Amazon AWS Certified Solutions Architect - Associate SAA-C03 Exam Dumps & Practice Test Questions

Question 1:

Your organization gathers temperature, humidity, and pressure data daily—approximately 500 GB per site—from cities around the world. Every location has access to high-speed internet. The goal is to consolidate all this data into a single Amazon S3 bucket as quickly and simply as possible, minimizing the need for manual intervention and operational overhead.

Which approach best satisfies these criteria?

A. Enable S3 Transfer Acceleration on the target bucket and use multipart uploads from each site.
B. Upload data to a nearby S3 bucket, then use Cross-Region Replication to copy it to the central S3 bucket.
C. Use AWS Snowball Edge devices to move data daily from each site to their nearest region, then replicate.
D. Send data to EC2 instances in nearby regions, store it in EBS volumes, and snapshot/copy it to the destination region.

Correct Answer: A

Explanation:

The most efficient and low-maintenance method for transferring large volumes of global data to a centralized Amazon S3 bucket is to use S3 Transfer Acceleration along with multipart uploads.

S3 Transfer Acceleration uses Amazon CloudFront’s globally distributed edge locations to route uploads, improving transfer speeds over long distances. This is ideal for globally dispersed data sources with good internet connectivity. By enabling Transfer Acceleration on the destination S3 bucket, each site can upload its data to the closest edge location, which then efficiently relays it to the central bucket.

Additionally, using multipart uploads allows large files to be broken into parts and uploaded in parallel, further enhancing speed and reliability. This combination reduces latency, minimizes upload time, and is easy to implement with little to no maintenance once configured.

The other options, while technically viable, add unnecessary layers of complexity:

• Option B involves setting up and managing multiple buckets and replication policies, increasing operational overhead.

• Option C uses AWS Snowball Edge, which is suitable for offline or low-bandwidth scenarios—not ideal when high-speed internet is available.

• Option D is the most complex, involving EC2, EBS management, snapshot scheduling, and cross-region replication, which significantly increases both cost and operational burden.

Thus, Option A is the optimal solution—it offers high performance, global acceleration, minimal infrastructure setup, and operational simplicity.

Question 2:

Your company keeps application logs in JSON format within an Amazon S3 bucket. Management wants to perform on-demand, simple SQL queries against this log data. They prefer a solution that requires minimal changes to the current setup and as little operational overhead as possible.

Which solution should you recommend?

A. Import all logs into Amazon Redshift and query from there.
B. Move logs into Amazon CloudWatch Logs and query them using the CloudWatch console.
C. Use Amazon Athena to query the JSON logs directly from S3.
D. Catalog the data with AWS Glue and analyze it using Apache Spark on Amazon EMR.

Correct Answer: C

Explanation:

Amazon Athena is the most suitable choice for querying structured or semi-structured data (like JSON) stored in Amazon S3, without requiring additional infrastructure or complex processing pipelines. It’s serverless, meaning you don’t need to manage any servers or clusters, and it supports SQL queries right out of the box. This perfectly aligns with the company’s need for minimal architectural changes and low operational effort.

Since the logs already reside in S3, Athena can query them directly without moving or transforming the data. You can define a schema using a simple SQL-like language or with AWS Glue as a data catalog (though Glue is optional for basic use). The company can immediately start running on-demand queries on the existing JSON log files with very little configuration.

Let’s examine the alternatives:

• Option A, using Redshift, would require loading all data into a data warehouse—introducing data pipelines, ETL jobs, and ongoing maintenance. It's overkill for simple queries.

• Option B, while suitable for real-time application logs, would require importing existing logs into CloudWatch Logs and setting up metrics and insights—not ideal for bulk or historical queries.

• Option D, using Glue and EMR with Spark, provides powerful analytics capabilities but adds unnecessary complexity. Setting up and maintaining EMR clusters contradicts the requirement for minimal operational overhead.

Ultimately, Amazon Athena provides a lightweight, scalable, and highly cost-effective method for running SQL queries directly on JSON log files in S3, making Option C the most efficient solution.

