Free Practice Questions for Amazon Web Services SAA-C03 Exam

This solution will meet the requirements of minimizing hosting service costs based on demand and providing the streaming content of a movie within 5 minutes of a user purchase. S3 Intelligent-Tiering is a storage class that automatically optimizes storage costs by moving data to the most cost-effective access tier when access patterns change. It is suitable for data with unknown, changing, or unpredictable access patterns, such as newer movies that may have higher demand1. S3 Glacier Flexible Retrieval is a storage class that provides low-cost storage for archive data that is retrieved asynchronously. It offers flexible data retrieval options fromminutes to hours, and free bulk retrievals in 5-12 hours. It is ideal for backup, disaster recovery, and offsite data storage needs2. By using expedited retrieval, the user can access the older movie video file in 1-5 minutes, which meets the requirement of 5 minutes3.

The least outstanding requests (LOR) algorithm is a load balancing algorithm that distributes incoming requests to the target with the fewest outstanding requests. This helps to avoid overloading any single target and improves the overall performance and availability of the web application. The LOR algorithmcan use the RequestCount and TargetResponseTime CloudWatch metrics to determine the number of outstanding requests and the response time of each target. These metrics measure the number of requests processed by each target and the time elapsed after the request leaves the load balancer until a response from the target is received by the load balancer, respectively. By using these metrics, the LOR algorithm can route new requests to the targets that are less busy and more responsive, and avoid sending requests to the targets that are already overloaded or slow. This solution meets the requirements of the company.

CloudWatch cross-account observability is a feature that allows you to monitor and troubleshoot applications that span multiple accounts within a Region. You canseamlessly search, visualize, and analyze your metrics, logs, traces, and Application Insights applications in any of the linked accounts without account boundaries1. To enable CloudWatch cross-account observability, you need to set up one or more AWS accounts as monitoring accounts and link them with multiple source accounts. A monitoring account is a central AWS account that can view and interact with observability data shared by other accounts. A source account is an individual AWS account that shares observability data and resources with one or more monitoring accounts1. To create links between monitoring accounts and source accounts, you can use the CloudWatch console, the AWS CLI, or the AWS API. You can also use AWS Organizations to link accounts in an organization or organizational unit to the monitoring account1. CloudWatch provides a CloudFormation template that you can deploy in each source account to share observability data with the monitoring account. The template creates a sink resource in the monitoring account and an observability link resource in the source account. The template also creates the necessary IAM roles and policies to allow cross-account access to the observability data2. Therefore, the solution that meets the requirements of the question is to enable CloudWatch cross-account observability for the monitoring account and deploy the CloudFormation template provided by the monitoring account in each AWS account to share the data with the monitoring account.

The other options are not valid because:

Service control policies (SCPs) are a type of organization policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing youto ensure your accounts stay within your organization’s access control guidelines3. SCPs do not provide access to CloudWatch in the monitoring account, but rather restrict the actions that users and roles can perform in the source accounts. SCPs are not required toenable CloudWatch cross-account observability, as the CloudFormation template creates the necessary IAM roles and policies for cross-account access2.

IAM users are entities that you create in AWS to represent the people or applications that use them to interact with AWS. IAM users can have permissions to access the resources in your AWS account4. Configuring a new IAM user in the monitoring account and an IAM policy in each AWS account to have access to query and visualize the CloudWatch data in the account is not a valid solution, as it does not enable CloudWatch cross-account observability. This solution would require the IAM user to switch between different accounts to view the observability data, which is not seamless and efficient. Moreover, this solution would not allow the IAM user to search, visualize, and analyze metrics, logs, traces, and Application Insights applications across multiple accounts in a single place1.

Cross-account IAM policies are policies that allow you to delegate access to resources that are in different AWS accounts that you own. You attach a cross-account policy to a user or group in one account, and then specify which accounts theuser or group can access5. Creating a new IAM user in the monitoring account and cross-account IAM policies in each AWS account is not avalid solution, as it does not enable CloudWatch cross-account observability. This solution would also require the IAM user to switch between different accounts to view the observability data, which is not seamless and efficient. Moreover, this solution would not allow the IAM user to search, visualize, and analyze metrics, logs, traces, and Application Insights applications across multiple accounts in a single place1.

This solution meets the requirements most cost-effectively because it leverages the serverless and event-driven capabilities of AWS Lambda and Amazon EventBridge. AWS Lambda allows you to run code without provisioning or managing servers, and you pay only for the compute time you consume. Amazon EventBridge is a serverless event bus service that lets you connect your applications with data from various sources and routes that data to targets such as AWS Lambda. By using Amazon EventBridge, you can create a rule that triggers a Lambda function to run the program when reports are requested, and you can also schedule the rule to run during the last week of each month. This way, you can generate reports in the least amount of time and pay only for the resources you use.

