Topic 1: Exam Pool A
A company runs a shopping application that uses Amazon DynamoDB to store customer information. In case of data corruption, a solutions architect needs to design a solution that meets a recovery point objective (RPO) of 15 minutes and a recovery time objective (RTO) of 1 hour.
What should the solutions architect recommend to meet these requirements?
A. Configure DynamoDB global tables. For RPO recovery, point the application to a different AWS Region.
B. Configure DynamoDB point-in-time recovery. For RPO recovery, restore to the desired point in time.
C. Export the DynamoDB data to Amazon S3 Glacier on a daily basis. For RPO recovery, import the data from S3 Glacier to DynamoDB.
D. Schedule Amazon Elastic Block Store (Amazon EBS) snapshots for the DynamoDB table every 15 minutes. For RPO recovery, restore the DynamoDB table by using the EBS snapshot.
A company hosts a data lake on AWS. The data lake consists of data in Amazon S3 and Amazon RDS for PostgreSQL. The company needs a reporting solution that provides data visualization and includes all the data sources within the data lake. Only the company's management team should have full access to all the visualizations. The rest of the company should have only limited access.
Which solution will meet these requirements?
A. Create an analysis in Amazon QuickSight. Connect all the data sources and create new datasets. Publish dashboards to visualize the data. Share the dashboards with the appropriate IAM roles.
B. Create an analysis in Amazon OuickSighl. Connect all the data sources and create new datasets. Publish dashboards to visualize the data. Share the dashboards with the appropriate users and groups.
C. Create an AWS Glue table and crawler for the data in Amazon S3. Create an AWS Glue extract, transform, and load (ETL) job to produce reports. Publish the reports to Amazon S3. Use S3 bucket policies to limit access to the reports.
D. Create an AWS Glue table and crawler for the data in Amazon S3. Use Amazon Athena Federated Query to access data within Amazon RDS for PoslgreSQL. Generate reports by using Amazon Athena. Publish the reports to Amazon S3. Use S3 bucket policies to limit access to the reports.
Explanation: Amazon QuickSight is a data visualization service that allows you to create interactive dashboards and reports from various data sources, including Amazon S3 and Amazon RDS for PostgreSQL. You can connect all the data sources and create new datasets in QuickSight, and then publish dashboards to visualize the data. You can also share the dashboards with the appropriate users and groups, and control their access levels using IAM roles and permissions.
A media company collects and analyzes user activity data on premises. The company wants to migrate this capability to AWS. The user activity data store will continue to grow and will be petabytes in size. The company needs to build a highly available data ingestion solution that facilitates on-demand analytics of existing data and new data with SQL. Which solution will meet these requirements with the LEAST operational overhead?
A. Send activity data to an Amazon Kinesis data stream. Configure the stream to deliver the data to an Amazon S3 bucket.
B. Send activity data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Redshift cluster.
C. Place activity data in an Amazon S3 bucket. Configure Amazon S3 to run an AWS Lambda function on the data as the data arrives in the S3 bucket.
D. Create an ingestion service on Amazon EC2 instances that are spread across multiple Availability Zones. Configure the service to forward data to an Amazon RDS Multi-AZ database.
Explanation: Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This allows you to use your data to gain new insights for your business and customers. The first step to create a data warehouse is to launch a set of nodes, called an Amazon Redshift cluster. After you provision your cluster, you can upload your data set and then perform data analysis queries. Regardless of the size of the data set, Amazon Redshift offers fast query performance using the same SQL-based tools and business intelligence applications that you use today.
A company runs multiple Windows workloads on AWS. The company's employees use Windows file shares that are hosted on two Amazon EC2 instances. The file shares synchronize data between themselves and maintain duplicate copies. The company wants a highly available and durable storage solution that preserves how users currently access the files.
What should a solutions architect do to meet these requirements?
A. Migrate all the data to Amazon S3 Set up IAM authentication for users to access files
B. Set up an Amazon S3 File Gateway. Mount the S3 File Gateway on the existing EC2 Instances.
C. Extend the file share environment to Amazon FSx for Windows File Server with a Multi-AZ configuration. Migrate all the data to FSx for Windows File Server.
D. Extend the file share environment to Amazon Elastic File System (Amazon EFS) with a Multi-AZ configuration. Migrate all the data to Amazon EFS.
Explanation: Amazon FSx for Windows File Server provides fully managed Microsoft Windows file servers, backed by a fully native Windows file system.
A company stores data in Amazon S3. According to regulations, the data must not contain personally identifiable information (Pll). The company recently discovered that S3 buckets have some objects that contain Pll. The company needs to automatically detect Pll in S3 buckets and to notify the company's security team. Which solution will meet these requirements?