Question 3:

A company uses AWS Organizations to manage various AWS accounts across departments. The management account owns an Amazon S3 bucket that stores project reports. The company wants to ensure that only users from accounts that are part of the same AWS Organization can access this bucket, and they want to keep management overhead to a minimum.

Which solution best enforces this access control with the least complexity?

A. Add the aws:PrincipalOrgID condition key referencing the organization ID to the S3 bucket policy.
B. Create a separate organizational unit (OU) for each department and use the aws:PrincipalOrgPaths condition key in the bucket policy.
C. Use AWS CloudTrail to monitor account changes, and manually update the S3 bucket policy accordingly.
D. Assign specific tags to users who need access, and use aws:PrincipalTag condition key in the S3 bucket policy.

Correct Answer: A

Explanation:

To restrict access to an S3 bucket strictly to accounts within the same AWS Organization, the most efficient and low-maintenance solution is to use the aws:PrincipalOrgID global condition key in the bucket policy. This key allows you to reference your Organization ID directly in the policy, ensuring that only principals (users, roles, or services) from accounts in the same AWS Organization can access the bucket.

This method is highly scalable. As new accounts are added or removed from the organization, the access control automatically adjusts—no manual intervention is needed. There's no need to manage organizational units (OUs), track user tags, or continuously monitor account creation/deletion events.

Let’s review the alternatives:

• Option B uses aws:PrincipalOrgPaths, which is valid but more granular and requires defining and managing OUs. It adds unnecessary complexity when the goal is to allow access across the entire organization.

• Option C proposes using AWS CloudTrail to monitor changes and then manually update the policy. This is operationally expensive and reactive, not proactive, and it doesn't scale well.

• Option D requires tagging each user and controlling access via aws:PrincipalTag. This approach introduces a lot of management overhead, especially as the number of users and accounts increases.

Therefore, Option A is the optimal solution, offering the simplest, most scalable, and maintenance-free way to enforce organization-wide access to an S3 bucket.

Question 4:

You have an application running on an Amazon EC2 instance within a VPC. The application needs to retrieve logs from an Amazon S3 bucket. However, the EC2 instance cannot have internet connectivity due to security policies.

What is the best way to allow the EC2 instance to securely access the S3 bucket using only internal AWS networking?

A. Create a gateway VPC endpoint for Amazon S3.
B. Stream logs to Amazon CloudWatch Logs and export them to S3.
C. Attach an instance profile to the EC2 instance for S3 access.
D. Use Amazon API Gateway with a private link to route traffic to the S3 endpoint.

Correct Answer: A

Explanation:

To allow private, secure connectivity between a VPC and Amazon S3 without internet access, the most straightforward solution is to use a gateway VPC endpoint. This type of endpoint is specifically designed for services like S3 and DynamoDB and enables traffic to stay within the AWS network.

With a gateway endpoint, your EC2 instance can directly access the S3 bucket using AWS’s internal network—bypassing the need for a NAT gateway, internet gateway, or any public routing.

Let’s evaluate the other options:

• Option B suggests using CloudWatch Logs as an intermediary, which is unnecessary and doesn't establish private S3 access. It's a logging solution, not a networking one.

• Option C (adding an instance profile) is necessary for authorization, but it does not provide network connectivity. You would still need internet access or a VPC endpoint to reach S3.

• Option D involves creating an API Gateway and integrating it with PrivateLink, which is a complex workaround and not intended for direct S3 access.

Thus, Option A—setting up a gateway VPC endpoint for S3—is the most secure, efficient, and straightforward way to meet the requirement of private connectivity.

Question 5:

A company runs a web application on AWS using two EC2 instances in different Availability Zones, each attached to its own EBS volume. These instances are behind an Application Load Balancer. Users upload documents to the application, but after the architecture was duplicated, users noticed that they could only see a portion of their uploaded files depending on which instance served their request.

