Question # 1
The customers of a finance company request appointments with financial advisors by sending text messages. A web application that runs on Amazon EC2 instances accepts the appointment requests. The text messages are published to an Amazon Simple Queue Service (Amazon SQS) queue through the web application. Another application that runs on EC2 instances then sends meeting invitations and meeting confirmation email messages to the customers. After successful scheduling, this application stores the meeting information in an Amazon DynamoDB database.
As the company expands, customers report that their meeting invitations are taking longer to arrive.
What should a solutions architect recommend to resolve this issue? | A. Add a DynamoDB Accelerator (DAX) cluster in front of the DynamoDB database.
| B. Add an Amazon API Gateway API in front of the web application that accepts the appointment requests.
| C. Add an Amazon CloudFront distribution. Set the origin as the web application that accepts the appointment requests.
| D. Add an Auto Scaling group for the application that sends meeting invitations. Configure the Auto Scaling group to scale based on the depth of the SQS queue. |
D. Add an Auto Scaling group for the application that sends meeting invitations. Configure the Auto Scaling group to scale based on the depth of the SQS queue.
Explanation: To resolve the issue of longer delivery times for meeting invitations, the
solutions architect can recommend adding an Auto Scaling group for the application that sends meeting invitations and configuring the Auto Scaling group to scale based on the depth of the SQS queue. This will allow the application to scale up as the number of appointment requests increases, improving the performance and delivery times of the meeting invitations.
Question # 2
A company's HTTP application is behind a Network Load Balancer (NLB). The NLB's target group is configured to use an Amazon EC2 Auto Scaling group with multiple EC2 instances that run the web service.
The company notices that the NLB is not detecting HTTP errors for the application. These errors require a manual restart of the EC2 instances that run the web service. The company needs to improve the application's availability without writing custom scripts or code.
What should a solutions architect do to meet these requirements? | A. Enable HTTP health checks on the NLB. supplying the URL of the company's application. | B. Add a cron job to the EC2 instances to check the local application's logs once each minute. If HTTP errors are detected, the application will restart. | C. Replace the NLB with an Application Load Balancer. Enable HTTP health checks by supplying the URL of the company's application. Configure an Auto Scaling action to replace unhealthy instances. | D. Create an Amazon Cloud Watch alarm that monitors the UnhealthyHostCount metric for the NLB. Configure an Auto Scaling action to replace unhealthy instances when the alarm is in the ALARM state. |
C. Replace the NLB with an Application Load Balancer. Enable HTTP health checks by supplying the URL of the company's application. Configure an Auto Scaling action to replace unhealthy instances.
Explanation: Application availability: NLB cannot assure the availability of the application. This is because it bases its decisions solely on network and TCP-layer variables and has no awareness of the application at all. Generally, NLB determines availability based on the ability of a server to respond to ICMP ping or to correctly complete the three-way TCP handshake. ALB goes much deeper and is capable of determining availability based on not only a successful HTTP GET of a particular page but also the verification that the content is as was expected based on the input parameters.
Question # 3
A company hosts a two-tier application on Amazon EC2 instances and Amazon RDS. The application's demand varies based on the time of day. The load is minimal after work hours and on weekends. The EC2 instances run in an EC2 Auto Scaling group that is configured with a minimum of two instances and a maximum of five instances. The application must be available at all times, but the company is concerned about overall cost.
Which solution meets the availability requirement MOST cost-effectively?
| A. Use all EC2 Spot Instances. Stop the RDS database when it is not in use. | B. Purchase EC2 Instance Savings Plans to cover five EC2 instances. Purchase an RDS Reserved DB Instance | C. Purchase two EC2 Reserved Instances Use up to three additional EC2 Spot Instances as needed. Stop the RDS database when it is not in use. | D. Purchase EC2 Instance Savings Plans to cover two EC2 instances. Use up to three additional EC2 On-Demand Instances as needed. Purchase an RDS Reserved DB Instance. |
C. Purchase two EC2 Reserved Instances Use up to three additional EC2 Spot Instances as needed. Stop the RDS database when it is not in use.
Explanation:
This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On- Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.
Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use.
Option D is incorrect because purchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.
Question # 4
A company needs to migrate a MySQL database from an on-premises data center to AWS
within 2 weeks. The database is 180 TB in size. The company cannot partition the
database.
The company wants to minimize downtime during the migration. The company's internet
connection speed is 100 Mbps.
Which solution will meet these requirements? | A. Order an AWS Snowball Edge Storage Optimized device. Use AWS Database Migration
Service (AWS DMS) and the AWS Schema Conversion Tool (AWS SCT) to migrate the
database to Amazon RDS for MySQL and replicate ongoing changes. Send the Snowball
Edge device back to AWS to finish the migration. Continue to replicate ongoing changes. | B. Establish an AWS Site-to-Site VPN connection between the data center and AWS. Use
AWS Database Migration Service (AWS DMS) and the AWS Schema Conversion Tool
(AWS SCT) to migrate the database to Amazon RDS tor MySQL and replicate ongoing
changes. | C. Establish a 10 Gbps dedicated AWS Direct Connect connection between the data center
and AWS. Use AWS DataSync to replicate the database to Amazon S3. Create a script to
import the data from Amazon S3 to a new Amazon RDS for MySQL database instance. | D. Use the company's existing internet connection. Use AWS DataSync to replicate the
database to Amazon S3. Create a script to import the data from Amazon S3 to a new
Amazon RDS for MySQL database instance. |
A. Order an AWS Snowball Edge Storage Optimized device. Use AWS Database Migration
Service (AWS DMS) and the AWS Schema Conversion Tool (AWS SCT) to migrate the
database to Amazon RDS for MySQL and replicate ongoing changes. Send the Snowball
Edge device back to AWS to finish the migration. Continue to replicate ongoing changes.
