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Amazon AWS Certified Solutions Architect - Professional Sample Questions (Q113-Q118):

NEW QUESTION # 113
Which of the following statements is correct about AWS Direct Connect?

A. An AWS Direct Connect location provides access to Amazon Web Services in the region it is associated with.
B. To use AWS Direct Connect, your network must be colocated with a new AWS Direct Connect location.
C. AWS Direct Connect links your internal network to an AWS Direct Connect location over a standard 50 gigabit Ethernet cable.
D. Connections to AWS Direct Connect require double clad fiber for 1 gigabit Ethernet with Auto Negotiation enabled for the port.

Answer: A

Explanation:
AWS Direct Connect links your internal network to an AWS Direct Connect location over a standard 1 gigabit or 10 gigabit Ethernet fiber-optic cable. An AWS Direct Connect location provides access to Amazon Web Services in the region it is associated with, as well as access to other US regions. To use AWS Direct Connect, your network is colocated with an existing AWS Direct Connect location. Connections to AWS Direct Connect require single mode fiber,
1000BASE-LX (1310nm) for 1 gigabit Ethernet, or 10GBASE-LR (1310nm) for 10 gigabit Ethernet. Auto Negotiation for the port must be disabled.
http://docs.aws.amazon.com/directconnect/latest/UserGuide/Welcome.html

NEW QUESTION # 114
A manufacturing company is building an inspection solution for its factory. The company has IP cameras at the end of each assembly line. The company has used Amazon SageMaker to train a machine learning (ML) model to identify common defects from still images.
The company wants to provide local feedback to factory workers when a defect is detected. The company must be able to provide this feedback even if the factory's internet connectivity is down. The company has a local Linux server that hosts an API that provides local feedback to the workers.
How should the company deploy the ML model to meet these requirements?

A. Set up an Amazon Kinesis video stream from each IP camera to AWS. Use Amazon EC2 instances to take still images of the streams. Upload the images to an Amazon S3 bucket. Deploy a SageMaker endpoint with the ML model. Invoke an AWS Lambda function to call the inference endpoint when new images are uploaded. Configure the Lambda function to call the local API when a defect is detected.
B. Order an AWS Snowball device. Deploy a SageMaker endpoint the ML model and an Amazon EC2 instance on the Snowball device. Take still images from the cameras. Run inference from the EC2 instance. Configure the instance to call the local API when a defect is detected.
C. Deploy Amazon Monitron devices on each IP camera. Deploy an Amazon Monitron Gateway on premises. Deploy the ML model to the Amazon Monitron devices. Use Amazon Monitron health state alarms to call the local API from an AWS Lambda function when a defect is detected.
D. Deploy AWS IoT Greengrass on the local server. Deploy the ML model to the Greengrass server. Create a Greengrass component to take still images from the cameras and run inference. Configure the component to call the local API when a defect is detected.

Answer: D

Explanation:
The company should use AWS IoT Greengrass to deploy the ML model to the local server and provide local feedback to the factory workers. AWS IoT Greengrass is a service that extends AWS cloud capabilities to local devices, allowing them to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks1. AWS IoT Greengrass also supports ML inference at the edge, enabling devices to run ML models locally without requiring internet connectivity2.
The other options are not correct because:
Setting up an Amazon Kinesis video stream from each IP camera to AWS would not work if the factory's internet connectivity is down. It would also incur unnecessary costs and latency to stream video data to the cloud and back.
Ordering an AWS Snowball device would not be a scalable or cost-effective solution for deploying the ML model. AWS Snowball is a service that provides physical devices for data transfer and edge computing, but it is not designed for continuous operation or frequent updates3.
Deploying Amazon Monitron devices on each IP camera would not work because Amazon Monitron is a service that monitors the condition and performance of industrial equipment using sensors and machine learning, not cameras4.
References:
https://aws.amazon.com/greengrass/
https://docs.aws.amazon.com/greengrass/v2/developerguide/use-machine-learning-inference.html
https://aws.amazon.com/snowball/
https://aws.amazon.com/monitron/

NEW QUESTION # 115
A company's solutions architect is reviewing a new internally developed application in a sandbox AWS account The application uses an AWS Auto Scaling group of Amazon EC2 instances that have an IAM instance profile attached Part of the application logic creates and accesses secrets from AWS Secrets Manager The company has an AWS Lambda function that calls the application API to test the functionality The company also has created an AWS CloudTrail trail in the account The application's developer has attached the SecretsManagerReadWnte AWS managed IAM policy to an IAM role The IAM role is associated with the instance profile that is attached to the EC2 instances The solutions architect has invoked the Lambda function for testing The solutions architect must replace the SecretsManagerReadWnte policy with a new policy that provides least privilege access to the Secrets Manager actions that the application requires What is the MOST operationally efficient solution that meets these requirements?

