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

This solution meets the requirements because it requires the least amount of infrastructure management and guarantees exactly-once delivery for application messaging. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You only pay for the compute time you consume.Lambda scales automatically with the size of your workload. Amazon SQS FIFO queues are designed to ensure that messages are processed exactly once, in the exact order that they are sent. FIFO queues have high availability and deliver messages in a strict first-in, first-out order. You can use Amazon SQS to decouple and scale microservices, distributed systems, and serverless applications. References: AWS Lambda, Amazon SQS FIFO queues

AWS Glue is a serverless data integration service that can crawl, catalog, and prepare data for analysis. AWS Glue can automatically discover the schema and partitioning of the data stored in Apache Parquet format in S3, and create a table in the AWS Glue Data Catalog. Amazon Athena is a serverless interactive query service that can run SQL queries directly on data in S3, without requiring any data loading or transformation. Athena can use the table metadata from the AWS Glue Data Catalog to query the data in S3. By using AWS Glue and Athena, you can analyze the log files in S3 most cost-effectively, as you only pay for the resources consumed by the crawler and the queries, and you do not need to provision or manage any servers or clusters.

To maintain predictable database performance and ensure that the Lambda invocations do not overload the database with too many connections, a solutions architect should point the client driver at an RDS proxy endpoint and deploy the Lambda functions inside a VPC. An RDS proxy is a fully managed database proxy that allows applications to share connections to a database, improving database availability and scalability. By using an RDS proxy, the Lambda functions can reuse existing connections, rather than creating new ones for every invocation, reducing the connection overhead and latency. Deploying the Lambda functions inside a VPC allows them to access the private RDS DB instance securely and efficiently, without exposing it to the public internet. References:

Using Amazon RDS Proxy with AWS Lambda

Configuring a Lambda function to access resources in a VPC

The most suitable solution for the company’s compliance policy is to enable the restricted-ssh AWS Config managed rule and generate an Amazon Simple Notification Service (Amazon SNS) notification when a noncompliant rule is created. This solution has the least operational overheadbecause it uses a predefined rule that is already available in AWS Config, which is a service that enables users to assess, audit, and evaluate the configurations of their AWS resources. The restricted-ssh rule checks whether security groups that are in use have inbound rules that allow SSH from 0.0.0.0/0 addresses, and reports them as noncompliant1. Users can configure the rule to send notifications to an Amazon SNS topic when a noncompliant change occurs, and subscribe to the topic to receive alerts via email, SMS, or other methods2.

The other options are not correct because they either have more operational overhead or do not meet the requirements. Writing an AWS Lambda script that monitors security groups for SSH being open to 0.0.0.0/0 addresses and creates a notification every time it finds one is not correct because it requires custom code development and maintenance, which adds complexity and cost to the solution. Creating an IAM role with permissions to globally open security groups and network ACLs, and creating an Amazon SNS topic to generate a notification every time the role is assumed by a user is not correct because it does not prevent or detect the creation of noncompliant rules by other users or roles, and it does not address the existing rules that may violate the policy. Configuring a service control policy (SCP) that prevents non-administrative users from creating or editing security groups, and creating a notification in the ticketing system when a user requests a rule that needs administrator permissions is not correct because it does not provide an automated solution for the policy enforcement and notification, and it may limit the flexibility and productivity of the users.

This solution meets the following requirements:

It is the least effort, as it does not require any additional AWS services, custom scripts, or data processing steps. Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. You can use Athena to query CloudTrail logs directly from the S3 bucket where they are stored, without any data loading or transformation. You can also use the AWS Management Console, the AWS CLI, or the Athena API to run and manage your queries.

It is effective, as it allows you to filter, aggregate, and join CloudTrail log data using SQL syntax. You can use various SQL functions and operators to specify the criteria for identifyingAccess Denied and Unauthorized errors, such as the error code, the user identity, the event source, the event name, the event time, and the resource ARN. You can also use subqueries, views, and common table expressions to simplify and optimize your queries.