What should a solutions architect recommend to ensure all users always see all of their documents, regardless of which instance they access?

A. Duplicate all files to both EBS volumes on each instance.
B. Configure the Load Balancer to send each user to the instance where their documents are stored.
C. Migrate documents to Amazon EFS and update the application to read/write to EFS.
D. Modify the Load Balancer to forward requests to both instances and return merged results.

Correct Answer: C

Explanation:

The issue stems from each EC2 instance using separate EBS volumes, which are not shared storage solutions. Since user documents are distributed between two isolated storage volumes, users get inconsistent views of their files based on which instance serves them.

The best solution is to migrate file storage to Amazon EFS (Elastic File System). EFS is a shared file system that can be mounted simultaneously by multiple EC2 instances across Availability Zones. This ensures that both instances see and interact with the same data, delivering a consistent experience to all users.

Let’s break down why the other options are suboptimal:

• Option A (manually syncing EBS volumes) introduces data duplication and synchronization complexity. It’s prone to errors and does not scale well.

• Option B (session-based routing) adds brittle logic to route users to specific instances and still doesn't ensure high availability or scalability.

• Option D (splitting requests between instances) is not feasible and adds unnecessary complexity in managing file consistency across separate disks.

By switching to Amazon EFS, the company gains a highly available, durable, and scalable shared storage solution that solves the root problem. It allows both EC2 instances to read and write to the same file system in real time, ensuring users always see the full set of their documents.

Question 6:

A media company stores large video files—ranging from 1 MB to 500 GB—on its on-premises NFS-based NAS system. The total dataset is approximately 70 TB. The company has decided to migrate all video content to Amazon S3. The team wants to complete this migration as fast as possible while keeping the impact on the network bandwidth to a minimum.

Which of the following solutions is the most appropriate to meet these needs?

A. Use AWS CLI to upload all files to Amazon S3 after configuring IAM permissions.
B. Order an AWS Snowball Edge, transfer the data locally to the device, and ship it back for AWS to upload to S3.
C. Deploy an S3 File Gateway, configure an NFS file share pointing to an S3 bucket, and copy the data over.
D. Set up AWS Direct Connect, configure a public virtual interface (VIF), deploy S3 File Gateway, and transfer the data.

Answer: B

Explanation:

When migrating a large dataset like 70 TB from on-premises storage to Amazon S3, transferring over the network—especially over a public internet connection—can be slow, costly, and bandwidth-intensive. In such cases, a physical data transport service like AWS Snowball Edge offers the fastest and most efficient method with minimal impact on the network.

With Option B, the company can request an AWS Snowball Edge device, connect it to the local network, and transfer all 70 TB of data using the Snowball Edge client. Once the data transfer is complete, the device is shipped back to AWS, where the data is securely uploaded into the specified S3 bucket. This bypasses the need for large-scale internet-based data transfer and avoids potential downtime or throttling due to network saturation.

Option A, using the AWS CLI over the internet, is not optimal because transferring 70 TB can take several days or even weeks depending on the available bandwidth, and it places a significant burden on the network infrastructure.

Option C, involving an S3 File Gateway, is more suitable for ongoing hybrid storage access or incremental uploads. While technically feasible for migration, it still relies on network capacity and may result in delays.

Option D, using AWS Direct Connect and S3 File Gateway, offers a dedicated line to AWS, but setting it up takes considerable time and cost. It also doesn’t remove the network bandwidth issue entirely during the actual data transfer.

Thus, Snowball Edge is the fastest and most network-efficient approach for a one-time, large-volume data migration to Amazon S3.

Question 7:

A company operates a central application that ingests a massive and variable number of messages—sometimes spiking to 100,000 messages per second. These messages need to be consumed quickly by several downstream applications and microservices. The company wants to decouple message ingestion from processing while improving scalability.

Which solution best supports these requirements?