An Application Load Balancer (ALB) is a type of Elastic Load Balancer (ELB) that distributes incoming application traffic across multiple targets, such as EC2 instances, containers, Lambda functions, and IP addresses, in multiple Availability Zones1. An ALB can be internet-facing or internal. An internet-facing ALB has a public DNS name that clients can use to send requests over the internet1. An internal ALB has a private DNS name that clients can use to send requests within a VPC1. This solution meets the requirements of the question because:

It allows external internet traffic to connect to the web server on ports 80 and 443, as the ALB listens for requests on these ports and forwards them to the EC2 instance in the private subnet1.

It does not violate the corporate security mandate, as the EC2 instance is launched in a private subnet and does not have a public IPaddress or a route to an internet gateway2.

It reduces the operational overhead, as the ALB is a fully managed service that handles the tasks of load balancing, health checking, scaling, and security1.

This solution will meet the requirements with the most operational efficiency because:

Amazon Cognito user pools provide a secure and scalable user directory that can store and manage user profiles, and handle user sign-up, sign-in, and access control. User pools can also integrate with social identity providers and enterprise identity providers via SAML or OIDC. User pools can issue JSON Web Tokens (JWTs) that can be used to authenticate users and authorize API requests.

Amazon API Gateway REST APIs enable you to create and deploy APIs that expose your backend services to your clients. REST APIs support multiple authorization mechanisms, including Cognito user pools, IAM, Lambda, and custom authorizers. A Cognito authorizer is a type of Lambda authorizer that uses a Cognito user pool as the identity source. When a client makes a request to a REST API method that is configured with a Cognito authorizer, API Gateway verifies the JWTs that are issued by the user pool and grants access based on the token’s claims and the authorizer’s configuration.

By using Cognito user pools and API Gateway REST APIs with a Cognito authorizer, you can achieve a high level of security, scalability, and performance for your web analytics service. You can also leverage the built-in features of Cognito and API Gateway, such as user management, token validation, caching, throttling, and monitoring, without having to implement them yourself. This reduces the operational overhead and complexity of your solution.

The solution that will meet the requirements is to run the application on Amazon Elastic Container Service (Amazon ECS) as microservices with service auto scaling. This solution will allow the application to be flexible, scalable, and gradually improved, as well as minimize application downtime. By breaking down the monolithic application into microservices, the company can decouple the modules and update them independently, without affecting the whole application. By running the microservices on Amazon ECS, the company can leverage the benefits of containerization, such as portability, efficiency, and isolation. By enabling service auto scaling, the company can adjust the number of containers running for each microservice based on demand, ensuring optimal performance and cost. Amazon ECS also supports various deployment strategies, such as rolling update or blue/green deployment, that can reduce or eliminate downtime during updates.

The other solutions are not as effective as the first one because they either do not meet the requirements or introduce new challenges. Running the application on AWS Lambda as a single function with maximum provisioned concurrency will not meet the requirements, as it will not break down the monolith into microservices, nor will it reduce the complexity of maintenance. Lambda functions are also limited by execution time (15 minutes), memory size (10 GB), and concurrency quotas, which may not be sufficient for the report generation application. Running the application on Amazon EC2 Spot Instances as microservices with a Spot Fleet default allocation strategy will not meet the requirements, as it will introduce the risk of interruptions due to spot price fluctuations. Spot Instances are not guaranteed to be available or stable, and may be reclaimed by AWS at any time with a two-minute warning. This may cause report generation to fail or restart from scratch. Running the application on AWS Elastic Beanstalk as a single application environment with an all-at-once deployment strategy will not meet the requirements, as it will not break down the monolith into microservices, nor will it minimize application downtime. The all-at-once deployment strategy will deploy updates to all instances simultaneously, causing a brief outage for the application.

This answer is the most secure and recommended option for securing the root user of a new AWS account. The root user is the identity that has complete access to all AWS services and resources in the account. It is accessed by signing in with the email address and password that were used to create the account. To protect the root user credentials from unauthorized use, AWS advises the following best practices:

Create IAM users for daily administrative tasks. IAM users are identities that you create in your account that have specific permissions to access AWS resources. You can create individual IAM users for yourself and for others who need access to your account. You can also assign IAM users to IAM groups that have a set of policies that grant permissions to perform common tasks. By using IAM users instead of the root user, you can follow the principle of least privilege and reduce the risk of compromising your account.

Enable multi-factor authentication (MFA) on the root user. MFA is a security feature that requires users to prove their identity by providing two pieces of information: their password and a code from a device that only they have access to. By enabling MFA on the root user, you can add an extra layer of protection to your account and prevent unauthorized access even if your password is compromised.

