AWS-Solution-Architect-Associate Free Practice Test

- (Topic 2)
A hospital wants to create digital copies for its large collection of historical written records. The hospital will continue to add hundreds of new documents each day. The hospital's data team will scan the documents and will upload the documents to the AWS Cloud.
A solutions architect must implement a solution to analyze the documents, extract the medical information, and store the documents so that an application can run SQL queries on the data. The solution must maximize scalability and operational efficiency.
Which combination of steps should the solutions architect take to meet these requirements? (Select TWO.)

1. A. Write the document information to an Amazon EC2 instance that runs a MySQL database.
2. B. Write the document information to an Amazon S3 bucke
3. C. Use Amazon Athena to query the data.
4. D. Create an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information.
5. E. Create an AWS Lambda function that runs when new documents are uploade
6. F. Use Amazon Rekognition to convert the documents to raw tex
7. G. Use Amazon Transcribe Medical to detect and extract relevant medical information from the text.
8. H. Create an AWS Lambda function that runs when new documents are uploade
9. I. Use Amazon Textract to convert the documents to raw tex
10. J. Use Amazon Comprehend Medical to detect and extract relevant medical information from the text.

Correct Answer: BE
This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide
accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.
Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverage existing AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.
References:
✑ https://aws.amazon.com/s3/
✑ https://aws.amazon.com/athena/
✑ https://aws.amazon.com/lambda/
✑ https://aws.amazon.com/textract/
✑ https://aws.amazon.com/comprehend/medical/

- (Topic 3)
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 group’s capacity.
Which solution will meet these requiements with the LEAST operational overhead?

1. A. Ceate a dynamic scalling policy for the Auto Scaling grou
2. B. Configure the policy to scale based on the CPU utilization metric to 60%.
3. C. Create a scheduled scaling polcy for the Auto Scaling grou
4. D. Set the appropriate desired capacity, minimum capacity, and maximum capacit
5. E. Set the recurrence to weekl
6. F. Set the start time to 30 minute
7. G. Before the batch jobs run.
8. H. Create a predictive scaling policy for the Auto Scaling grou
9. I. Configure the policy to scale based on forecas
10. J. Set the scaling metric to CPU utilizatio
11. K. Set the target value for the metric to 60%. In the Policy, set the instances to pre-launch 30 minutes before the jobs run.
12. L. 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 group’s desired capacity and maximum capacity by 20%.

Correct Answer: C
This option is the most efficient because it uses a predictive scaling policy for the Auto Scaling group, which is a 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 traffic changes. 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 least operational overhead. 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 group’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. Option B is less efficient because 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.

- (Topic 4)
A company's ecommerce website has unpredictable traffic and uses AWS Lambda functions to directly access a private Amazon RDS for PostgreSQL DB instance. The company wants to maintain predictable database performance and ensure that the Lambda invocations do not overload the database with too many connections.
What should a solutions architect do to meet these requirements?

1. A. Point the client driver at an RDS custom endpoin
2. B. Deploy the Lambda functions inside a VPC.
3. C. Point the client driver at an RDS proxy endpoin
4. D. Deploy the Lambda functions inside a VPC.
5. E. Point the client driver at an RDS custom endpoin
6. F. Deploy the Lambda functions outside a VPC.
7. G. Point the client driver at an RDS proxy endpoin
8. H. Deploy the Lambda functions outside a VPC.

Correct Answer: B
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

- (Topic 4)
An ecommerce company is running a seasonal online sale. The company hosts its website on Amazon EC2 instances spanning multiple Availability Zones. The company wants its website to manage sudden traffic increases during the sale.
Which solution will meet these requirements MOST cost-effectively?

1. A. Create an Auto Scaling group that is large enough to handle peak traffic loa
2. B. Stop half of the Amazon EC2 instance
3. C. Configure the Auto Scaling group to use the stopped instances to scale out when traffic increases.
4. D. Create an Auto Scaling group for the websit
5. E. Set the minimum size of the Auto Scaling group so that it can handle high traffic volumes without the need to scale out.
6. F. Use Amazon CIoudFront and Amazon ElastiCache to cache dynamic content with an Auto Scaling group set as the origi
7. G. Configure the Auto Scaling group with the instances necessary to populate CIoudFront and ElastiCach
8. H. Scale in after the cache is fully populated.
9. I. Configure an Auto Scaling group to scale out as traffic increase
10. J. Create a launch template to start new instances from a preconfigured Amazon Machine Image (AMI).

Correct Answer: D
The solution that meets the requirements of high availability, resiliency, and minimal operational effort is to use AWS Transfer for SFTP and an Amazon S3 bucket for storage. This solution allows the company to securely transfer files over SFTP to Amazon S3, which is a durable and scalable object storage service. The company can then modify the application to pull the batch files from Amazon S3 to an Amazon EC2 instance for processing. The EC2 instance can be part of an Auto Scaling group with a scheduled scaling policy to run the batch operation only at night. This way, the company can save costs by scaling down the EC2 instances when they are not needed. The other solutions do not meet all the requirements because they either use Amazon EFS or Amazon EBS for storage, which are more expensive and less scalable than Amazon S3, or they do not use a scheduled scaling policy to optimize the EC2 instances usage. References :=
✑ AWS Transfer for SFTP
✑ Amazon S3
✑ Amazon EC2 Auto Scaling

- (Topic 4)
A company uses an organization in AWS Organizations to manage AWS accounts that contain applications. The company sets up a dedicated monitoring member account in the organization. The company wants to query and visualize observability data across the accounts by using Amazon CloudWatch.
Which solution will meet these requirements?

1. A. Enable CloudWatch cross-account observability for the monitoring accoun
2. B. Deploy an AWS CloudFormation template provided by the monitoring account in each AWS account to share the data with the monitoring account.
3. C. Set up service control policies (SCPs) to provide access to CloudWatch in the monitoring account under the Organizations root organizational unit (OU).
4. D. Configure a new IAM user in the monitoring accoun
5. E. In each AWS account, configure an 1AM policy to have access to query and visualize the CloudWatch data in the accoun
6. F. Attach the new 1AM policy to the new 1AM user.
7. G. Create a new IAM user in the monitoring accoun
8. H. Create cross-account 1AM policies in each AWS accoun
9. I. Attach the 1AM policies to the new IAM user.

Correct Answer: A
This solution meets the requirements because it allows the monitoring account to query and visualize observability data across the accounts by using CloudWatch. CloudWatch cross-account observability is a feature that enables a central monitoring account to view and interact with observability data shared by other accounts. To enable cross-account observability, the monitoring account needs to configure the types of data to be shared (metrics, logs, and traces) and the source accounts to be linked. The source accounts can be specified by account IDs, organization IDs, or organization paths. To share the data with the monitoring account, the source accounts need to deploy an AWS CloudFormation template provided by the monitoring account. This template creates an observability link resource that represents the link between the source account and the monitoring account. The template also creates a sink resource that represents an attachment point in the monitoring account. The source accounts can share their observability data with the sink in the monitoring account. The monitoring account can then use the CloudWatch console, API, or CLI to search, analyze, and correlate the
observability data across the accounts. References: CloudWatch cross-account observability, Setting up CloudWatch cross-account observability, [Observability Access Manager API Reference]