Correct Approach:Offers low operational overhead with optimal security and performance.

Option C:

Using NLB and Network Firewall is unnecessary for a static website. This approach increases cost and complexity without addressing the frontend requirements effectively.

Incorrect Approach:Over-engineered solution.

Option D:

Hosting the frontend on S3 and using API Gateway is a viable option, but managing AWS WAF rules separately for both the S3 bucket and the REST API increases complexity.

Incorrect Approach:Less efficient than using CloudFront with multiple origins.

AWS Solution Architect References:

Amazon CloudFront Overview

AWS WAF with CloudFront

Amazon S3 Multi-Region Access Points allow applications to access S3 buckets in multiple Regions through a single global endpoint. This provides active-active read access with automatic routing to the closest bucket for low latency. With cross-Region replication, writes in the primary Region are automatically copied to the secondary Region, providing fault tolerance. Option A (CloudFront) provides caching and distribution, but does not address write replication or active-active bucket access. Option B (Transfer Acceleration) optimizes uploads across distances but does not enable cross-Region fault tolerance. Option C (AWS Backup) is designed for backup/restore, not real-time multi-Region reads and writes. Therefore, D is the correct solution for active-active read access and disaster recovery.

The most efficient way for management-style reporting is AWS Cost Explorer, because it is designed for interactive cost analysis, filtering, grouping, and exporting reports without building a data pipeline. If the organization has activated cost allocation capabilities (for example, by tagging resources or using other allocation methods), Cost Explorer can present costs in a way that supports chargeback/showback and budgeting. From an operational standpoint, generating and downloading a report from Cost Explorer is quick, requires minimal setup, and is repeatable for periodic budget planning.

Option A (Athena) is powerful but typically requires setting up the Cost and Usage Report (CUR) delivery to Amazon S3, defining schemas, and writing/maintaining SQL queries. That is more operational overhead than needed when the ask is “most efficient way to obtain this report information.” Option C (billing dashboard bill download) provides invoice-style line items, but it is not optimized for slicing/grouping “by user” and may not align with departmental budgeting workflows. Option D is unrelated: AWS Budgets alerts on thresholds and forecasts; it does not generate a billed-items-by-user report.

Important nuance: “by user” reporting in AWS is usually achieved through cost allocation tags, account structure, or other allocation dimensions rather than literal per-person IAM identity billing. In practice, departments/teams/users are mapped via tagging and chargeback structures, which Cost Explorer is intended to analyze and export. As a result, Cost Explorer is the most operationally efficient tool to produce a downloadable report for budget planning.

Therefore, B best meets the requirement with the least effort and fastest path to a usable report.

Amazon DynamoDB is a serverless, NoSQL database that is fully managed, highly available, and scales automatically. It is ideal for data without a fixed schema and for use cases where fields can vary by user. DynamoDB Streams enables the capture of changes to table items in real time, which is ideal for triggering additional processing or workflows on data modifications.

Reference Extract from AWS Documentation / Study Guide:

" DynamoDB provides a scalable, highly available NoSQL database service for applications requiring flexible schema. DynamoDB Streams captures table activity for processing changes in real time. "

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB and Serverless section.

Amazon S3 File Gateway (AWS Storage Gateway) exposes an on-premises NFS/SMB file share that durably stores files as S3 objects with local caching, enabling low-latency writes on-premises and asynchronous, near-real-time ingestion into Amazon S3 for analytics. It is purpose-built to “present a file interface backed by Amazon S3” and to “store files as objects in your S3 buckets,” so existing applications writing to a network share can transparently land data in S3 without custom scripts or job orchestration. Compared with scheduled transfers (e.g., end-of-day DataSync), S3 File Gateway continuously uploads new/changed files, better meeting the near-real-time requirement. Transfer Family (SFTP) would require custom polling and client changes, increasing operational burden. DataSync is excellent for bulk or periodic migrations/synchronizations but is not as seamless for continuous ingestion from a live file share. Therefore, updating the application to point to an S3 File Gateway share provides the simplest, performant path to near-real-time delivery into S3 for downstream analytics.

