SAA-C03 Exam Dumps - AWS Certified Solutions Architect - Associate (SAA-C03)

The Application Load Balancer (ALB) supports sticky sessions (session affinity) using application cookies. AWS WAF integrates natively with ALB to provide Layer 7 protection at the same endpoint.

From AWS Documentation:

“You can enable sticky sessions for your Application Load Balancer target groups to ensure that a user’s requests are consistently routed to the same target. AWS WAF integrates with Application Load Balancer to protect your web applications from common exploits.”

(Source: Elastic Load Balancing User Guide & AWS WAF Developer Guide)

Why C and E are correct:

C: ALB operates at Layer 7 (HTTP/HTTPS), supports sticky sessions, and can serve as a public endpoint.

E: AWS WAF can be directly associated with the ALB to inspect traffic and enforce rules.Together, they fulfill both the security and session affinity requirements.

Why others are incorrect:

A: Network Load Balancer doesn’t support session affinity.

B: Gateway Load Balancer is used for virtual appliances, not web applications.

D: Using EIPs bypasses load balancing and WAF integration.

This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3- Information on how to store and manage data in Amazon S3.

Amazon Athena- Documentation on using Amazon Athena for querying data stored in S3 using SQL.

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.

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

To capture traffic metadata to and from network interfaces in a VPC, the correct telemetry source is VPC Flow Logs. Flow Logs can publish records to destinations such as CloudWatch Logs, which is a natural first landing zone for near-real-time log ingestion and centralized retention/permissions. The next requirement is to stream that data to Amazon OpenSearch Service for analysis with minimal operational overhead.

Amazon Data Firehose is purpose-built for fully managed delivery of streaming data to destinations including OpenSearch. Firehose handles buffering, batching, retry behavior, and optional transformations, and it minimizes the need to build and operate custom consumers. Therefore, sending VPC Flow Logs to CloudWatch Logs and then using Firehose to deliver to OpenSearch is an operationally efficient and scalable pattern.

Option A uses Kinesis Data Streams, which would require building/operating consumers (or additional integration components) to read from the stream and load into OpenSearch, adding operational complexity compared with Firehose’s managed delivery. Options C and D are incorrect because CloudTrail records API activity and governance events, not VPC network flow records; VPC Flow Logs do not publish to CloudTrail trails.

Thus, B best meets the requirement: Flow Logs capture the network interface traffic metadata; CloudWatch Logs provides near-real-time log collection; and Firehose provides managed streaming delivery into OpenSearch for indexing and analytics with minimal operational effort.

Amazon Athena enables serverless SQL queries directly on S3 objects, eliminating the need to download or preprocess data. EMR adds operational overhead, and API Gateway/ElastiCache are not data lake query engines.

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn ' t require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

For application access to AWS services from ECS tasks, the correct identity is the ECS task role. AWS separates the task execution role, which is mainly for pulling images and sending logs, from the task role, which is used by the application code running inside the container. Because the application itself needs to read from S3, the least-effort and most secure design is to grant the necessary S3 permissions directly to the task role. Using IAM users and access keys would introduce unnecessary credential management and reduce security. Therefore, assigning the required S3 permissions to the task role is the proper AWS best-practice solution.

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