Limit the tasks you perform with the root user account. You should use the root user only for tasks that require root user credentials, such as changing your account settings, closing your account, or managing consolidated billing. For a complete list of tasks that require root user credentials, see Tasks that require root user credentials. For all other tasks, you should use IAM users or roles that have the appropriate permissions.

AWS Snowball is a petabyte-scale data transport service that uses secure devices to transfer large amounts of data into and out of the AWS Cloud. Snowball addresses common challenges with large-scale data transfers including high network costs, long transfer times, and security concerns. AWS Snowball can transfer up to 80 TB of data per device, and multiple devices can be used in parallel to meet the migration deadline. AWS Snowball is more cost-effective than AWS Snowmobile, which is designed for exabyte-scale data transfers, or Amazon S3 Transfer Acceleration, which is optimized for fast transfers over long distances. Amazon S3 VPC endpoint does not increase the upload speed, but only provides a secure and private connection between the VPC and S3. References: AWS Snowball, AWS Snowmobile, Amazon S3 Transfer Acceleration, Amazon S3 VPC endpoint

This JSON text is an identity-based policy that grants specific permissions. The IAM principals that the solutions architect can attach this policy to are Role and Group. This is because the policy is written in JSON and is an identity-based policy, which can be attached to IAM principals such as users, groups, and roles. Identity-based policies are permissions policies that you attach to IAM identities (users, groups, or roles) and explicitly state what that identity is allowed (or denied) to do1. Identity-based policies are different from resource-based policies, which define the permissions around the specific resource1. Resource-based policies are attached to a resource, such as an Amazon S3 bucket or anAmazon EC2 instance1. Resource-based policies can also specify a principal, which is the entity that is allowed or denied access to the resource1. Organization is not an IAM principal, but a feature of AWS Organizations that allows you to manage multiple AWSaccountscentrally2. Amazon ECS resource and Amazon EC2 resource are not IAM principals, but AWS resources that can have resource-based policies attached to them34.

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices.Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

These options are the best solutions because they allow the company to run the application with Docker containers in the AWS Cloud using a managed service that scales automatically and does not require any infrastructure to manage. By using AWS Fargate, the company can launch and run containers without having to provision, configure, or scale clusters of EC2 instances. Fargate allocates the right amount of compute resources for each container and scales them up or down as needed. By using Amazon ECS or Amazon EKS, the company can choose the container orchestration platform that suits its needs. Amazon ECS is a fully managed service that integrates with other AWS services and simplifies the deployment and management of containers. Amazon EKS is a managed service that runs Kubernetes on AWS and provides compatibility with existing Kubernetes tools and plugins.

C. Provision an Amazon API Gateway API Connect the API to AWS Lambda to run the containers. This option is not feasible because AWS Lambda does not support running Docker containers directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run Docker containers on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the Docker API, which can introduce additional complexity and overhead.

D. Use Amazon Elastic Container Service (Amazon ECS) with Amazon EC2 worker nodes. This option is not optimal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

E. Use Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 worker nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs.

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.

This option is the most operationally efficient way to meet the requirements because it allows the company to monitor and analyze the database login activity across all the accounts in the organization. By publishing the Aurora general logs to a log group in Amazon CloudWatch Logs, the company can enable the logging of the database connections, disconnections, and failed authentication attempts. By exporting the log data to a central Amazon S3 bucket, the company can store the log data in a durable and cost-effective way and use other AWS services or tools to perform further analysis or alerting on the log data. For example, the company can use Amazon Athena to query the log data in Amazon S3, or use Amazon SNS to send notifications based on the log data.

A. Attach service control policies (SCPs) to the root of the organization to identify the failed login attempts. This option is not effective because SCPs are not designed to identify the failed login attempts, but to restrict the actions that the users and roles can perform in the member accounts of the organization. SCPs are applied to the AWS API calls, not to the database login attempts. Moreover, SCPs do not provide any logging or analysis capabilities for the database activity.

B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the member accounts of the organization. This option is not optimal because the Amazon RDS Protection feature in Amazon GuardDuty is not available for Aurora PostgreSQL databases, but only for Amazon RDS for MySQL and Amazon RDS for MariaDB databases. Moreover, the Amazon RDS Protection feature does not monitor the database login attempts, but the network and API activity related to the RDS instances.

D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a central Amazon S3 bucket. This option is not sufficient because AWS CloudTrail does not capture the database login attempts, but only the AWS API calls made by or on behalf of the Aurora PostgreSQLdatabase. For example, AWS CloudTrail can record the events such as creating, modifying, or deleting the database instances, clusters, or snapshots, but not the events such as connecting, disconnecting, or failing to authenticate to the database.

