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

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Question # 4

A company is developing an ecommerce application that will consist of a load-balanced front end, a container-based application, and a relational database. A solutions architect needs to create a highly available solution that operates with as little manual intervention as possible.

Which solutions meet these requirements? (Select TWO.)

Create an Amazon RDS DB instance in Multi-AZ mode.

Create an Amazon RDS DB instance and one or more replicas in another Availability Zone.

Create an Amazon EC2 in stance-based Docker cluster to handle the dynamic application load.

Create an Amazon Elastic Container Service (Amazon ECS) cluster with a Fargate launch type to handle the dynamic application load.

Create an Amazon Elastic Container Service (Amazon ECS) cluster with an Amazon EC2 launch type to handle the dynamic application load.

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Question # 5

A company is implementing a new business application. The application runs on two Amazon EC2 instances and uses an Amazon S3 bucket for document storage. A solutions architect needs to ensure that the EC2 instances can access the S3 bucket.

What should the solutions architect do to meet this requirement?

Create an IAM role that grants access to the S3 bucket. Attach the role to the EC2 instances.

Create an IAM policy that grants access to the S3 bucket. Attach the policy to the EC2 instances.

Create an IAM group that grants access to the S3 bucket. Attach the group to the EC2 instances.

Create an IAM user that grants access to the S3 bucket. Attach the user account to the EC2 instances.

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Question # 6

A company uses AWS Organizations. The company wants to operate some of its AWS accounts with different budgets. The company wants to receive alerts and automatically prevent provisioning of additional resources on AWS accounts when the allocated budget threshold is met during a specific period.

Which combination of solutions will meet these requirements? (Select THREE.)

Use AWS Budgets to create a budget. Set the budget amount under the Cost and Usage Reports section of the required AWS accounts.

Use AWS Budgets to create a budget. Set the budget amount under the Billing dashboards of the required AWS accounts.

Create an IAM user for AWS Budgets to run budget actions with the required permissions.

Create an IAM role for AWS Budgets to run budget actions with the required permissions.

Add an alert to notify the company when each account meets its budget threshold. Add a budget action that selects the IAM identity created with the appropriate config rule to prevent provisioning of additional resources.

Add an alert to notify the company when each account meets its budget threshold. Add a budget action that selects the IAM identity created with the appropriate service control policy (SCP) to prevent provisioning of additional resources.

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Question # 7

A transaction processing company has weekly scripted batch jobs that run on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group. The number of transactions can vary but the beseline CPU utilization that is noted on each run is at least 60%. The company needs to provision the capacity 30 minutes before the jobs run.

Currently engineering complete this task by manually modifying the Auto Scaling group parameters. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. The company needs an automated way to modify the Auto Scaling groupâ€™s capacity.

Which solution will meet these requiements with the LEAST operational overhead?

Ceate a dynamic scalling policy for the Auto Scaling group. Configure the policy to scale based on the CPU utilization metric to 60%.

Create a scheduled scaling polcy for the Auto Scaling group. Set the appropriate desired capacity, minimum capacity, and maximum capacity. Set the recurrence to weekly. Set the start time to 30 minutes. Before the batch jobs run.

Create a predictive scaling policy for the Auto Scaling group. Configure the policy to scale based on forecast. Set the scaling metric to CPU utilization. Set the target value for the metric to 60%. In the Policy, set the instances to pre-launch 30 minutes before the jobs run.

Create an Amazon EventBridge event to invoke an AWS Lamda function when the CPU utilization metric value for the Auto Scaling group reaches 60%. Configure the Lambda function to increase the Auto Scaling groupâ€™s desired capacity and maximum capacity by 20%.

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Answer:

Explanation:

This option is the most efficient because it uses a predictive scaling policy for the Auto Scaling group, which isa type of scaling policy that uses machine learning to predict capacity requirements based on historical data from CloudWatch1. It also configures the policy to scale based on forecast, which enables the Auto Scaling group to adjust its capacity in advance of trafficchanges. It also sets the scaling metric to CPU utilization and the target value for the metric to 60%, which aligns with the baseline CPU utilization that is noted on each run. It also sets the instances to pre-launch 30 minutes before the jobs run, which ensures that enough capacity is provisioned before the weekly scripted batch jobs start. This solution meets the requirement of provisioning the capacity 30 minutes before the jobs run with the leastoperational overhead.Â Option A is less efficient because it uses a dynamic scaling policy for the Auto Scaling group, which is a type of scaling policy that adjusts your Auto Scaling groupâ€™s capacity in response to changing demand2. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic.Â Option B is less efficientbecause it uses a scheduled scaling policy for the Auto Scaling group, which is a type of scaling policy that lets you scale your Auto Scaling group based on a schedule that you create3. However, this does not provide a way to scale based on forecast or CPU utilization, as it only scales based on predefined metrics and policies. Option D is less efficient because it uses an Amazon EventBridge event to invoke an AWS Lambda function when the CPU utilization metric value for the Auto Scaling group reaches 60%, which is a way to trigger serverless functions based on events. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic.

Question # 8

Question:

A company uses AWS Organizations to manage multiple AWS accounts. Each department in the company has its own AWS account. A security team needs to implement centralized governance and control to enforce security best practices across all accounts. The team wants to have control over which AWS services each account can use. The team needs to restrict access to sensitive resources based on IP addresses or geographic regions. The root user must be protected with multi-factor authentication (MFA) across all accounts.

Options:

Use AWS Identity and Access Management (IAM) to manage IAM users and IAM roles in each account. Implement MFA for the root user in each account. Enforce service restrictions by using AWS managed prefix lists.

Use AWS Control Tower to establish a multi-account environment. Use service control policies (SCPs) to enforce service restrictions in AWS Organizations. Configure MFA for the root user across all accounts.

Use AWS Systems Manager to enforce service restrictions across multiple accounts. Use IAM policies to enforce MFA for the root user across all accounts.

Use AWS IAM Identity Center to manage user access and to enforce service restrictions by using permissions boundaries in each account.

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