A. Store the messages in Amazon Kinesis Data Analytics and configure consumers to read from it.
B. Use EC2 Auto Scaling to scale message ingestion based on CPU load.
C. Ingest messages into Kinesis Data Streams (1 shard), preprocess with Lambda, store in DynamoDB, then let consumers read from DynamoDB.
D. Publish messages to Amazon SNS and subscribe multiple SQS queues, each serving a separate consumer.

Answer: D

Explanation:

To achieve both high scalability and decoupling between producers and consumers in a messaging architecture, Amazon SNS combined with Amazon SQS is the most effective approach.

Option D leverages Amazon SNS to broadcast (fan out) incoming messages to multiple SQS queues. Each queue can be independently processed by a dedicated application or microservice. This model fully decouples the producer (message publisher) from the consumers, allowing each system to scale independently. It also supports horizontal scaling, meaning more consumer instances or queues can be added as traffic increases.

SQS ensures durable message storage, retries, and at-least-once delivery, which makes it ideal for bursty workloads. This architecture can easily absorb sudden surges, such as 100,000 messages per second, without losing data or requiring complex synchronization.

Option A with Kinesis Data Analytics focuses more on real-time analytics, not decoupling nor wide fan-out. It’s not designed to deliver messages to multiple independent consumers simultaneously.

Option B, using EC2 instances in an Auto Scaling group, may help scale message ingestion, but it doesn't provide decoupling. Also, scaling based on CPU is reactive and not as responsive to sudden, unpredictable traffic spikes.

Option C introduces unnecessary complexity by using Kinesis with only one shard (which is insufficient for high-volume throughput), Lambda, and DynamoDB. Also, DynamoDB isn’t a messaging solution and lacks the native features needed for reliable message fan-out.

Therefore, Option D offers the best combination of scalability, resilience, and architecture flexibility for a high-throughput, message-driven system.

Question 8:

A company plans to migrate a distributed application with variable workloads to AWS. The existing system uses a central server to delegate jobs across multiple compute nodes. The company seeks to modernize the application for maximum scalability and fault tolerance. 

What architectural solution should a solutions architect implement to meet these goals?

A. Use Amazon SQS for job queuing and Amazon EC2 instances in an Auto Scaling group with scheduled scaling.
B. Use Amazon SQS for job queuing and Amazon EC2 instances in an Auto Scaling group that scales based on queue size.
C. Use EC2 instances in an Auto Scaling group for both the primary server and compute nodes. Send jobs to AWS CloudTrail and scale based on primary server load.
D. Use EC2 instances in an Auto Scaling group for the primary server and compute nodes. Use Amazon EventBridge for job distribution and scale based on compute node load.

Correct Answer: B

Explanation:

To modernize a distributed application with fluctuating workloads in AWS, the ideal solution should emphasize decoupling, scalability, and resiliency. Option B meets all of these requirements effectively.

By using Amazon Simple Queue Service (SQS), the job submission process is decoupled from job processing. This allows compute nodes (Amazon EC2 instances) to pull and process tasks asynchronously, which improves scalability and fault tolerance. SQS serves as a buffer, smoothing out traffic spikes and ensuring that jobs are reliably stored and processed even if downstream compute resources are temporarily unavailable.

Configuring the EC2 Auto Scaling group to adjust based on the number of messages in the SQS queue ensures that the infrastructure dynamically scales up during periods of high job volume and scales down during idle times. This dynamic responsiveness not only increases resilience but also helps optimize resource utilization and cost.

Option A uses scheduled scaling, which is insufficient for workloads with unpredictable or spiky patterns, as it relies on predefined schedules instead of real-time demand.

Option C misuses AWS CloudTrail, which is meant for logging and monitoring API activity, not job queuing or processing. It cannot manage workload distribution or support scalability.

Option D proposes Amazon EventBridge, which is ideal for event-driven architectures, but not for managing a high volume of queued tasks that require ordered or decoupled processing.

In summary, Option B provides a scalable, resilient, and cost-effective solution by combining Amazon SQS with Auto Scaling EC2 instances based on queue size—making it the most suitable approach for modernizing the application.