A. Use Amazon Macie. Create an Amazon EventBridge rule to filter the SensitiveData event type from Macie findings and to send an Amazon Simple Notification Service (Amazon SNS) notification to the security team.
B. Use Amazon GuardDuty. Create an Amazon EventBridge rule to filter the CRITICAL event type from GuardDuty findings and to send an Amazon Simple Notification Service (Amazon SNS) notification to the security team.
C. Use Amazon Macie. Create an Amazon EventBridge rule to filter the SensitiveData:S30bject/Personal event type from Macie findings and to send an Amazon Simple Queue Service (Amazon SQS) notification to the security team.
D. Use Amazon GuardDuty. Create an Amazon EventBridge rule to filter the CRITICAL event type from GuardDuty findings and to send an Amazon Simple Queue Service (Amazon SQS) notification to the security team.
Explanation: Amazon Macie can also send its findings to Amazon EventBridge, which is a serverless event bus that makes it easy to connect applications using data from a variety of sources. You can create an EventBridge rule that filters the SensitiveData event type from Macie findings and sends an Amazon SNS notification to the security team. Amazon SNS is a fully managed messaging service that enables you to send messages to subscribers or other applications. References: https://docs.aws.amazon.com/macie/latest/userguide/macie-findings.html#macie-findings- eventbridge
An online retail company has more than 50 million active customers and receives more than 25,000 orders each day. The company collects purchase data for customers and stores this data in Amazon S3. Additional customer data is stored in Amazon RDS. The company wants to make all the data available to various teams so that the teams can perform analytics. The solution must provide the ability to manage fine-grained permissions for the data and must minimize operational overhead. Which solution will meet these requirements?
A. Migrate the purchase data to write directly to Amazon RDS. Use RDS access controls to limit access.
B. Schedule an AWS Lambda function to periodically copy data from Amazon RDS to Amazon S3. Create an AWS Glue crawler. Use Amazon Athena to query the data. Use S3 policies to limit access.
C. Create a data lake by using AWS Lake Formation. Create an AWS Glue JDBC connection to Amazon RDS. Register the S3 bucket in Lake Formation. Use Lake Formation access controls to limit access.
D. Create an Amazon Redshift cluster. Schedule an AWS Lambda function to periodically copy data from Amazon S3 and Amazon RDS to Amazon Redshift. Use Amazon Redshift access controls to limit access.
A bicycle sharing company is developing a multi-tier architecture to track the location of its bicycles during peak operating hours The company wants to use these data points in its existing analytics platform A solutions architect must determine the most viable multi-tier option to support this architecture The data points must be accessible from the REST API.
Which action meets these requirements for storing and retrieving location data?
A. Use Amazon Athena with Amazon S3
B. Use Amazon API Gateway with AWS Lambda
C. Use Amazon QuickSight with Amazon Redshift.
D. Use Amazon API Gateway with Amazon Kinesis Data Analytics
An image hosting company uploads its large assets to Amazon S3 Standard buckets The company uses multipart upload in parallel by using S3 APIs and overwrites if the same object is uploaded again. For the first 30 days after upload, the objects will be accessed frequently. The objects will be used less frequently after 30 days, but the access patterns for each object will be inconsistent The company must optimize its S3 storage costs while maintaining high availability and resiliency of stored assets. Which combination of actions should a solutions architect recommend to meet these requirements? (Select TWO.)
A. Move assets to S3 Intelligent-Tiering after 30 days.
B. Configure an S3 Lifecycle policy to clean up incomplete multipart uploads.
C. Configure an S3 Lifecycle policy to clean up expired object delete markers.
D. Move assets to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days
E. Move assets to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 30 days.
Explanation: S3 Intelligent-Tiering is a storage class that automatically moves data to the most cost-effective access tier based on access frequency, without performance impact, retrieval fees, or operational overhead1. It is ideal for data with unknown or changing access patterns, such as the company’s assets. By moving assets to S3 Intelligent-Tiering after 30 days, the company can optimize its storage costs while maintaining high availability and resilience of stored assets. S3 Lifecycle is a feature that enables you to manage your objects so that they are stored cost effectively throughout their lifecycle2. You can create lifecycle rules to define actions that Amazon S3 applies to a group of objects. One of the actions is to abort incomplete multipart uploads that can occur when an upload is interrupted. By configuring an S3 Lifecycle policy to clean up incomplete multipart uploads, the company can reduce its storage costs and avoid paying for parts that are not used. Option C is incorrect because expired object delete markers are automatically deleted by Amazon S3 and do not incur any storage costs3. Therefore, configuring an S3 Lifecycle policy to clean up expired object delete markers will not have any effect on the company’s storage costs. Option D is incorrect because S3 Standard-IA is a storage class for data that is accessed less frequently, but requires rapid access when needed1. It has a lower storage cost than S3 Standard, but it has a higher retrieval cost and a minimum storage duration charge of 30 days. Therefore, moving assets to S3 Standard-IA after 30 days may not optimize the company’s storage costs if the assets are still accessed occasionally. Option E is incorrect because S3 One Zone-IA is a storage class for data that is accessed less frequently, but requires rapid access when needed1. It has a lower storage cost than S3 Standard-IA, but it stores data in only one Availability Zone and has less resilience than other storage classes. It also has a higher retrieval cost and a minimum storage duration charge of 30 days. Therefore, moving assets to S3 One Zone-IA after 30 days may not optimize the company’s storage costs if the assets are still accessed occasionally or require high availability.
