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AWS Certified Solutions Architect - Associate SAA-C03 Questions 401–405 (page: 82)

Updated On: 16-Mar-2026

A company wants to use the AWS Cloud to make an existing application highly available and resilient. The current version of the application resides in the company's data center. The application recently experienced data loss after a database server crashed because of an unexpected power outage.
The company needs a solution that avoids any single points of failure. The solution must give the application the ability to scale to meet user demand.
Which solution will meet these requirements?

  1. Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones. Use an Amazon RDS DB instance in a Multi-AZ configuration.
  2. Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group in a single Availability Zone. Deploy the database on an EC2 instance. Enable EC2 Auto Recovery.
  3. Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones. Use an Amazon RDS DB instance with a read replica in a single Availability Zone. Promote the read replica to replace the primary DB instance if the primary DB instance fails.
  4. Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones. Deploy the primary and secondary database servers on EC2 instances across multiple Availability Zones. Use Amazon Elastic Block Store (Amazon EBS) Multi-Attach to create shared storage between the instances.

Answer(s): A

Explanation:

A) The combination of an Auto Scaling group across multiple Availability Zones provides high availability, fault tolerance, and horizontal scaling for compute. Using an RDS DB instance in a Multi-AZ configuration delivers synchronous standby failover, protecting against DB failures and data loss. This setup avoids single points of failure and supports scale-out/in.
B) Single AZ loses AZ-level redundancy; EC2 Auto Recovery does not protect the database and does not provide multi-AZ fault tolerance. C) Read replica in a single AZ cannot sustain primary DB failure without manual promotion and does not provide multi-AZ failover for the write primary. D) EBS Multi-Attach is for shared block storage across AZs but not a managed, highly available DB solution and adds complexity.



A company needs to ingest and handle large amounts of streaming data that its application generates. The application runs on Amazon EC2 instances and sends data to Amazon Kinesis Data Streams, which is configured with default settings. Every other day, the application consumes the data and writes the data to an Amazon S3 bucket for business intelligence (BI) processing. The company observes that Amazon S3 is not receiving all the data that the application sends to Kinesis Data Streams.
What should a solutions architect do to resolve this issue?

  1. Update the Kinesis Data Streams default settings by modifying the data retention period.
  2. Update the application to use the Kinesis Producer Library (KPL) to send the data to Kinesis Data Streams.
  3. Update the number of Kinesis shards to handle the throughput of the data that is sent to Kinesis Data Streams.
  4. Turn on S3 Versioning within the S3 bucket to preserve every version of every object that is ingested in the S3 bucket.

Answer(s): A

Explanation:

K) Increasing shard count to match throughput is the typical fix; however the correct answer provided is A. Update the Kinesis Data Streams default settings by modifying the data retention period. If data retention is too short, records expire before being consumed and delivered to S3, causing data loss. B) KPL improves producer efficiency but does not prevent data loss due to retention; C) still requires more shards but not addressing retention window. D) S3 Versioning does not affect ingestion or durability of streaming data to S3.



A developer has an application that uses an AWS Lambda function to upload files to Amazon S3 and needs the required permissions to perform the task. The developer already has an IAM user with valid IAM credentials required for Amazon S3.
What should a solutions architect do to grant the permissions?

  1. Add required IAM permissions in the resource policy of the Lambda function.
  2. Create a signed request using the existing IAM credentials in the Lambda function.
  3. Create a new IAM user and use the existing IAM credentials in the Lambda function.
  4. Create an IAM execution role with the required permissions and attach the IAM role to the Lambda function.

Answer(s): D

Explanation:

A Lambda function that needs to access S3 should assume an execution role with the required permissions. D is correct because creating an IAM execution role containing S3 permissions and attaching it to the Lambda grants the function the necessary access at runtime in a least-privilege, scalable way. A is incorrect because resource policy on the Lambda function does not grant the function access to S3; permissions must be in an execution role. B is incorrect because signing requests with user credentials inside Lambda is insecure and not how AWS Lambda should authenticate. C is incorrect because creating a new IAM user is unnecessary and would require embedding credentials, which is discouraged.



A company has deployed a serverless application that invokes an AWS Lambda function when new documents are uploaded to an Amazon S3 bucket. The application uses the Lambda function to process the documents. After a recent marketing campaign, the company noticed that the application did not process many of the documents.
What should a solutions architect do to improve the architecture of this application?

  1. Set the Lambda function's runtime timeout value to 15 minutes.
  2. Configure an S3 bucket replication policy. Stage the documents in the S3 bucket for later processing.
  3. Deploy an additional Lambda function. Load balance the processing of the documents across the two Lambda functions.
  4. Create an Amazon Simple Queue Service (Amazon SQS) queue. Send the requests to the queue. Configure the queue as an event source for Lambda.

Answer(s): D

Explanation:

Submitting to an SQS queue and configuring it as an event source improves reliability and decouples ingestion from processing, enabling buffering during spikes and retry/backoff for failed invokes, which helps process more documents consistently.
A) Increasing Lambda timeout to 15 minutes is not aligned with typical S3-triggered processing; Lambda max is 15 minutes but not addressing throttling/buffering and may mask performance issues.
B) S3 replication only duplicates data; it does not improve processing throughput or reliability for Lambda processing.
C) Adding another Lambda without a queue does not address potential concurrent invocation limits, backpressure, or error handling as effectively as a queue-based decoupling.
D) Correct.



A solutions architect is designing the architecture for a software demonstration environment. The environment will run on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). The system will experience significant increases in traffic during working hours but is not required to operate on weekends.
Which combination of actions should the solutions architect take to ensure that the system can scale to meet demand? (Choose two.)

  1. Use AWS Auto Scaling to adjust the ALB capacity based on request rate.
  2. Use AWS Auto Scaling to scale the capacity of the VPC internet gateway.
  3. Launch the EC2 instances in multiple AWS Regions to distribute the load across Regions.
  4. Use a target tracking scaling policy to scale the Auto Scaling group based on instance CPU utilization.
  5. Use scheduled scaling to change the Auto Scaling group minimum, maximum, and desired capacity to zero for weekends. Revert to the default values at the start of the week.

Answer(s): D,E

Explanation:

The correct options are D and E.
D) A target tracking scaling policy adjusts the Auto Scaling group based on a metric (CPUUtilization) to maintain a target value, enabling responsive scaling with load.
E) Scheduled scaling changes min/max/desired capacity on weekends, ensuring no weekend load and rapid restoration at start of week for predictable cost and capacity management.
A is incorrect because ALB capacity is managed automatically by AWS; scaling should target ASG capacity, not ALB. B is incorrect because VPC internet gateway does not scale; scaling applies to instances and ASG. C is incorrect because cross-region deployment adds latency and complexity for a demo and is not required for scaling within a single region.



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