Pass the Amazon Web Services AWS Certified Associate SAA-C03 Questions and answers with ExamsMirror

Amazon SQS FIFO queues are designed for workloads that require strict message ordering and exactly-once processing semantics. This makes FIFO the correct queue type for an order processing system that must preserve exact order. SQS also provides decoupling between producers and consumers, improving resilience because order ingestion does not depend on the processor being immediately available. AWS Lambda can poll SQS and process messages without managing servers, while the SQS queue buffers orders durably. A standard SQS queue does not guarantee exact ordering. SNS alone is a pub/sub service and does not provide the same ordered queue processing requirement. Kinesis can preserve ordering within shards, but using EC2 Spot Instances introduces interruption risk and unnecessary operational responsibility for this architecture.

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 correct answer isBbecause the company has apredictable baseline workloadthat will run forat least 1 year, plus atemporary 2-week increasein demand during a holiday period. The most cost-effective approach is to use a long-term discount option for the steady-state workload and a flexible pricing model for the short-term burst capacity.

AnEC2 Instance Savings Planis a strong fit for the existing workload because it provides cost savings for committed EC2 usage while matching a stable, predictable application footprint. Since the company is already using EC2 and the workload pattern is known, this is an efficient commitment for the baseline capacity. For the additional temporary holiday demand,On-Demand Instancesare appropriate because the extra capacity is needed only for a short duration. This avoids overcommitting to capacity that will not be used after the holiday period ends.

Option A is less cost-effective because it misses the savings opportunity for the predictable baseline workload. Option C is incorrect because the application is described asstateful, which makesSpot Instancesa poor fit due to potential interruption. Option D is incorrect because purchasing a 12-month commitment for both the normal workload and the temporary holiday increase would likely result in paying for excess committed usage after the 2-week peak ends.

AWS cost optimization guidance recommends usingdiscounted commitment models for steady-state usageandOn-Demandfor short-lived, temporary spikes. Therefore, the best answer is to use anEC2 Instance Savings Planfor the baseline andOn-Demand Instancesfor the seasonal increase.

Amazon EFS provides a fully managed, highly available, and shared file system that can be mounted by instances across multiple Availability Zones. This ensures consistency of files between EC2 instances and avoids replication issues. EBS volumes, in contrast, are AZ-scoped and not designed for multi-instance sharing. Options A and B rely on custom replication or manual file handling, which increases operational overhead and risks inconsistencies. Option D does not solve the shared access and consistency requirement. By migrating storage to EFS, both EC2 instances will read and write to the same storage system, ensuring that users always access the latest files without 404 errors.

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

The correct answer isDbecause the application peak hours occur at thesame time each day, and the performance issue happens specifically at thestart of peak hours. This indicates that the company knows in advance when additional capacity will be needed. Ascheduled scaling policyis the most appropriate solution because it allows the Auto Scaling group to increase capacitybeforedemand rises, ensuring that instances are already launched and available when users begin accessing the application.

Dynamic scaling based on CPU or memory reacts onlyafterutilization increases. Because launching EC2 instances takes time, reactive scaling can lead to delays and degraded user experience at the beginning of peak periods. That directly matches the problem described in the question, where performance is slow at the start but normalizes later once capacity catches up. Scheduled scaling solves this by making capacity available proactively.

Option A is incorrect because an Application Load Balancer distributes traffic among existing instances but does not add capacity ahead of predictable demand. Option B is incorrect because Auto Scaling does not natively scale on memory utilization unless a custom metric is configured, and even then it would still be reactive rather than proactive. Option C is also incorrect because CPU-based dynamic scaling reacts after demand increases and therefore may not prevent the startup lag during peak traffic.

AWS design guidance recommends usingscheduled scalingwhen traffic patterns are predictable and occur at known times. This approach improves application responsiveness and user experience by making sure enough EC2 capacity is available before the workload surge begins.

The correct answer isDbecause the application runs onAWS Fargate On-Demand capacity, cannot tolerate interruptions, and the company wants tooptimize costswhile keeping the application operational. ACompute Savings Planis designed to reduce costs for consistent compute usage across AWS services, includingAWS Fargate, without requiring changes to the application architecture or using interruptible capacity.

A Savings Plan provides discounted pricing in exchange for a usage commitment over a period of time. This is ideal for workloads that must continue running without interruption because the application can remain onFargate On-Demand, which avoids the termination risk associated with Spot capacity. The company gets cost savings while preserving the reliability characteristics of the current deployment model.

Option A is incorrect becauseOn-Demand Capacity Reservationsare used to reserve EC2 capacity in a specific Availability Zone and do not apply as the main cost optimization mechanism for Fargate tasks. Option B is incorrect becauseConvertible Reserved Instancesapply to Amazon EC2, not Fargate. Option C is incorrect becauseFargate Spotcan be interrupted and therefore does not meet the requirement that the application cannot tolerate sudden interruptions.

AWS cost optimization guidance recommends selecting discount models that align with workload needs. For predictable, steady serverless container usage on Fargate that must remain highly available,Compute Savings Plansoffer the best balance of lower cost and uninterrupted operation. Therefore, purchasing aCompute Savings Planis the most appropriate solution.

Same scenario as above—cost-effective cold start reduction for Java Lambdas is best achieved by using Lambda SnapStart with the correct code structure. Unique IDs and similar non-deterministic logic should be in the handler or in a pre-snapshot hook. SnapStart is supported only for published versions. This combination offers cold start performance at a lower cost than provisioned concurrency.

