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Question # 1
A company hosts an application used to upload files to an Amazon S3 bucket Onceuploaded, the files are processed to extract metadata which takes less than 5 seconds Thevolume and frequency of the uploads varies from a few files each hour to hundreds ofconcurrent uploads The company has asked a solutions architect to design a cost-effectivearchitecture that will meet these requirements.What should the solutions architect recommend?
A. Configure AWS CloudTrail trails to tog S3 API calls Use AWS AppSync to process thefiles. B. Configure an object-created event notification within the S3 bucket to invoke an AWSLambda function to process the files. C. Configure Amazon Kinesis Data Streams to process and send data to Amazon S3.Invoke an AWS Lambda function to process the files. D. Configure an Amazon Simple Notification Service (Amazon SNS) topic to process thefiles uploaded to Amazon S3 Invoke an AWS Lambda function to process the files.
Answer: B
Explanation: This option is the most cost-effective and scalable way to process the files
uploaded to S3. AWS CloudTrail is used to log API calls, not to trigger actions based on
them. AWS AppSync is a service for building GraphQL APIs, not for processing files.
Amazon Kinesis Data Streams is used to ingest and process streaming data, not to send
data to S3. Amazon SNS is a pub/sub service that can be used to notify subscribers of
events, not to process files. References:
Using AWS Lambda with Amazon S3
AWS CloudTrail FAQs
What Is AWS AppSync?
[What Is Amazon Kinesis Data Streams?]
[What Is Amazon Simple Notification Service?]
Question # 2
A company runs analytics software on Amazon EC2 instances The software accepts jobrequests from users to process data that has been uploaded to Amazon S3 Users reportthat some submitted data is not being processed Amazon CloudWatch reveals that theEC2 instances have a consistent CPU utilization at or near 100% The company wants toimprove system performance and scale the system based on user load.What should a solutions architect do to meet these requirements?
A. Create a copy of the instance Place all instances behind an Application Load Balancer B. Create an S3 VPC endpoint for Amazon S3 Update the software to reference theendpoint C. Stop the EC2 instances. Modify the instance type to one with a more powerful CPU andmore memory. Restart the instances. D. Route incoming requests to Amazon Simple Queue Service (Amazon SQS) Configurean EC2 Auto Scaling group based on queue size Update the software to read from the queue.
Answer: D
Explanation: This option is the best solution because it allows the company to decouple
the analytics software from the user requests and scale the EC2 instances dynamically
based on the demand. By using Amazon SQS, the company can create a queue that
stores the user requests and acts as a buffer between the users and the analytics software.
This way, the software can process the requests at its own pace without losing any data or
overloading the EC2 instances. By using EC2 Auto Scaling, the company can create an
Auto Scaling group that launches or terminates EC2 instances automatically based on the
size of the queue. This way, the company can ensure that there are enough instances to
handle the load and optimize the cost and performance of the system. By updating the
software to read from the queue, the company can enable the analytics software to
consume the requests from the queue and process the data from Amazon S3.
A. Create a copy of the instance Place all instances behind an Application Load Balancer.
This option is not optimal because it does not address the root cause of the problem, which
is the high CPU utilization of the EC2 instances. An Application Load Balancer can
distribute the incoming traffic across multiple instances, but it cannot scale the instances
based on the load or reduce the processing time of the analytics software. Moreover, this
option can incur additional costs for the load balancer and the extra instances.
B. Create an S3 VPC endpoint for Amazon S3 Update the software to reference the
endpoint. This option is not effective because it does not solve the issue of the high CPU
utilization of the EC2 instances. An S3 VPC endpoint can enable the EC2 instances to
access Amazon S3 without going through the internet, which can improve the network
performance and security. However, it cannot reduce the processing time of the analytics
software or scale the instances based on the load.