Explanation: Given the large size (180 TB) of the database and the time constraint, AWS
Snowball Edge Storage Optimized is the best solution. Snowball Edge allows for the
physical transfer of large datasets to AWS efficiently without relying on slow internet
connections. AWS DMS and SCT can be used to perform ongoing replication of any
changes made during the migration, ensuring minimal downtime.
Option B (VPN): Using a 100 Mbps internet connection would take far too long to transfer
180 TB.
Option C (Direct Connect): Establishing a 10 Gbps Direct Connect link might not be
feasible within the 2-week timeframe.
Option D (DataSync over internet): With the existing internet connection, DataSync
would also take too long.
Question # 5
A company's website uses an Amazon EC2 instance store for its catalog of items. The company wants to make sure that the catalog is highly available and that the catalog is stored in a durable location.
What should a solutions architect do to meet these requirements? | A. Move the catalog to Amazon ElastiCache for Redis. | B. Deploy a larger EC2 instance with a larger instance store. | C. Move the catalog from the instance store to Amazon S3 Glacier Deep Archive. | D. Move the catalog to an Amazon Elastic File System (Amazon EFS) file system. |
D. Move the catalog to an Amazon Elastic File System (Amazon EFS) file system.
Explanation: Moving the catalog to an Amazon Elastic File System (Amazon EFS) file system provides both high availability and durability. Amazon EFS is a fully-managed, highly-available, and durable file system that is built to scale on demand. With Amazon EFS, the catalog data can be stored and accessed from multiple EC2 instances in different availability zones, ensuring high availability. Also, Amazon EFS automatically stores files redundantly within and across multiple availability zones, making it a durable storage option.
Question # 6
A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket A
series ot data preparation jobs aggregate the data for reporting The data preparation jobs
need to run at regular intervals in parallel A few jobs need to run in a specific order later
The company wants to remove the operational overhead of job error handling retry logic,
and state management
Which solution will meet these requirements? | A. Use an AWS Lambda function to process the data as soon as the data is uploaded to
the S3 bucket Invoke Other Lambda functions at regularly scheduled intervals
| B. Use Amazon Athena to process the data Use Amazon EventBndge Scheduler to invoke
Athena on a regular internal
| C. Use AWS Glue DataBrew to process the data Use an AWS Step Functions state
machine to run the DataBrew data preparation jobs
| D. Use AWS Data Pipeline to process the data. Schedule Data Pipeline to process the data
once at midnight. |
C. Use AWS Glue DataBrew to process the data Use an AWS Step Functions state
machine to run the DataBrew data preparation jobs
Explanation: AWS Glue DataBrew is a visual data preparation tool that allows you to
easily clean, normalize, and transform your data without writing any code. You can create
and run data preparation jobs on your data stored in Amazon S3, Amazon Redshift, or
other data sources. AWS Step Functions is a service that lets you coordinate multiple AWS
services into serverless workflows. You can use Step Functions to orchestrate your
DataBrew jobs, define the order and parallelism of execution, handle errors and retries, and
monitor the state of your workflow. By using AWS Glue DataBrew and AWS Step
Functions, you can meet the requirements of the company with minimal operational
overhead, as you do not need to write any code, manage any servers, or deal with complex
dependencies.
Question # 7
A company has an AWS Direct Connect connection from its corporate data center to its
VPC in the us-east-1 Region. The company recently acquired a corporation that has
several VPCs and a Direct Connect connection between its on-premises data center and
the eu-west-2 Region. The CIDR blocks for the VPCs of the company and the corporation
do not overlap. The company requires connectivity between two Regions and the data
centers. The company needs a solution that is scalable while reducing operational
overhead.
What should a solutions architect do to meet these requirements? | A. Set up inter-Region VPC peering between the VPC in us-east-1 and the VPCs in euwest-
2. | B. Create private virtual interfaces from the Direct Connect connection in us-east-1 to the
VPCs in eu-west-2. | C. Establish VPN appliances in a fully meshed VPN network hosted by Amazon EC2. Use
AWS VPN CloudHub to send and receive data between the data centers and each VPC. | D. Connect the existing Direct Connect connection to a Direct Connect gateway. Route
traffic from the virtual private gateways of the VPCs in each Region to the Direct Connect
gateway. |
D. Connect the existing Direct Connect connection to a Direct Connect gateway. Route
traffic from the virtual private gateways of the VPCs in each Region to the Direct Connect
gateway.
Explanation: This solution meets the requirements because it allows the company to use a
single Direct Connect connection to connect to multiple VPCs in different Regions using a Direct Connect gateway. A Direct Connect gateway is a globally available resource that
enables you to connect your on-premises network to VPCs in any AWS Region, except the
AWS China Regions. You can associate a Direct Connect gateway with a transit gateway
or a virtual private gateway in each Region. By routing traffic from the virtual private
gateways of the VPCs to the Direct Connect gateway, you can enable inter-Region and on premises
connectivity for your VPCs. This solution is scalable because you can add more
VPCs in different Regions to the Direct Connect gateway without creating additional
connections. This solution also reduces operational overhead because you do not need to
manage multiple VPN appliances, VPN connections, or VPC peering connections.
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