Free Amazon Web Services SAA-C03 Practice Exam with Questions & Answers | Set: 2

SSE-S3 - is free and uses AWS owned CMKs (CMK = Customer Master Key). The encryption key is owned and managed by AWS, and is shared among many accounts. Its rotation is automatic with time that varies as shown in the table here. The time is not explicitly defined.

SSE-KMS - has two flavors:

AWS managed CMK. This is free CMK generated only for your account. You can only view it policies and audit usage, but not manage it. Rotation is automatic - once per 1095 days (3 years),

Customer managed CMK. This uses your own key that you create and can manage. Rotation is not enabled by default. But if you enable it, it will be automatically rotated every 1 year. This variant can also use an imported key material by you. If you create such key with an imported material, there is no automated rotation. Only manual rotation.

SSE-C - customer provided key. The encryption key is fully managed by you outside of AWS. AWS will not rotate it.

This solution meets the requirements of moving data to an Amazon S3 bucket, encrypting the data when it is stored in the S3 bucket, and automatically rotating the encryption key every year with the least operational overhead. AWS Key Management Service (AWS KMS) is a service that enables you to create and manage encryption keys for your data. A customer managed key is a symmetric encryption key that you create and manage in AWS KMS. You can enable automatic key rotation for a customer managed key, which means that AWS KMS generates new cryptographic material for the key every year. You can set the S3 bucket’s default encryption behavior to use the customer managed KMS key, which means that any object that is uploaded to the bucket without specifying an encryption method will be encrypted with that key.

Option A is incorrect because using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not allow you to control or manage the encryption keys. SSE-S3 uses a unique key for each object, and encrypts that key with a master key that is regularly rotated by S3. However, you cannot enable or disable key rotation for SSE-S3 keys, or specify the rotation interval. Option C is incorrect because manually rotating the KMS key every year can increase the operational overhead and complexity, and it may not meet the requirement of rotating the key every year if you forget or delay the rotationprocess. Option D is incorrect because encrypting the data with customer key material before moving the data to the S3 bucket can increase the operational overhead and complexity, and it may not provide consistent encryption for all objects in the bucket. Creating a KMS key without key material and importing the customer key material into the KMS key can enable you to use your own source of random bits to generate your KMS keys, but it does not support automatic key rotation.

When designing a microservice architecture where each microservice interacts with different AWS services, it's essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individualIAM roleswith policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAMincludes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn't adhere to the principle of least privilege.

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

This answer is correct because it meets the requirements of running the ML models as independent microservices that can handle irregular and unpredictable usage patterns. By directing the requests from the API into an Amazon SQS queue, the company can decouple therequest processing from the model execution, and ensure that no requests are lost due to spikes in demand. By deploying the models as Amazon ECS services that read from the queue, the company can leverage containers to isolate and package each model as a microservice, and fetch the model data from S3 at startup. By enabling AWS Auto Scaling on Amazon ECS for both the cluster and copies of the service based on the queue size, the company can automatically scale up or down the number of EC2 instances in the cluster and the number of tasks in each service to match the demand and optimize performance.

1. database shows no signs of being overloaded. CPU, memory, and disk access metrics are all low==>A and C out. We cannot only add nodes instance or add read replica, because database workload is totally fine, very low. 2. "least operational overhead"==>B out, because b need to configure lambda. 3. ROS proxy: Shares infrequently used connections; High availability with failover; Drives increased efficiency==>proxy can leverage failover to redirect traffic from timeout rds instance to healthy rds instance. So D is right.

https://aws.amazon.com/premiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

 it allows the company to store sensitive customer information in a managed AWS service and give customers the ability to complete purchase transactions on the website. By using AWS Key Management Service (AWS KMS) client-side encryption, the company can encrypt the data before sending it to Amazon RDS for MySQL. This ensures that sensitive customer data is protected, even from database administrators, as only the application has access to the encryption keys. References:

Using Encryption with Amazon RDS for MySQL

Encrypting Amazon RDS Resources

This solution meets the requirements of moving the files automatically, running Lambda functions on the copied data, and sending the data files to SageMaker Pipelines with the least operational overhead. S3 replication can copy the files from the initial S3 bucket to the analysis S3 bucket as they arrive. The analysis S3 bucket can send event notifications to Amazon EventBridge (Amazon CloudWatch Events) when an object is created.EventBridge can trigger Lambda and SageMaker Pipelines as targets for the ObjectCreated rule. Lambda can run pattern-matching code on the copied data, and SageMaker Pipelines can execute a pipeline with the data files.

Option A is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option B is incorrect because creating a Lambda function to copy the files to the analysis S3 bucket is not necessary when S3 replication can do that automatically. It also adds operational overhead to manage the Lambda function. Option C is incorrect because using S3 event notification with multiple destinations can result in throttling or delivery failures if there are too many events.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

To protect data in an S3 bucket from accidental deletion, versioning should be enabled, which enables you to preserve, retrieve, and restore every version of every object in an S3 bucket. Additionally, enabling MFA (multi-factor authentication) Delete on the S3 bucket adds an extra layer of protection by requiring an authentication token in addition to the user's access keys to delete objects in the bucket.

