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

The right solution isAPI Gateway + Lambda + Amazon Comprehend + DynamoDB. AWS documents that Amazon Comprehend is the NLP service for detecting entities, sentiment, and key phrases, which matches the workload exactly. DynamoDB is a fully managed, distributed database designed for high-throughput workloads and single-digit millisecond performance at scale, making it a strong fit for a highly available application handling many concurrent requests. The other answers misuse services: Amazon Translate is for translation, Amazon Textract is for extracting text from documents, and ElastiCache is a cache rather than the durable primary database described in the question. This makes option A the only architecture that aligns with both the NLP functions and the scalable database requirement. (AWS Documentation)

============

Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.

Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.

Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.

The most cost-effective general solution isDynamoDB auto scaling. AWS recommends atarget utilization of 70%for provisioned throughput with auto scaling, which lets DynamoDB adjust capacity up or down based on sustained workload patterns. That improves both read and write performance while controlling cost better than manual scaling. A GSI is only useful for specific alternate query patterns and does not solve overall throughput management by itself. DAX helps only with read-heavy caching, not writes. A custom CloudWatch-plus-Lambda scaling solution adds unnecessary operational work compared to native auto scaling. Because the question asks broadly for read and write performance at the best cost, auto scaling with the recommended 70% target is the strongest answer.

============

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

Amazon Transcribe supports automatic speech recognition (ASR) with speaker diarization (i.e., multiple speaker identification). The transcripts can be stored in Amazon S3 and queried using Amazon Athena, which provides a serverless, pay-as-you-go interactive querying model.

IAM Roles for Service Accounts (IRSA) is the AWS-recommended mechanism to grant fine-grained, pod-level AWS permissions in Amazon EKS. IRSA avoids the security risks of granting permissions to the entire node and aligns with the principle of least privilege.

To implement IRSA, two core components are required. First, an IAM role must be created with a trust policy that references an OIDC identity provider associated with the EKS cluster. This is why Option E is required. The OIDC provider allows AWS STS to authenticate Kubernetes service accounts and issue temporary credentials. Without this trust relationship, service accounts cannot assume IAM roles securely.

Second, the Kubernetes service account must be explicitly annotated with the ARN of the IAM role it is allowed to assume. This is provided by Option D. The annotation creates a direct mapping between a specific service account and a specific IAM role, ensuring granular access control at the pod level.

Option A is incorrect because attaching policies to the node IAM role grants permissions to all pods on the node, which violates least-privilege principles. Option B addresses network-level access but does not control AWS API authorization. Option C is incorrect because IAM roles should not be overloaded with permissions for multiple service accounts, and there is no direct one-to-one mapping between IAM roles and Kubernetes roles in this way.

Therefore, D and E together form the correct and secure IRSA configuration, enabling granular, auditable, and secure access to AWS resources from EKS workloads.

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.

============

Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ( " green " ) of your production environment ( " blue " ) where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.

Reference Extract from AWS Documentation / Study Guide:

" Amazon RDS Blue/Green Deployments enable you to make changes to your database environment safely. You can perform schema updates and feature testing in a fully managed staging environment and switch over with minimal downtime, ensuring the highest availability. "

Source: AWS Certified Solutions Architect – Official Study Guide, Database and Migration section; Amazon RDS Blue/Green Deployments Documentation.

Amazon S3 Transfer Acceleration is designed specifically to speed up long-distance uploads and downloads to an S3 bucket by using globally distributed CloudFront edge locations and optimized AWS network paths. Because users are located worldwide and upload images to one centralized S3 bucket, Transfer Acceleration directly addresses the performance bottleneck. The important detail is that clients must use the S3 Accelerate endpoint; simply enabling Transfer Acceleration but continuing to use the standard endpoint would not provide the acceleration benefit. CloudFront is useful for caching and accelerating content delivery to users, but it is not the best answer for improving global upload speed into a centralized S3 bucket.

Amazon CloudFront is a content delivery network (CDN) service that distributes content with low latency and high transfer speeds. Placing CloudFront in front of the S3 bucket ensures globalusers download content from the nearest edge location, reducing latency significantly.

This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task ' s requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler- Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate- Information on how to run containers in a serverless manner using AWS Fargate.

For logs that are:

Written and processed within a short period (24 hours)

Accessed quickly for compute/analytics

With unknown object count and size

Amazon S3 Standard is the most appropriate and cost-effective. Intelligent-Tiering is designed for data stored for longer periods (typically 30+ days) with changing access patterns and charges a per-object monitoring and automation fee that becomes inefficient for very short-lived objects.

S3 Glacier Deep Archive and S3 One Zone-IA are optimized for long-term archival or infrequently accessed data with retrieval time or availability constraints that are not suitable for nightly active processing.

Aservice control policyis the correct organization-wide control because SCPs define the maximum available permissions across member accounts. AWS documentation also notes that in SCPs you can use the global condition keyaws:PrincipalArnin theConditionelement to identify specific principals, including roles. That makes option D the correct pattern: deny modification of Lambda functions for everyone except the designated security-team role. An SCP that denies all changes with no exception would block the security team too. The Principal element is not the right way to express this exception in an SCP for this use case. Therefore, the correct answer is an SCP with an exception in the Condition clause for the security team role.

Aservice control policyis the right control because SCPs apply guardrails at the AWS Organizations level across member accounts. AWS documentation states that SCPs do not grant permissions; instead, they define the maximum permissions available in member accounts. An explicit deny oniam:CreateUserapplied through an SCP prevents new IAM users from being created in those accounts, while existing IAM users can continue to operate within the permissions they already have, subject to the SCP. IAM policies and permissions boundaries are not the best organization-wide control for this requirement because they are not the same centralized account-level guardrail mechanism that SCPs provide. Therefore, the correct answer is to use anSCP denying iam:CreateUser. (AWS Documentation)

============

Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.

Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.

Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.

Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.

Amazon Timestream Documentation

For the web tier, the company needs multi-AZ scaling and high availability, which is best achieved with anAuto Scaling group across multiple Availability Zones behind an Application Load Balancer. AWS fault-tolerance guidance recommends distributing EC2 instances across multiple AZs and using Auto Scaling to maintain balance and resilience. For the database tier, the question specifically requires reduced MySQL read latency, so anAurora MySQL cluster with a reader instance in a separate AZis the stronger fit. AWS Aurora guidance recommends distributing the primary and reader instances across Availability Zones and notes that Aurora Auto Scaling can add reader instances for handling increased connectivity and workload. Together, B and C deliver scalability, HA, and better read performance.

============

Amazon EFS is a fully managed, scalable file storage service that can be accessed concurrentlyby thousands of EC2 instances. It supports General Purpose performance mode for latency-sensitive use cases and provides high availability and durability across multiple Availability Zones with minimal changes to application code.

Amazon S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves data between frequent and infrequent access tiers based on usage. This tier offers immediate access to all objects, regardless of which tier they are stored in, while optimizing storage costs. S3 Intelligent-Tiering also provides the same high durability, availability, and security as other S3 storage classes and supports access from external resources using standard S3 APIs. Lifecycle policies and Glacier classes are more suitable for archival when infrequent access is predictable, but retrieval from Glacier classes is not immediate and incurs extra charges and delays.

Reference Extract from AWS Documentation / Study Guide:

" S3 Intelligent-Tiering is designed to optimize costs by automatically moving data between two access tiers when access patterns change. Data is always available and immediately accessible, making it ideal for unknown or unpredictable access patterns. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Storage Classes section.

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.