A company has a web application for travel ticketing. The application is based on a database that runs in a single data center in North America. The company wants to expand the application to serve a global user base. The company needs to deploy the application to multiple AWS Regions. Average latency must be less than 1 second on updates to the reservation database. The company wants to have separate deployments of its web platform across multiple Regions. However the company must maintain a single primary reservation database that is globally consistent. Which solution should a solutions architect recommend to meet these requirements?
A. Convert the application to use Amazon DynamoDB. Use a global table for the center reservation table. Use the correct Regional endpoint in each Regional deployment.
B. Migrate the database to an Amazon Aurora MySQL database. Deploy Aurora Read Replicas in each Region. Use the correct Regional endpoint in each Regional deployment for access to the database.
C. Migrate the database to an Amazon RDS for MySQL database Deploy MySQL read replicas in each Region. Use the correct Regional endpoint in each Regional deployment for access to the database.
D. Migrate the application to an Amazon Aurora Serverless database. Deploy instances of the database to each Region. Use the correct Regional endpoint in each Regional deployment to access the database. Use AWS Lambda functions to process event streams in each Region to synchronize the databases.
An application runs on an Amazon EC2 instance in a VPC. The application processes logs that are stored in an Amazon S3 bucket. The EC2 instance needs to access the S3 bucket without connectivity to the internet.
Which solution will provide private network connectivity to Amazon S3?
A. Create a gateway VPC endpoint to the S3 bucket.
B. Stream the logs to Amazon CloudWatch Logs. Export the logs to the S3 bucket.
C. Create an instance profile on Amazon EC2 to allow S3 access.
D. Create an Amazon API Gateway API with a private link to access the S3 endpoint.
Explanation: VPC endpoint allows you to connect to AWS services using a private network instead of using the public Internet
A company has data collection sensors at different locations. The data collection sensors stream a high volume of data to the company. The company wants to design a platform on AWS to ingest and process high-volume streaming data. The solution must be scalable and support data collection in near real time. The company must store the data in Amazon S3 for future reporting. Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3.
B. Use AWS Glue to deliver streaming data to Amazon S3.
C. Use AWS Lambda to deliver streaming data and store the data to Amazon S3.
D. Use AWS Database Migration Service (AWS DMS) to deliver streaming data to Amazon S3.
Explanation: To ingest and process high-volume streaming data with the least operational overhead, Amazon Kinesis Data Firehose is a suitable solution. Amazon Kinesis Data Firehose can capture, transform, and deliver streaming data to Amazon S3 or other destinations. Amazon Kinesis Data Firehose can scale automatically to match the throughput of the data and handle any amount of data. Amazon Kinesis Data Firehose is also a fully managed service that does not require any servers to provision or manage.
A company hosts an internal serverless application on AWS by using Amazon API Gateway and AWS Lambda. The company's employees report issues with high latency when they begin using the application each day. The company wants to reduce latency. Which solution will meet these requirements?
A. Increase the API Gateway throttling limit.
B. Set up a scheduled scaling to increase Lambda provisioned concurrency before employees begin to use the application each day.
C. Create an Amazon CloudWatch alarm to initiate a Lambda function as a target for the alarm at the beginning of each day.
D. Increase the Lambda function memory.
Explanation: AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda scales automatically based on the incoming requests, but it may take some time to initialize new instances of your function if there is a sudden increase in demand. This may result in high latency or cold starts for your application. To avoid this, you can use provisioned concurrency, which ensures that your function is initialized and ready to respond at any time. You can also set up a scheduled scaling policy that increases the provisioned concurrency before employees begin to use the application each day, and decreases it when the demand is low. References: https://docs.aws.amazon.com/lambda/latest/dg/configuration-concurrency.html
| Page 18 out of 81 Pages |
| Previous |