C. Stop the EC2 instances. Modify the instance type to one with a more powerful CPU and
more memory. Restart the instances. This option is not scalable because it does not
account for the variability of the user load. Changing the instance type to a more powerful
one can improve the performance of the analytics software, but it cannot adjust the number
of instances based on the demand. Moreover, this option can increase the cost of the
system and cause downtime during the instance modification.
References:
1 Using Amazon SQS queues with Amazon EC2 Auto Scaling - Amazon EC2 Auto
Scaling
2 Tutorial: Set up a scaled and load-balanced application - Amazon EC2 Auto
Scaling
3 Amazon EC2 Auto Scaling FAQs
Question # 3
A company is deploying an application that processes streaming data in near-real time Thecompany plans to use Amazon EC2 instances for the workload The network architecturemust be configurable to provide the lowest possible latency between nodesWhich combination of network solutions will meet these requirements? (Select TWO)
A. Enable and configure enhanced networking on each EC2 instance B. Group the EC2 instances in separate accounts C. Run the EC2 instances in a cluster placement group D. Attach multiple elastic network interfaces to each EC2 instance E. Use Amazon Elastic Block Store (Amazon EBS) optimized instance types.
Answer: A,C
Explanation: These options are the most suitable ways to configure the network
architecture to provide the lowest possible latency between nodes. Option A enables and
configures enhanced networking on each EC2 instance, which is a feature that improves
the network performance of the instance by providing higher bandwidth, lower latency, and
lower jitter. Enhanced networking uses single root I/O virtualization (SR-IOV) or Elastic
Fabric Adapter (EFA) to provide direct access to the network hardware. You can enable
and configure enhanced networking by choosing a supported instance type and a
compatible operating system, and installing the required drivers. Option C runs the EC2
instances in a cluster placement group, which is a logical grouping of instances within a
single Availability Zone that are placed close together on the same underlying hardware.
Cluster placement groups provide the lowest network latency and the highest network
throughput among the placement group options. You can run the EC2 instances in a
cluster placement group by creating a placement group and launching the instances into it.
Option B is not suitable because grouping the EC2 instances in separate accounts does
not provide the lowest possible latency between nodes. Separate accounts are used to
isolate and organize resources for different purposes, such as security, billing, or
compliance. However, they do not affect the network performance or proximity of the
instances. Moreover, grouping the EC2 instances in separate accounts would incur
additional costs and complexity, and it would require setting up cross-account networking
and permissions.
Option D is not suitable because attaching multiple elastic network interfaces to each EC2
instance does not provide the lowest possible latency between nodes. Elastic network
interfaces are virtual network interfaces that can be attached to EC2 instances to provide
additional network capabilities, such as multiple IP addresses, multiple subnets, or
enhanced security. However, they do not affect the network performance or proximity of the
instances. Moreover, attaching multiple elastic network interfaces to each EC2 instance
would consume additional resources and limit the instance type choices. Option E is not suitable because using Amazon EBS optimized instance types does not
provide the lowest possible latency between nodes. Amazon EBS optimized instance types
are instances that provide dedicated bandwidth for Amazon EBS volumes, which are block
storage volumes that can be attached to EC2 instances. EBS optimized instance types
improve the performance and consistency of the EBS volumes, but they do not affect the
network performance or proximity of the instances. Moreover, using EBS optimized
instance types would incur additional costs and may not be necessary for the streaming
data workload. References:
Enhanced networking on Linux
Placement groups
Elastic network interfaces
Amazon EBS-optimized instances
Question # 4
A company runs a container application on a Kubernetes cluster in the company's datacenter The application uses Advanced Message Queuing Protocol (AMQP) tocommunicate with a message queue The data center cannot scale fast enough to meet thecompany's expanding business needs The company wants to migrate the workloads toAWSWhich solution will meet these requirements with the LEAST operational overhead? \
A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS)Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. B. Migrate the container application to Amazon Elastic Kubernetes Service (Amazon EKS)Use Amazon MQ to retrieve the messages. C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ toretrieve the messages. D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service(Amazon SQS) to retrieve the messages.