AWS Backup offers centralized management, scheduling, and retention of backups for supported AWS services including Amazon DynamoDB. It enables compliance retention with minimal management.

From AWS Documentation:

“AWS Backup provides centralized backup management across AWS services, including DynamoDB. You can define backup plans, schedules, and retention policies to meet business or regulatory requirements.”

(Source: AWS Backup Developer Guide – Backing up DynamoDB Tables)

Why B is correct:

Automatically manages backup scheduling and retention (7 years).

Centralized compliance monitoring via AWS Backup Audit Manager.

Fully managed and requires no custom automation.

Why others are incorrect:

A: PITR is for continuous restore within 35 days, not long-term compliance retention.

C & D: Manual or Lambda-based backups require custom management and increase operational complexity.

To meet the requirements, the key must support automatic rotation, and the company must be able to disable (deactivate) the key and schedule deletion. In AWS KMS, these lifecycle controls are available for customer managed keys. Automatic key rotation is supported for symmetric customer managed KMS keys used for encryption and decryption operations. When automatic rotation is enabled, AWS KMS generates new cryptographic material for the key on a regular schedule while the key ID remains the same, helping organizations meet compliance and security best practices without manual operational work.

Option C is the only choice that explicitly combines a symmetric customer managed key with automatic rotation enabled, which directly satisfies the rotation requirement. As a customer managed key, it can also be disabled (to prevent use) and scheduled for deletion (with a waiting period), meeting the rest of the lifecycle needs.

Option A is incorrect because automatic rotation is not generally available for asymmetric KMS keys in the same way; asymmetric keys are used for specialized use cases such as signing or asymmetric encryption, and rotation behavior differs. Options B and D mention “disabling envelope encryption,” which is not a configurable “option” you turn off in KMS; envelope encryption is a recommended pattern where KMS protects data keys, and services commonly use it under the hood. Those options therefore do not describe a valid configuration that meets the requirement.

Therefore, the correct solution is to create a symmetric customer managed KMS key and enable automatic rotation, while using standard KMS controls to disable and schedule deletion when required.

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

The requirement issecure at-least-once processing, not exactly-once processing. Amazon SQSstandard queuesprovide at-least-once delivery, which matches the requirement and is generally more cost-effective than FIFO queues. When Lambda is configured with an encrypted SQS queue that usesSSE-KMS, AWS requires the Lambda execution role to have thekms:Decryptpermission so Lambda can poll and process messages successfully. FIFO queues add ordering and deduplication features that are not required here, so they are not the most economical choice. Option A is therefore the best fit because it combines the correct delivery model, queue encryption with AWS KMS, and the necessary IAM permission for Lambda.

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The requirements are high availability, scalability for traffic spikes, and least administrative overhead. Option C matches established AWS reference patterns: use an Application Load Balancer + Auto Scaling group for the web tier and a managed database service with Multi-AZ for the database tier.

For the web application, placing EC2 instances in an Auto Scaling group across multiple Availability Zones allows the fleet to scale out automatically during the promotional event and to scale in afterward, preserving performance while controlling cost. The Application Load Balancer distributes requests across instances and AZs and removes unhealthy instances from rotation, improving availability.

For the database, Amazon RDS Multi-AZ is the simplest managed HA solution for relational engines, including SQL Server. Multi-AZ provides synchronous replication to a standby instance in another Availability Zone and supports automatic failover. This reduces administrative effort because AWS handles replication, failover orchestration, backups, and underlying infrastructure maintenance within service capabilities. It also ensures database availability during instance or AZ-level disruptions.

Option A is problematic because RDS for SQL Server does not use read replicas as the primary HA mechanism the way Aurora does, and focusing on replicas does not directly address HA in the simplest supported form. Option B and D rely on self-managed SQL Server on EC2, which requires significant operational overhead (patching, backups, replication setup, failover testing, monitoring, and recovery). Option B also suggests multi-Region replication, which adds complexity and is not required to meet “highly available and scalable” for a single-event scale scenario.