A. Create an analyzer in AWS Identity and Access Management Access Analyzer Use the IAM role's Access Advisor findings to create a new IAM policy Use the newly created IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role
B. Use the IAM policy simulator to generate an IAM policy for the IAM role Use the newly generated IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role
C. Use the aws cloudtrail lookup-events AWS CLI command to filter and export CloudTrail events that are related to Secrets Manager Use a new IAM policy that contains the actions from CloudTrail to replace the SecretsManagerReadWnte policy that is attached to the IAM role
D. Generate a policy based on CloudTrail events for the IAM role Use the generated policy output to create a new IAM policy Use the newly generated IAM policy to replace the SecretsManagerReadWnte policy that is attached to the IAM role

Answer: A

Explanation:
The IAM policy simulator will generate a policy that contains only the necessary permissions for the application to access Secrets Manager, providing the least privilege necessary to get the job done. This is the most efficient solution as it will not require additional steps such as analyzing CloudTrail events or manually creating and testing an IAM policy.
You can use the IAM policy simulator to generate an IAM policy for an IAM role by specifying the role and the API actions and resources that the application or service requires. The simulator will then generate an IAM policy that grants the least privilege access to those actions and resources.
Once you have generated an IAM policy using the simulator, you can replace the existing SecretsManagerReadWnte policy that is attached to the IAM role with the newly generated policy. This will ensure that the application or service has the least privilege access to the Secrets Manager actions that it requires.
You can access the IAM policy simulator through the IAM console, AWS CLI, and AWS SDKs. Here is the link for more information:
https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_simulator.html

NEW QUESTION # 116
A company has deployed an application to multiple environments in AWS, including production and testing.
The company has separate accounts for production and testing, and users are allowed to create additional application users for team members or services, as needed. The Security team has asked the Operations team for better isolation between production and testing with centralized controls on security credentials and improved management of permissions between environments.
Which of the following options would MOST securely accomplish this goal?

A. Create all user accounts in the production account. Create roles for access in the production account and testing accounts. Grant cross-account access from the production account to the testing account.
B. Create a new AWS account to hold user and service accounts, such as an identity account. Create users and groups in the identity account. Create roles with appropriate permissions in the production and testing accounts. Add the identity account to the trust policies for the roles.
C. Modify permissions in the production and testing accounts to limit creating new IAM users to members of the Operations team. Set a strong IAM password policy on each account. Create new IAM users and groups in each account to limit developer access to just the services required to complete their job function.
D. Create a script that runs on each account that checks user accounts for adherence to a security policy.
Disable any user or service accounts that do not comply.

Answer: C

NEW QUESTION # 117
A photo-sharing and publishing company receives 10,000 to 150,000 images daily. The company receives the images from multiple suppliers and users registered with the service. The company is moving to AWS and wants to enrich the existing metadata by adding data using Amazon Rekognition.
The following is an example of the additional data:

As part of the cloud migration program, the company uploaded existing image data to Amazon S3 and told users to upload images directly to Amazon S3.
What should the Solutions Architect do to support these requirements?

A. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon RDS MySQL Multi-AZ to store the metadata information and use Lambda to create an index. Use a web front-end with search capabilities backed by Lambda.
B. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon DynamoDB to store the metadata and Amazon ES to create an index. Use a web front-end to provide search capabilities backed by Amazon ES.
C. Use Amazon Kinesis to stream data based on an S3 event. Use an application running in Amazon EC2 to extract metadata from the images. Then store the data on Amazon DynamoDB and Amazon CloudSearch and create an index. Use a web front-end with search capabilities backed by CloudSearch.
D. Start an Amazon SQS queue based on S3 event notifications. Then have Amazon SQS send the metadata information to Amazon DynamoDB. An application running on Amazon EC2 extracts data from Amazon Rekognition using the API and adds data to DynamoDB and Amazon ES. Use a web front-end to provide search capabilities backed by Amazon ES.

Answer: B

Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition

NEW QUESTION # 118
......

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