EC2 instance Savings Plan saves 72% while Compute Savings Plans saves 66%. But according to link, it says "Compute Savings Plans provide the most flexibility and help to reduce your costs by up to 66%. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, and also apply to Fargate and Lambda usage." EC2 instance Savings Plans are not applied to Fargate or Lambda

Amazon RDS for Microsoft SQL Server is a fully managed service that offers SQL Server 2014, 2016, 2017, and 2019 editions while offloading database administration tasks such as backups, patching, and scaling. Amazon RDS supports read replicas, which are read-only copies of the primary database that can be used for reporting purposes without affecting the performance of the online application. This solution will meet the requirements with the least operational overhead, as it does not require any code changes or manual intervention.

An Application Load Balancer is a type of Elastic Load Balancer that operates at the application layer (layer 7) of the OSI model. It can distribute incoming traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, and Lambda functions. It can also route requests based on the content of the request, such as the host name, path, or query parameters1.

The AWS Load Balancer Controller is a controller that helps you manage Elastic Load Balancers for your Kubernetes cluster. It can provision Application Load Balancers or Network Load Balancers when you create Kubernetes Ingress or Service resources2.

By using the AWS Load Balancer Controller to provision an Application Load Balancer for your Amazon EKS cluster, you can achieve the following benefits:

You can route incoming requests to the appropriate microservices based on the rules you define in your Ingress resource. For example, you can route requests with different host names or paths to different microservices that handle customers and orders2.

You can improve the performance and availability of your container applications by distributing the load across multiple targets and enabling health checks and automatic scaling1.

You can reduce the cost and complexity of managing your load balancers by using a single controller that integrates with Amazon EKS and Kubernetes. You do not need to manually create or configure load balancers or update them when your cluster changes2.

DynamoDB Accelerator (DAX) is a fully managed, highly available caching service built for Amazon DynamoDB. DAX delivers up to a 10 times performance improvement—from milliseconds to microseconds—even at millions of requests per second. DAX does all the heavy lifting required to add in-memory acceleration to your DynamoDB tables, without requiring developers to manage cache invalidation, data population, or cluster management. Now you can focus on building great applications for your customers without worrying about performance at scale. You do not need to modify application logic because DAX is compatible with existing DynamoDB API calls. This solution will meet the requirements with the least operational overhead, as it does not require any code development or manual intervention.

This approach will provide both high availability and scalability for the website platform. By moving the database to Amazon Aurora with a read replica in another availability zone, it will provide a failover option for the database. The use of an Application Load Balancer and an Auto Scaling group across two availability zones allows for automatic scaling of the website to meet increased user demand. Additionally, creating an AMI from the original EC2 instance allows for easy replication of the instance in case of failure.

S3 Object Lock enables a write-once-read-many (WORM) model for objects stored in Amazon S3. It can help prevent objects from being deleted or overwritten for a fixed amount of time or indefinitely1. S3 Object Lock has two retention modes: governance mode and compliance mode. Compliance mode provides the highest level of protection and prevents any user, including the root user, from deleting or modifying an object version until the retention period expires. To use S3 Object Lock, a new bucket with Object Lock enabled must be created, and a default retention period can be optionally configured for objects placed in the bucket2. To bring existing objects into compliance, they must be recopied into the bucket with a retention period specified.

Option A is incorrect because S3 Versioning and S3 Lifecycle do not provide WORM protection for objects. Moreover, MFA delete only applies to deleting object versions, not modifying them.

Option B is incorrect because governance mode allows users with special permissions to override or remove the retention settings or delete the object if necessary. This does not meet the legal requirement of retaining all data for 7 years.

Option D is incorrect because S3 Batch Operations cannot be used to apply compliance mode retention periods to existing objects. S3 Batch Operations can only apply governance mode retention periods or legal holds. Reference URL: 2: https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock-console.html 3:https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-class-intro.html#sc-dynamic-data-access 4:https://docs.aws.amazon.com/AmazonS3/latest/userguide/transfer-acceleration.html 1: https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock.html : https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock-overview.html : https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lock-managing.html : https://aws.amazon.com/blogs/storage/managing-amazon-s3-access-with-vpc-endpoints-and-s3-access-points/

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

https://aws.amazon.com/blogs/database/dynamodb-streams-use-cases-and-design-patterns/

https://aws.amazon.com/premiumsupport/knowledge-center/back-up-dynamodb-s3/

This answer is correct because it meets the requirements of displaying a top-10 scoreboard in near-real time and offering the ability to stop and restore the game while preserving the current scores. Amazon ElastiCache for Redis is a blazing fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications. You can use Amazon ElastiCache for Redis to set up an ElastiCache for Redis cluster to compute and cache the scores for the web application to display. You can use Redis data structures such as sorted sets and hashes to store and rank the scores of the players, and use Redis commands such as ZRANGE and ZADD to retrieve and update the scores efficiently. You can also use Redis persistence features such as snapshots and append-only files (AOF) to enable point-in-time recovery of your data, which can help you stop and restore the game while preserving the current scores.

The most efficient solution for automatically starting and stopping EC2 instances and DB instances on a schedule while minimizing cost and infrastructure maintenance is to create an AWS Lambda function and configure Amazon EventBridge to invoke the function on a schedule.

Option A, scaling EC2 instances by using elastic resize and scaling DB instances to zero outside of business hours, is not feasible as DB instances cannot be scaled to zero.

Option B, exploring AWS Marketplace for partner solutions, may be an option, but it may not be the most efficient solution and could potentially add additional costs.

Option C, launching another EC2 instance and configuring a crontab schedule to run shell scripts that will start and stop the existing EC2 instances and DB instances on a schedule, adds unnecessary infrastructure and maintenance.