It is flexible, as it allows you to customize and save your queries for future use. You can also export the query results to other formats, such as CSV or JSON, or integrate them with other AWS services, such as Amazon QuickSight, for further analysis and visualization.

The solution that meets the requirements with the most operational efficiency is to configure public subnets in the existing VPC and deploy an MSK cluster in the public subnets. This solution allows the data ingestion solution to be publicly available over the internet without creating a new VPC or deploying a load balancer. The solution also ensures that the data intransit is encrypted by enabling mutual TLS authentication, which requires both the client and the server to present certificates for verification. This solution leverages the public access feature of Amazon MSK, which is available for clusters running Apache Kafka 2.6.0 or later versions1.

The other solutions are not as efficient as the first one because they either create unnecessary resources or do not encrypt the data in transit. Creating a new VPC with public subnets would incur additional costs and complexity for managing network resources and routing. Deploying an ALB or an NLB would also add more costs and latency for the data ingestion solution. Moreover, an ALB or an NLB would not encrypt thedata in transit by itself, unless they are configured with HTTPS listeners and certificates, which would require additional steps and maintenance. Therefore, these solutions are not optimal for the given requirements.

A Network Load Balancer can be assigned one Elastic IP address for each Availability Zone it uses1. This allows the clients to reach the load balancer using a static IP address that can be authorized on their firewalls. An Application Load Balancer cannot be assigned an Elastic IP address2. An A record in an Amazon Route 53 hosted zone pointing to an Elastic IP address would not work because the load balancer would still use its own IP address as the source of the forwarded requests to the web service. An EC2 instance with a public IP address running as a proxy in front of the load balancer would add unnecessary complexity and cost, and would not provide the same scalability and availability as a Network Load Balancer. References: 1: Network Load Balancers - Elastic Load Balancing3, IP address type section2: How to assign Elastic IP to Application Load Balancer in AWS?4, answer section.

The most suitable solution for the company’s requirements is to set the home AWS Region in AWS Migration Hub and use AWS Application Discovery Service to collect data about the on-premises servers. This solution will enable the company to gather usage and configuration data of its on-premises servers and workloads, and plan a migration to AWS.

AWS Migration Hub is a service that simplifies and accelerates migration tracking by aggregating migration status information into a single console. Users can view the discovered servers, group them into applications, and track the migration status of each application from the Migration Hub console in their home Region.The home Region is the AWS Region where users store their migration data, regardless of which Regions they migrate into1.

AWS Application Discovery Service is a service that helps users plan their migration to AWS by collecting usage and configuration data about their on-premises servers and databases. Application Discovery Service is integrated with AWS Migration Hub and supports two methods of performing discovery: agentless discovery and agent-based discovery. Agentless discoverycan be performed by deploying the Application Discovery Service Agentless Collector through VMware vCenter, which collects static configuration data and utilization data for virtual machines (VMs) and databases.Agent-based discovery can be performed by deploying the AWS Application Discovery Agent on each ofthe VMs and physical servers, which collects static configuration data, detailed time-series system-performance information, inbound and outbound network connections, and processes that are running2.

The other options are not correct because they do not meet the requirements or are not relevant for the use case. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Trusted Advisor to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads.AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another, such as from Oracle to PostgreSQL3.AWS Trusted Advisor is a service that provides best practice recommendations for cost optimization, performance, security, fault tolerance, and service limits4. Using the AWS Schema Conversion Tool (AWS SCT) to create the relevant templates and using AWS Database Migration Service (AWS DMS) to collect data about the on-premises servers is not correct because this solution is not suitable for collecting usage and configuration data of on-premises servers and workloads. As mentioned above, AWS SCT is a tool that helps users convert database schemas and code objects from one database engine to another.AWS DMS is a service that helps users migrate relational databases, non-relational databases, and other types of data stores to AWS with minimal downtime5.