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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design Cost-Optimized Architectures | 20% | - Cost management tools
|
| Topic 2: Design High-Performing Architectures | 24% | - Compute and storage optimization
|
| Topic 3: Design Secure Architectures | 30% | - Secure workloads and applications
|
| Topic 4: Design Resilient Architectures | 26% | - Fault tolerance and recovery
|
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NEW QUESTION # 304
A company wants to use artificial intelligence (Al) to determine the quality of its customer service calls. The company currently manages calls in four different languages, including English. The company will offer new languages in the future. The company does not have the resources to regularly maintain machine learning (ML) models.
The company needs to create written sentiment analysis reports from the customer service call recordings. The customer service call recording text must be translated into English.
Which combination of steps will meet these requirements? (Select THREE.)
Answer: A,D,E
Explanation:
These answers are correct because they meet the requirements of creating written sentiment analysis reports from the customer service call recordings in any language and translating them into English. Amazon Transcribe is a service that uses advanced machine learning technologies to recognize speech in audio files and transcribe them into text. You can use Amazon Transcribe to convert the audio recordings in any language into text, and specify the language code of the source audio. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. You can use Amazon Translate to translate text in any language to English, and specify the source and target language codes. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. You can use Amazon Comprehend to create the sentiment analysis reports, which determine if the text is positive, negative, neutral, or mixed.
References:
https://docs.aws.amazon.com/transcribe/latest/dg/what-is-transcribe.html
https://docs.aws.amazon.com/translate/latest/dg/what-is.html
https://docs.aws.amazon.com/comprehend/latest/dg/how-sentiment.html
NEW QUESTION # 305
A manufacturing company has EC2 instances running in AWS. The EC2 instances are configured with Auto Scaling. There are a lot of requests being lost because of too much load on the servers. The Auto Scaling is launching new EC2 instances to take the load accordingly yet, there are still some requests that are being lost.
Which of the following is the MOST suitable solution that you should implement to avoid losing recently submitted requests?
Answer: A
Explanation:
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that makes it easy to decouple and scale microservices, distributed systems, and serverless applications. Building applications from individual components that each perform a discrete function improves scalability and reliability, and is best practice design for modern applications. SQS makes it simple and cost-effective to decouple and coordinate the components of a cloud application. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be always available.
The number of messages in your Amazon SQS queue does not solely define the number of instances needed. In fact, the number of instances in the fleet can be driven by multiple factors, including how long it takes to process a message and the acceptable amount of latency (queue delay).
The solution is to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows:
Backlog per instance: To determine your backlog per instance, start with the Amazon SQS metric ApproximateNumberOfMessages to determine the length of the SQS queue (number of messages available for retrieval from the queue). Divide that number by the fleet's running capacity, which for an Auto Scaling group is the number of instances in the InService state, to get the backlog per instance.
Acceptable backlog per instance: To determine your target value, first calculate what your application can accept in terms of latency. Then, take the acceptable latency value and divide it by the average time that an EC2 instance takes to process a message.
To illustrate with an example, let's say that the current ApproximateNumberOfMessages is 1500 and the fleet's running capacity is 10. If the average processing time is 0.1 seconds for each message and the longest acceptable latency is 10 seconds then the acceptable backlog per instance is 10 / 0.1, which equals 100. This means that 100 is the target value for your target tracking policy. Because the backlog per instance is currently at 150 (1500 / 10), your fleet scales out by five instances to maintain proportion to the target value.
Hence, the correct answer is: Use an Amazon SQS queue to decouple the application components and scale-out the EC2 instances based upon the ApproximateNumberOfMessages metric in Amazon CloudWatch.
Replacing the Auto Scaling group with a cluster placement group to achieve a low-latency network performance necessary for tightly-coupled node-to-node communication is incorrect because although it is true that a cluster placement group allows you to achieve a low-latency network performance, you still need to use Auto Scaling for your architecture to add more EC2 instances.
Using larger instances for your application with an attached Elastic Fabric Adapter (EFA) is incorrect because using a larger EC2 instance would not prevent data from being lost in case of a larger spike.