This option is the best solution because it allows the company to migrate the container application to AWS with minimal changes and leverage a managed service to run the Kubernetes cluster and the message queue. By using Amazon EKS, the company can run the container application on a fully managed Kubernetes control plane that is compatible with the existingKubernetes tools and plugins. Amazon EKS handles the provisioning, scaling, patching, and security of the Kubernetes cluster, reducing the operational overhead and complexity. By using Amazon MQ, the company can use a fully managed message broker service that supports AMQP and other popular messaging protocols. Amazon MQ handles the administration, maintenance, and scaling of the message broker, ensuring high availability, durability, and security of the messages.

A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS) Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not optimal because it requires the company to change the container orchestration platform from Kubernetes to ECS, which can introduce additional complexity and risk. Moreover, it requires the company to change the messaging protocol from AMQP to SQS, which can also affect the application logic and performance. Amazon ECS and Amazon SQS are both fully managed services that simplify the deployment and management of containers and messages, but they may not be compatible with the existing application architecture and requirements.

C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ to retrieve the messages. This option is not ideal because it requires the company to manage the EC2 instances that host the container application. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, the company would need to install and maintain the Kubernetes software on the EC2 instances, which can also add complexity and risk. Amazon MQ is a fully managed message broker service that supports AMQP and other popular messaging protocols, but it cannot compensate for the lack of a managed Kubernetes service.

D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option is not feasible because AWS Lambda does not support running container applications directly. Lambda functions are executed in a sandboxed environment that is isolated from other functions and resources. To run container applications on Lambda, the company would need to use a custom runtime or a wrapper library that emulates the container API, which can introduce additional complexity and overhead. Moreover, Lambda functions have limitations in terms of available CPU, memory, and runtime, which may not suit the application needs. Amazon SQS is a fully managed message queue service that supports asynchronous communication, but it does not support AMQP or other messaging protocols.

Amazon EFS is a fully managed, elastic, and scalable file system that can be shared between multiple containers. It provides high availability and fault tolerance by replicating data across multiple Availability Zones. Amazon EFS is compatible with Amazon EKS and AWS Fargate, and can be registered in a StorageClass object on an EKS cluster. Amazon EBS volumes are not supported by AWS Fargate, and cannot be shared between multiple containers without using EBS Multi-Attach, which has limitations and performance implications. EBS Multi-Attach also requires the volumes to be in the same Availability Zone as the worker nodes, which reduces availability and fault tolerance. Synchronizing data between multiple EFS file systems using AWS Lambda is unnecessary, complex, and prone to errors. References:

Amazon EFS Storage Classes

Amazon EKSStorage Classes

Amazon EBS Multi-Attach

To provide access to the SQS queue to the other company without giving up its own account permissions, a solutions architect should create an SQS access policy that provides the other company access to the SQS queue. An SQS access policy is a resource-based policy that defines who can access the queue and what actions they can perform. The policy can specify the AWS account ID of the other company as a principal, and grant permissions for actions such as sqs:ReceiveMessage, sqs:DeleteMessage, and sqs:GetQueueAttributes. This way, the other company can poll the queue using its own credentials, without needing to assume a role or use cross-account access keys. References:

Using identity-based policies (IAM policies) for Amazon SQS

Using custom policies with the Amazon SQS access policy language

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefitfrom faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

SCPs are a type of organization policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization. SCPs help you to ensure your accounts stay within your organization’s access control guidelines1.

To apply an SCP to a specific set of accounts, you need to create an OU for those accounts and attach the SCP to the OU. This way, the SCP affects only the member accounts in that OU and not the other accounts in the organization. If you attach the SCP to the root OU, it will apply to all accounts in the organization, including the production account, which is not the desired outcome. If you attach the SCP to the management account, it will have no effect, as SCPs do not affect users or roles in the management account1.

Therefore, the best solutions to deploy the SCP are B and E. Option B attaches the SCP directly to the three nonproduction accounts, while option E creates a separate OU for the nonproductionaccounts and attaches the SCP to the OU. Both options will achieve the same result of restricting the EC2instance types in the nonproduction accounts, but option E might be more scalable and manageable if there are more accounts or policies to be applied in the future2.

This solution meets the requirements because it allows the company to improve visibility into the infrastructure by using ALB access logging and Amazon Athena. ALB access logging is afeature that captures detailed information about requests sent to the load balancer, such as the client’s IP address, request path, response code, and latency. By enabling ALB access logging to Amazon S3, the company can store the access logs in an S3 bucket as compressed files. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. By creating a table in Amazon Athena for the access logs, the company can query the logs and get results in seconds. This way, the company can better understand the abnormal traffic access patterns across the application.