Therefore, C provides scalable compute with managed load balancing and managed database high availability, meeting the requirements with the least operational burden.

Amazon Data Lifecycle Manager (DLM) automates the creation, retention, and deletion of EBSsnapshots. This allows organizations to define policies that ensure snapshots are only kept as long as needed, reducing costs automatically and minimizing manual effort. AWS recommends using DLM for optimizing storage and managing backup lifecycle with minimal overhead.

The AWS Cost and Usage Report (CUR) delivers detailed, line-item billing data to Amazon S3. AWS recommends querying CUR with Amazon Athena by creating external tables over the CUR S3 location (partitioned by time) to produce daily cost aggregations such as NAT Gateway (EC2:NatGateway) usage and cost. Amazon QuickSight natively connects to Athena as a data source to build and share dashboards with visuals (tables, time series) filtered from the start of the current month. DataSync (A, C) is a file transfer service and cannot query data. CloudWatch dashboards (C, D) visualize metrics/logs, not CUR datasets. Therefore, using Athena to query CUR and QuickSight to present a daily NAT gateway cost dashboard is the most direct and operationally efficient approach.

Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.

AWS documents that Amazon Macie supportsautomated sensitive data discovery, which continuously discovers and reports sensitive data in S3. AWS also documents that Macie findings can be processed automatically by usingAmazon EventBridge, including sending those events to a Lambda function for remediation or downstream processing. That combination provides continuous monitoring with low operational overhead and avoids the custom scheduling and job-launch logic in the other options. Because the question explicitly asks for continuous monitoring and low overhead, Macie automated sensitive data discovery plus EventBridge-triggered Lambda remediation is the best fit.

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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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The correct answer isDbecause the company needsshared storagefor Amazon EC2 instances that are deployed acrossmultiple Availability Zones, and the application already relies onLinux file system permissionswithout requiring application code changes.Amazon EFSis a fully managed, elastic, shared file system that s upports theNFS protocol, which is commonly used by Linux-based applications. It can be mounted concurrently from multiple EC2 instances across multiple Availability Zones, making it the best fit for this requirement.

Amazon EFS preserves standard file system semantics and POSIX-style permissions, which means the company can continue using familiar Linux file permissions and ownership models. This allows the application to work without code modification. EFS is also highly available and durable because it is designed as a Regional service that stores data redundantly across Availability Zones.

Option A is incorrect because Amazon S3 is object storage, not a native shared POSIX file system. Mounting S3 does not provide the same file system behavior and permissions model required by the application. Option B is incorrect because instance store volumes are ephemeral and tied to individual EC2 instances, so they are not suitable for shared, durable storage across multiple instances. Option C is incorrect because Amazon EBS volumes are block storage volumes that are generally attached to a single instance and are not the best solution for shared multi-AZ file access.

AWS best practices recommendAmazon EFSwhen Linux applications require a shared file system, file permissions, and multi-instance access with minimal operational changes. Therefore,Amazon EFSis the most appropriate solution.

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.

Aservice control policyattached at the organization root is the right preventive control because SCPs define the maximum permissions for principals in member accounts across the organization. AWS documentation states that SCPs restrict permissions for IAM users and roles in member accounts and that an explicit deny overrides allows from other policies. That makes an SCP the cleanest centralized way to block creation of IAM users and access keys everywhere. Inline user policies would be harder to maintain, and GuardDuty or Config would be detective or corrective controls rather than the preventive control the question asks for.

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Securing the root user account requires setting a strong password and enabling multi-factor authentication (MFA). AWS recommends never sharing the root user credentials, setting up individual IAM users for everyday operations, and always protecting the root user with MFA for maximum security.

Reference Extract:

" AWS recommends securing the root user with a strong password and enabling multi-factor authentication (MFA). Do not use or share root credentials for everyday tasks. "

Source: AWS Certified Solutions Architect – Official Study Guide, IAM and Security Best Practices section.