AWS Documentation Extract:

" When using Lambda SnapStart for Java, ensure non-deterministic initialization code, such as unique ID generators, is executed inside the handler or in a pre-snapshot hook. SnapStart is supported only on published versions. "

(Source: AWS Lambda documentation, SnapStart for Java)

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.

The best answer isAPI Gateway + SQS + DLQ + Lambdabecause it combines API ingestion with durable queuing and asynchronous processing. AWS guidance for event-driven serverless architectures specifically recommends usingAmazon SQSto decouple components and durably persist messages when downstream processing is slower or bursty. AWS also documents thatdead-letter queueshelp isolate messages that fail processing, which supports the requirement to avoid losing requests. API Gateway can integrate with AWS services, including SQS, to accept requests from mobile and web clients. The ALB options do not provide the same durable buffering, and the SNS option is less appropriate because the key requirement is loose coupling with durable request preservation. Therefore,Bis the strongest design. (AWS Documentation)

For bidirectional communication such as chat applications, Amazon API Gateway WebSocket API is the correct service. WebSocket APIs allow clients to establish long-lived connections and exchange messages with the backend Lambda functions in real time.

HTTP APIs and REST APIs are suitable for request-response models, not continuous two-way communication. CloudFront cannot maintain stateful WebSocket connections, so only Option C fits the requirements for a real-time, bidirectional application.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

For infrequent or ad hoc querying of log data, Amazon S3 + Amazon Athena provides the most cost-effective, serverless, and scalable analytics solution.

From AWS Documentation:

“Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”

(Source: Amazon Athena User Guide)

Why A is correct:

Amazon S3 offers durable, scalable, and cost-efficient storage.

Athena allows SQL-based querying on structured or semi-structured data like logs.

No need to provision or manage infrastructure.

Ideal for occasional querying at low cost.

Why the others are not optimal:

Option B: OpenSearch adds cost and is best for frequent, low-latency log querying.

Option C: RDS is not optimized for large-scale write-heavy log ingestion and costs more.

Option D: CloudWatch Logs is suitable for real-time monitoring, not for long-term storage and analytics of large log volumes.

The correct answer isCbecause the company already usesAmazon FSx for NetApp ONTAPand requires a disaster recovery solution in a secondary Region that preserves access through thesame protocols, specificallyCIFS and NFS. The most appropriate solution is to deploy a secondFSx for ONTAPfile system in the secondary Region and useNetApp SnapMirrorto replicate data between Regions. SnapMirror is built for ONTAP-based replication and is the native mechanism for efficient, storage-level disaster recovery.

This option provides the least operational overhead because it uses the capabilities already integrated into FSx for ONTAP rather than introducing another storage platform or custom replication tooling. It also preserves protocol compatibility, so applications in the recovery Region can continue to access data through CIFS and NFS without architectural changes.

Option A is incorrect because replicating data to Amazon S3 does not preserve native CIFS and NFS file shares. Option B can support recovery, but backups and restores are not the best fit for an active DR strategy where the secondary Region needs accessible replicated storage with the same protocols and lower recovery effort. Option D is incorrect because Amazon EFS does not provide CIFS/SMB support in the same way as FSx for ONTAP and would require a platform change.

AWS guidance for FSx for ONTAP disaster recovery favors usingSnapMirror replicationto another FSx for ONTAP deployment. This approach is protocol-compatible, efficient, and operationally simpler than building a custom DR workflow.

AWS Lambda function URLs are specifically designed to give a Lambda function a simple direct HTTP(S) endpoint. AWS documentation describes function URLs as a straightforward way to invoke a Lambda function through an HTTP request, and AWS even provides a webhook tutorial built around Lambda function URLs. That makes this the most operationally efficient option because it avoids placing an ALB in front of the function or building an unnecessary messaging layer with SNS or SQS. The requirement is simply to expose the Lambda function so a third party can call it through a webhook. Function URLs are the lightest-weight AWS-native feature for that use case and provide the fastest path from webhook sender to function execution. (AWS Documentation)

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Amazon EBS Snapshots Recycle Bin enables you to specify retention rules for EBS snapshots based on tags. When snapshots are deleted, they are retained in the Recycle Bin for a specified duration, preventing accidental deletion. Tag-level rules allow selective protection without changing IAM roles or user permissions.

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

AWS Firewall Manager integrates with AWS Organizations to centrally manage and apply security group policies, AWS WAF rules, and AWS Shield Advanced protections. It automates the propagation of rules across accounts and Regions and can also audit and remediate noncompliant configurations.

AWS recommends Auto Scaling with dynamic scaling policies to handle unpredictable workload spikes. Target tracking scaling policies automatically adjust capacity based on defined metrics such as average CPU utilization or request count per target. This approach ensures applications maintain responsiveness without overprovisioning. Scheduled scaling (options B and C) is only effective when traffic patterns are predictable, which is not the case here. Reserved Instances or Spot Instances also do not address sudden demand changes effectively. Replacing the ALB with an NLB (D) does not solve latency caused by EC2 instance capacity. Therefore, using EC2 instances in an Auto Scaling group with a target tracking scaling policy is the most effective and cost-efficient solution for unpredictable, short-lived traffic spikes.