Question 9:

A company uses an on-premises SMB file server that stores large files. These files are accessed frequently during the first 7 days after creation, then rarely accessed. As data continues to grow, the company is nearing its storage limit. 

What AWS-based solution can expand storage and provide automated lifecycle management for files?

A. Use AWS DataSync to transfer files older than 7 days to AWS.
B. Use Amazon S3 File Gateway to expand storage and implement an S3 Lifecycle policy to move files to S3 Glacier Deep Archive after 7 days.
C. Deploy Amazon FSx for Windows File Server to increase storage capacity.
D. Equip each user with a tool to access S3, and apply an S3 Lifecycle policy to move files to Glacier Flexible Retrieval after 7 days.

Correct Answer: B

Explanation:

To handle a rapidly expanding dataset while maintaining efficient access to frequently used files, Option B offers the most comprehensive and cost-effective solution.

Amazon S3 File Gateway enables hybrid cloud storage by extending your on-premises file storage to Amazon S3. It provides a local cache that ensures low-latency access to recently used files, while seamlessly uploading them to S3 for durable, scalable storage. As the total data size increases, the gateway offloads older files to S3, thus freeing up on-premises storage.

Additionally, configuring an S3 Lifecycle policy allows files older than 7 days to be automatically transitioned to S3 Glacier Deep Archive, which is the most cost-effective storage class for rarely accessed data. This strategy ensures that while the files remain preserved long-term, storage costs are minimized.

Option A (AWS DataSync) is suitable for data transfer but lacks the built-in, ongoing lifecycle management required to automate movement between storage tiers.

Option C (Amazon FSx) supports Windows-based file systems but doesn’t offer lifecycle transitions or the deep archiving benefits of S3. It would simply provide more storage space without addressing long-term cost efficiency.

Option D (having users access S3 directly via a utility) is impractical at scale, introduces operational overhead, and does not leverage automated management or caching like S3 File Gateway does.

In summary, Option B effectively addresses the need for scalable storage, low-latency access to recent files, and automated archival, making it the most balanced and sustainable long-term solution.

Question 10:

An ecommerce company wants to ensure that new customer orders submitted through an Amazon API Gateway REST API are processed in the exact sequence they are received. 

What architecture should the solutions architect use to enforce strict ordering of order processing?

A. Use API Gateway to publish messages to an Amazon SNS topic, with an AWS Lambda subscriber for processing.
B. Use API Gateway to send messages to an Amazon SQS FIFO queue, and trigger a Lambda function for processing.
C. Use an API Gateway authorizer to block new requests until current orders are processed.
D. Use API Gateway to send messages to an Amazon SQS standard queue, and invoke a Lambda function for processing.

Correct Answer: B

Explanation:

When strict message ordering is essential—as in ecommerce systems where orders must be processed in the exact order they arrive—the most appropriate AWS service is an Amazon SQS FIFO (First-In-First-Out) queue.

In Option B, integrating API Gateway with an SQS FIFO queue ensures that each message (order) is delivered and processed in the precise order it was received. SQS FIFO queues maintain message order within a message group, which is crucial for ensuring data consistency, correct transaction handling, and customer satisfaction.

The FIFO queue can be configured to trigger an AWS Lambda function for processing each order sequentially. Additionally, FIFO queues support exactly-once processing, which prevents duplicate transactions—an essential requirement in order handling scenarios.

Option A (Amazon SNS) lacks ordering guarantees and is designed for pub/sub messaging patterns, not transactional workflows. Messages sent through SNS could reach subscribers in any order, violating the sequencing requirement.

Option C misuses the concept of an API Gateway authorizer, which is meant for access control and authorization, not for controlling processing sequence or message queuing.

Option D uses an SQS standard queue, which offers high throughput but does not guarantee message order, making it unsuitable for sequential processing.

Ultimately, Option B combines the reliable sequencing capabilities of SQS FIFO queues with the flexible compute of AWS Lambda, ensuring a scalable and orderly order processing pipeline that aligns with business requirements.