You can take advantage of the durability and elasticity of SQS to keep the messages available for consumption by your instances. Elastic Fabric Adapter (EFA) is simply a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS.
Setting up Amazon Aurora Serverless for on-demand, auto-scaling configuration of your EC2 Instances and also enabling Amazon Aurora Parallel Query feature for faster analytical queries over your current data is incorrect because although the Amazon Aurora Parallel Query feature provides faster analytical queries over your current data, Amazon Aurora Serverless is an on-demand, auto-scaling configuration for your database, and NOT for your EC2 instances. This is actually an auto-scaling configuration for your Amazon Aurora database and not for your compute services. References:
https://aws.amazon.com/sqs/
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html
https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html Check out this Amazon SQS Cheat Sheet:
https://tutorialsdojo.com/amazon-sqs/
NEW QUESTION # 306
[Design Resilient Architectures]
A company runs a three-tier web application in the AWS Cloud that operates across three Availability Zones. The application architecture has an Application Load Balancer, an Amazon EC2 web server that hosts user session states, and a MySQL database that runs on an EC2 instance. The company expects sudden increases in application traffic. The company wants to be able to scale to meet future application capacity demands and to ensure high availability across all three Availability Zones.
Which solution will meet these requirements?
Answer: D
Explanation:
This answer is correct because it meets the requirements of scaling to meet future application capacity demands and ensuring high availability across all three Availability Zones. By migrating the MySQL database to Amazon RDS for MySQL with a Multi-AZ DB cluster deployment, the company can benefit from automatic failover, backup, and patching of the database across multiple Availability Zones. By using Amazon ElastiCache for Redis with high availability, the company can store session data and cache reads in a fast, in-memory data store that can also fail over across Availability Zones. By migrating the web server to an Auto Scaling group that is in three Availability Zones, the company can automatically scale the web server capacity based on the demand and traffic patterns.
Reference:
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.MultiAZ.html
https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/AutoFailover.html
https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html
NEW QUESTION # 307
A company has multiple AWS accounts with applications deployed in the us-west-2 Region Application logs are stored within Amazon S3 buckets in each account The company wants tobuild a centralized log analysis solution that uses a single S3 bucket Logs must not leave us-west-2, and the company wants to incur minimal operational overhead Which solution meets these requirements and is MOST cost-effective?
Answer: D
Explanation:
This solution meets the following requirements:
It is cost-effective, as it only charges for the storage and data transfer of the replicated objects, and does not require any additional AWS services or custom scripts. S3 Same-Region Replication (SRR) is a feature that automatically replicates objects across S3 buckets within the same AWS Region. SRR can help you aggregate logs from multiple sources to a single destination for analysis and auditing. SRR also preserves the metadata, encryption, and access control of the source objects.
It is operationally efficient, as it does not require any manual intervention or scheduling. SRR replicates objects as soon as they are uploaded to the source bucket, ensuring that the destination bucket always has the latest log data. SRR also handles any updates or deletions of the source objects, keeping the destination bucket in sync. SRR can be enabled with a few clicks in the S3 console or with a simple API call.
It is secure, as it does not allow the logs to leave the us-west-2 Region. SRR only replicates objects within the same AWS Region, ensuring that the data sovereignty and compliance requirements are met. SRR also supports encryption of the source and destination objects, using either server-side encryption with AWS KMS or S3-managed keys, or client-side encryption.
References:
Same-Region Replication - Amazon Simple Storage Service
How do I replicate objects across S3 buckets in the same AWS Region?
Centralized Logging on AWS | AWS Solutions | AWS Solutions Library
NEW QUESTION # 308
A company discovers that an Amazon DynamoDB Accelerator (DAX) cluster for the company's web application workload is not encrypting data at rest. The company needs to resolve thesecurity issue.
Which solution will meet this requirement?
Answer: D
Explanation:
DAX does not support enabling encryption at rest on an existing cluster. To use encryption at rest, you must create a new DAX cluster with encryption enabled at creation time and migrate workloads accordingly.
Reference:
NEW QUESTION # 309
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