Answer: B
Explanation: This option is the best solution because it allows the company to migrate the
container application to AWS with minimal changes and leverage a managed service to run
the Kubernetes cluster and the message queue. By using Amazon EKS, the company can
run the container application on a fully managed Kubernetes control plane that is
compatible with the existing Kubernetes tools and plugins. Amazon EKS handles the
provisioning, scaling, patching, and security of the Kubernetes cluster, reducing the
operational overhead and complexity. By using Amazon MQ, the company can use a fully
managed message broker service that supports AMQP and other popular messaging
protocols. Amazon MQ handles the administration, maintenance, and scaling of the
message broker, ensuring high availability, durability, and security of the messages.
A. Migrate the container application to Amazon Elastic Container Service (Amazon ECS)
Use Amazon Simple Queue Service (Amazon SQS) to retrieve the messages. This option
is not optimal because it requires the company to change the container orchestration
platform from Kubernetes to ECS, which can introduce additional complexity and risk.
Moreover, it requires the company to change the messaging protocol from AMQP to SQS,
which can also affect the application logic and performance. Amazon ECS and Amazon
SQS are both fully managed services that simplify the deployment and management of
containers and messages, but they may not be compatible with the existing application
architecture and requirements.
C. Use highly available Amazon EC2 instances to run the application Use Amazon MQ to
retrieve the messages. This option is not ideal because it requires the company to manage
the EC2 instances that host the container application. The company would need to
provision, configure, scale, patch, and monitor the EC2 instances, which can increase the
operational overhead and infrastructure costs. Moreover, the company would need to
install and maintain the Kubernetes software on the EC2 instances, which can also add
complexity and risk. Amazon MQ is a fully managed message broker service that supports
AMQP and other popular messaging protocols, but it cannot compensate for the lack of a
managed Kubernetes service.
D. Use AWS Lambda functions to run the application Use Amazon Simple Queue Service
(Amazon SQS) to retrieve the messages. This option is not feasible because AWS Lambda
does not support running container applications directly. Lambda functions are executed in
a sandboxed environment that is isolated from other functions and resources. To run container applications on Lambda, the company would need to use a custom runtime or a
wrapper library that emulates the container API, which can introduce additional complexity
and overhead. Moreover, Lambda functions have limitations in terms of available CPU,
memory, and runtime, which may not suit the application needs. Amazon SQS is a fully
managed message queue service that supports asynchronous communication, but it does
not support AMQP or other messaging protocols.
References:
1 Amazon Elastic Kubernetes Service - Amazon Web Services
2 Amazon MQ - Amazon Web Services
3 Amazon Elastic Container Service - Amazon Web Services
4 AWS Lambda FAQs - Amazon Web Services
Question # 5
A company runs a real-time data ingestion solution on AWS. The solution consists of themost recent version of Amazon Managed Streaming for Apache Kafka (Amazon MSK). Thesolution is deployed in a VPC in private subnets across three Availability Zones.A solutions architect needs to redesign the data ingestion solution to be publicly availableover the internet. The data in transit must also be encrypted.Which solution will meet these requirements with the MOST operational efficiency?
A. Configure public subnets in the existing VPC. Deploy an MSK cluster in the publicsubnets. Update the MSK cluster security settings to enable mutual TLS authentication. B. Create a new VPC that has public subnets. Deploy an MSK cluster in the publicsubnets. Update the MSK cluster security settings to enable mutual TLS authentication. C. Deploy an Application Load Balancer (ALB) that uses private subnets. Configure an ALBsecurity group inbound rule to allow inbound traffic from the VPC CIDR block for HTTPSprotocol. D. Deploy a Network Load Balancer (NLB) that uses private subnets. Configure an NLBlistener for HTTPS communication over the internet.
Answer: A
Explanation: The solution that meets the requirements with the most operational efficiency
is to configure public subnets in the existing VPC and deploy an MSK cluster in the public
subnets. This solution allows the data ingestion solution to be publicly available over the
internet without creating a new VPC or deploying a load balancer. The solution also
ensures that the data in transit is encrypted by enabling mutual TLS authentication, which
requires both the client and the server to present certificates for verification. This solution
leverages the public access feature of Amazon MSK, which is available for clusters running
Apache Kafka 2.6.0 or later versions1.
The other solutions are not as efficient as the first one because they either create
unnecessary resources or do not encrypt the data in transit. Creating a new VPC with
public subnets would incur additional costs and complexity for managing network resources
and routing. Deploying an ALB or an NLB would also add more costs and latency for the
data ingestion solution. Moreover, an ALB or an NLB would not encrypt the data in transit
by itself, unless they are configured with HTTPS listeners and certificates, which would
require additional steps and maintenance. Therefore, these solutions are not optimal for the
given requirements.
References:
Public access - Amazon Managed Streaming for Apache Kafka
Question # 6
A company runs a Java-based job on an Amazon EC2 instance. The job runs every hourand takes 10 seconds to run. The job runs on a scheduled interval and consumes 1 GB ofmemory. The CPU utilization of the instance is low except for short surges during which thejob uses the maximum CPU available. The company wants to optimize the costs to run thejob.Which solution will meet these requirements?
A. Use AWS App2Container (A2C) to containerize the job. Run the job as an AmazonElastic Container Service (Amazon ECS) task on AWS Fargate with 0.5 virtual CPU(vCPU) and 1 GB of memory. B. Copy the code into an AWS Lambda function that has 1 GB of memory. Create anAmazon EventBridge scheduled rule to run the code each hour. C. Use AWS App2Container (A2C) to containerize the job. Install the container in theexisting Amazon Machine Image (AMI). Ensure that the schedule stops the container whenthe task finishes. D. Configure the existing schedule to stop the EC2 instance at the completion of the joband restart the EC2 instance when the next job starts.
Answer: B
Explanation: AWS Lambda is a serverless compute service that allows you to run code
without provisioning or managing servers. You can create Lambda functions using various
languages, including Java, and specify the amount of memory and CPU allocated to your function. Lambda charges you only for the compute time you consume, which is calculated
based on the number of requests and the duration of your code execution. You can use
Amazon EventBridge to trigger your Lambda function on a schedule, such as every hour,
using cron or rate expressions. This solution will optimize the costs to run the job, as you
will not pay for any idle time or unused resources, unlike running the job on an EC2
instance. References: 1: AWS Lambda - FAQs2, General Information section2: Tutorial:
Schedule AWS Lambda functions using EventBridge3, Introduction section3: Schedule
expressions using rate or cron - AWS Lambda4, Introduction section.
Question # 7
An ecommerce company runs applications in AWS accounts that are part of anorganization in AWS Organizations The applications run on Amazon Aurora PostgreSQLdatabases across all the accounts The company needs to prevent malicious activity andmust identify abnormal failed and incomplete login attempts to the databasesWhich solution will meet these requirements in the MOST operationally efficient way?
A. Attach service control policies (SCPs) to the root of the organization to identify the failedlogin attempts B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the memberaccounts of the organization C. Publish the Aurora general logs to a log group in Amazon CloudWatch Logs Export thelog data to a central Amazon S3 bucket D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a centralAmazon S3 bucket
Answer: C
Explanation: This option is the most operationally efficient way to meet the requirements
because it allows the company to monitor and analyze the database login activity across all
the accounts in the organization. By publishing the Aurora general logs to a log group in
Amazon CloudWatch Logs, the company can enable the logging of the database
connections, disconnections, and failed authentication attempts. By exporting the log data
to a central Amazon S3 bucket, the company can store the log data in a durable and costeffective
way and use other AWS services or tools to perform further analysis or alerting on
the log data. For example, the company can use Amazon Athena to query the log data in
Amazon S3, or use Amazon SNS to send notifications based on the log data.
A. Attach service control policies (SCPs) to the root of the organization to identify the failed
login attempts. This option is not effective because SCPs are not designed to identify the
failed login attempts, but to restrict the actions that the users and roles can perform in the
member accounts of the organization. SCPs are applied to the AWS API calls, not to the
database login attempts. Moreover, SCPs do not provide any logging or analysis
capabilities for the database activity.
B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the member
accounts of the organization. This option is not optimal because the Amazon RDS
Protection feature in Amazon GuardDuty is not available for Aurora PostgreSQL
databases, but only for Amazon RDS for MySQL and Amazon RDS for MariaDB databases. Moreover, the Amazon RDS Protection feature does not monitor the database
login attempts, but the network and API activity related to the RDS instances.
D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a central
Amazon S3 bucket. This option is not sufficient because AWS CloudTrail does not capture
the database login attempts, but only the AWS API calls made by or on behalf of the
Aurora PostgreSQL database. For example, AWS CloudTrail can record the events such
as creating, modifying, or deleting the database instances, clusters, or snapshots, but not
the events such as connecting, disconnecting, or failing to authenticate to the database.
References:
1 Working with Amazon Aurora PostgreSQL - Amazon Aurora
2 Working with log groups and log streams - Amazon CloudWatch Logs
3 Exporting Log Data to Amazon S3 - Amazon CloudWatch Logs
[4] Amazon GuardDuty FAQs
[5] Logging Amazon RDS API Calls with AWS CloudTrail - Amazon Relational
Database Service
Question # 8
A company needs to provide customers with secure access to its data. The companyprocesses customer data and stores the results in an Amazon S3 bucket.All the data is subject to strong regulations and security requirements. The data must beencrypted at rest. Each customer must be able to access only their data from their AWSaccount. Company employees must not be able to access the data.Which solution will meet these requirements?
A. Provision an AWS Certificate Manager (ACM) certificate for each customer. Encrypt thedata client-side. In the private certificate policy, deny access to the certificate for allprincipals except an 1AM role that the customer provides. B. Provision a separate AWS Key Management Service (AWS KMS) key for eachcustomer. Encrypt the data server-side. In the S3 bucket policy, deny decryption of data forall principals except an 1AM role that the customer provides. C. Provision a separate AWS Key Management Service (AWS KMS) key for eachcustomer. Encrypt the data server-side. In each KMS key policy, deny decryption of datafor all principals except an 1AM role that the customer provides. D. Provision an AWS Certificate Manager (ACM) certificate for each customer. Encrypt thedata client-side. In the public certificate policy, deny access to the certificate for allprincipals except an 1AM role that the customer provides.
Answer: C
Explanation: The correct solution is to provision a separate AWS KMS key for each
customer and encrypt the data server-side. This way, the company can use the S3
encryption feature to protect the data at rest and delegate the control of the encryption keys
to the customers. The customers can then use their own IAM roles to access and decrypt
their data. The company employees will not be able to access the data because they are
not authorized by the KMS key policies. The other options are incorrect because:
Option A and D are using ACM certificates to encrypt the data client-side. This is
not a recommended practice for S3 encryption because it adds complexity and
overhead to the encryption process. Moreover, the company will have to manage
the certificates and their policies for each customer, which is not scalable and
secure.
Option B is using a separate KMS key for each customer, but it is using the S3
bucket policy to control the decryption access. This is not a secure solution
because the bucket policy applies to the entire bucket, not to individual objects.
Therefore, the customers will be able to access and decrypt each other’s data if
they have the permission to list the bucket contents. The bucket policy also
overrides the KMS key policy, which means the company employees can access
the data if they have the permission to use the KMS key.
References:
S3 encryption
KMS key policies
ACM certificates
Question # 9
A company has a nightly batch processing routine that analyzes report files that an onpremisesfile system receives daily through SFTP. The company wants to move thesolution to the AWS Cloud. The solution must be highly available and resilient. The solutionalso must minimize operational effort.Which solution meets these requirements?
A. Deploy AWS Transfer for SFTP and an Amazon Elastic File System (Amazon EFS) filesystem for storage. Use an Amazon EC2 instance in an Auto Scaling group with ascheduled scaling policy to run the batch operation. B. Deploy an Amazon EC2 instance that runs Linux and an SFTP service. Use an AmazonElastic Block Store {Amazon EBS) volume for storage. Use an Auto Scaling group with theminimum number of instances and desired number of instances set to 1. C. Deploy an Amazon EC2 instance that runs Linux and an SFTP service. Use an AmazonElastic File System (Amazon EFS) file system for storage. Use an Auto Scaling group withthe minimum number of instances and desired number of instances set to 1. D. Deploy AWS Transfer for SFTP and an Amazon S3 bucket for storage. Modify theapplication to pull the batch files from Amazon S3 to an Amazon EC2 instance forprocessing. Use an EC2 instance in an Auto Scaling group with a scheduled scaling policyto run the batch operation.
Answer: D
Explanation: The solution that meets the requirements of high availability, performance,
security, and static IP addresses is to use Amazon CloudFront, Application Load Balancers
(ALBs), Amazon Route 53, and AWS WAF. This solution allows the company to distribute
its HTTP-based application globally using CloudFront, which is a content delivery network
(CDN) service that caches content at edge locations and provides static IP addresses for
each edge location. The company can also use Route 53 latency-based routing to route
requests to the closest ALB in each Region, which balances the load across the EC2
instances. The company can also deploy AWS WAF on the CloudFront distribution to
protect the application against common web exploits by creating rules that allow, block, or
count web requests based on conditions that are defined. The other solutions do not meet
all the requirements because they either use Network Load Balancers (NLBs), which do not
support HTTP-based applications, or they do not use CloudFront, which provides better
performance and security than AWS Global Accelerator. References :=
Amazon CloudFront
Application Load Balancer
Amazon Route 53
AWS WAF
Question # 10
A company uses high concurrency AWS Lambda functions to process a constantlyincreasing number of messages in a message queue during marketing events. TheLambda functions use CPU intensive code to process the messages. The company wantsto reduce the compute costs and to maintain service latency for its customers.Which solution will meet these requirements?
A. Configure reserved concurrency for the Lambda functions. Decrease the memoryallocated to the Lambda functions. B. Configure reserved concurrency for the Lambda functions. Increase the memoryaccording to AWS Compute Optimizer recommendations. C. Configure provisioned concurrency for the Lambda functions. Decrease the memoryallocated to the Lambda functions. D. Configure provisioned concurrency for the Lambda functions. Increase the memoryaccording to AWS Compute Optimizer recommendations.
Answer: D
Explanation: The company wants to reduce the compute costs and maintain service
latency for its Lambda functions that process a constantly increasing number of messages
in a message queue. The Lambda functions use CPU intensive code to process the
messages. To meet these requirements, a solutions architect should recommend the
following solution:
Configure provisioned concurrency for the Lambda functions. Provisioned
concurrency is the number of pre-initialized execution environments that are
allocated to the Lambda functions. These execution environments are prepared to
respond immediately to incoming function requests, reducing the cold start latency.
Configuring provisioned concurrency also helps to avoid throttling errors due to
reaching the concurrency limit of the Lambda service.
Increase the memory according to AWS Compute Optimizer recommendations.
AWS Compute Optimizer is a service that provides recommendations for optimal
AWS resource configurations based on your utilization data. By increasing the
memory allocated to the Lambda functions, you can also increase the CPU power
and improve the performance of your CPU intensive code. AWS Compute
Optimizer can help you find the optimal memory size for your Lambda functions
based on your workload characteristics and performance goals.
This solution will reduce the compute costs by avoiding unnecessary over-provisioning of
memory and CPU resources, and maintain service latency by using provisioned
concurrency and optimal memory size for the Lambda functions.
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