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

The requirement is to provide granular, scalable access to thousands of tables and columns in a data lake across many users and departments, with the least operational overhead.

AWS Lake Formation supports tag-based access control (TBAC) using LF-tags (Lake Formation tags), which allows you to assign tags to tables, columns, and databases. You can then define permissions on resources by specifying tags rather than managing permissions for individual resources. This approach is highly scalable and efficient when dealing with a growing number of tables and columns. By associating IAM roles to departments and granting access based on LF-tags, you dramatically reduce the operational burden as new tables or columns are added; you only need to assign the appropriate tags.

Amazon Athena can directly query data in S3 with Lake Formation providing fine-grained access control.

AWS Documentation Extract:

"With LF-tag-based access control, you can grant permissions to resources based on tags, making it easy to manage access at scale, especially in environments with large and dynamic numbers of resources."

"LF-tags provide a scalable way to manage permissions for large numbers of resources without having to define permissions individually for each table or column."

(Source: AWS Lake Formation documentation, Access Control, Tag-Based Access Control)

Other options:

A: Would require managing explicit permissions for each table and column as the environment grows, increasing operational overhead.

B & D: Involve significant duplication of resources (clusters) and do not scale as efficiently as a centralized data lake with tag-based access.

For maximum resiliency and fault tolerance in a mission-critical scenario, AWS recommends redundant Direct Connect connections from multiple data centers to multiple AWS Direct Connect locations.

This protects against:

Data center failure

Device failure

Location outages

“For workloads that require high availability, we recommend that you use multiple Direct Connect connections at multiple Direct Connect locations.”

— AWS Direct Connect Resiliency Recommendations

Option C follows AWS maximum resiliency model.

Comprehensive and Detailed Explanation:

For workloads where tasks are independent and can be processed in parallel, and where minimizing management overhead is a priority, a serverless architecture using AWS Lambda is ideal.

AWS Lambda allows you to run code without provisioning or managing servers. It automatically scales your application by running code in response to each trigger.

Parallel Processing: Lambda functions can process multiple tasks concurrently, making it suitable for parallel workloads.

Automatic Scaling: Lambda automatically scales by running code in response to each event, scaling precisely with the size of the workload.

Minimal Management Overhead: With Lambda, there's no need to manage the underlying infrastructure, reducing operational complexity.Wikipedia

To improve read performance for a MySQL-based RDS database under load, you can:

Use Read Replicas: Amazon RDS supports MySQL read replicas, which help offload read operations from the primary database, improving performance during high traffic.

Use ElastiCache (Memcached): Adding an in-memory cache layer using Amazon ElastiCache reduces the load on the RDS instance by serving frequent queries directly from memory, especially when data is not updated often.

Option A (AWS WAF) is for web security, not database performance. Option D relates to storage optimization, not query latency. Option E would require re-architecting from relational to NoSQL, which is unnecessary and disruptive.

AWS Systems Manager Session Manager allows secure, auditable SSH-like access to EC2 instances without the need to open SSH ports or manage bastion hosts. For this to work in a private subnet, an interface VPC endpoint is required (not a gateway endpoint).

The EC2 instances must have the AmazonSSMManagedInstanceCore policy attached to their IAM roles to allow Systems Manager operations.

With Session Manager, all session activity can be logged centrally to Amazon CloudWatch Logs or S3, satisfying the audit requirement and improving operational efficiency over manual SSH and bastion configurations.

Since the workload is interruptible and can resume after restarts, Amazon EC2 Spot Instances are the most cost-effective option, offering savings of up to 90% compared to On-Demand. Running the batch workload on AWS Batch with Spot Instances allows automatic job queue management, interruption handling, and scheduling. Option A and C reduce cost but still rely on reserved or committed pricing for On-Demand capacity. Option B (Capacity Reservations) increases cost instead of reducing it. Therefore, the most cost-optimized solution is D — AWS Batch with EC2 Spot Instances.

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables