Free Amazon Web Services SAA-C03 Practice Exam with Questions & Answers | Set: 15

This solution meets the requirements because it allows the monitoring account to query and visualize observability data across the accounts by using CloudWatch. CloudWatch cross-account observability is a feature that enables a central monitoring account to view and interact with observability data shared by other accounts. To enable cross-account observability, the monitoring account needs to configure the types of data to be shared (metrics, logs, and traces) and the source accounts to be linked. The source accounts can be specified by account IDs, organization IDs, or organization paths. To share the data with the monitoring account, the source accounts need to deploy an AWS CloudFormation template provided by the monitoring account. This template creates an observability link resource that represents the link between the source account and the monitoring account. The template also creates a sink resource that represents an attachment point in the monitoring account. The source accounts can share theirobservability data with the sink in the monitoring account. The monitoring account can then use the CloudWatch console, API, or CLI to search, analyze, and correlate the observability data across the accounts. References: CloudWatch cross-account observability, Setting up CloudWatch cross-account observability, [Observability Access Manager API Reference]

The correct solution is to create an Object Lambda Access Point and an AWS Lambda function that redacts the PII when the function reads the file. This way, the company can use the S3 Object Lambda feature to modify the S3 object content on the fly, without creating a copy or changing the originalobject. The external service provider can access the Object Lambda Access Point and get the redacted version of the file. This solution has the least operational overhead because it does not require any additional storage, processing, or synchronization. The solution also scales automatically with the number of customer conversations and the demand from the external service provider. The other options are incorrect because:

Option B is using a batch process on an EC2 instance to read, redact, and write the files to a different S3 bucket. This solution has more operational overhead because it requires managing the EC2 instance, the batch process, and the additional S3 bucket. It also introduces latency and inconsistency between the original and the redacted files.

Option C is using a web application on an EC2 instance to present, redact, and download the files. This solution has more operational overhead because it requires managing the EC2 instance, the web application, and the download process. It also exposes the original files to the web application, which increases the risk of leaking the PII.

Option D is using a DynamoDB table and a Lambda function to store the non-PII data from the files. This solution has more operational overhead because it requires managing the DynamoDB table, the Lambda function, and the data transformation. It also changes the format and the structure of the original files, which may affect the quality control process.

The solution that will meet the requirements is to create Amazon Kinesis data streams for data ingestion, create Amazon Kinesis Data Firehose delivery streams to consume the Kinesis data streams, and specify the S3 bucket as the destination of the delivery streams. This solution will allow the company’s application to ingest real-time data from third-party applications and place the ingested raw data in an S3 bucket. Amazon Kinesis data streams are scalable and durable streams that can capture and store data from hundreds of thousands of sources. Amazon Kinesis Data Firehose is a fully managed service that can deliver streaming data to destinations such as S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Amazon Kinesis Data Firehose can also transform and compress the data before delivering it to S3.

The other solutions are not as effective as the first one because they either do not support real-time data ingestion, do not work with third-party applications, or do not use S3 as the destination. Creating database migration tasks in AWS Database Migration Service (AWS DMS) will not support real-time data ingestion, as AWS DMS is mainly designed for migrating relational databases, not streaming data. AWS DMS also requires replication instances, source endpoints, and target endpoints to be compatible with specific database engines and versions. Creating and configuring AWS DataSync agents on the EC2 instances will not work with third-party applications, as AWS DataSync is a service that transfers data between on-premises storage systems and AWS storage services, not between applications. AWS DataSync also requires installing agents on the source or destination servers. Creating an AWS Direct Connect connection to the application for data ingestion will not use S3 as the destination, as AWS Direct Connect is a service that establishes a dedicated network connection between on-premises andAWS, not between applications and storage services. AWS Direct Connect also requires a physical connection to an AWS Direct Connect location.

This option is the best solution because it allows the company to run its payment application on AWS with minimal operational overhead and infrastructure management. By using Amazon API Gateway, the company can create a secure and scalable API to receive payment notifications from mobile devices. By using AWS Lambda, the company can run a serverless function to validate the payment notifications and send them to the backend application. Lambda handles the provisioning, scaling, and security of the function, reducing the operational complexity and cost. By using Amazon ECS with AWS Fargate,the company can run the backend application on a fully managed container service that scales the compute resources automatically and does not require any EC2 instances to manage. Fargate allocates the right amount of CPU and memory for each container and adjusts them as needed.

A. Create an Amazon Simple Queue Service (Amazon SQS) queue Integrate the queue with an Amazon EventBndge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Service (Amazon EKS) Anywhere Create a standalone cluster. This option is not optimal because it requires the company to manage the Kubernetes cluster that runs the backend application. Amazon EKS Anywhere is a deployment option that allows the company to create and operate Kubernetes clusters on-premises or in other environments outside AWS. The company would need to provision, configure, scale, patch, and monitor the cluster nodes, which can increase theoperational overhead and complexity. Moreover, the company would need to ensure the connectivity and security between the AWS services and the EKS Anywhere cluster, which can also add challenges and risks.

B. Create an Amazon API Gateway API Integrate the API with anAWS Step Functions state ma-chine to receive payment notifications from mobile devices Invoke the state machine to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon Elastic Kubernetes Sen/ice (Amazon EKS). Configure an EKS cluster with self-managed nodes. This option is not ideal because it requires the company to manage the EC2 instances that host the Kubernetes cluster that runs the backend application. Amazon EKS is a fully managed service that runs Kubernetes on AWS, but it still requires the company to manage the worker nodes that run the containers. The company would need to provision, configure, scale, patch, and monitor the EC2 instances, which can increase the operational overhead and infrastructure costs. Moreover, using AWS Step Functions to validate the payment notifications may be unnecessary and complex, as the validation logic can be implemented in a simpler way with Lambda or other services.

C. Create an Amazon Simple Queue Sen/ice (Amazon SQS) queue Integrate the queue with an Amazon EventBridge rule to receive payment notifications from mobile devices Configure the rule to validate payment notifications and send the notifications to the backend application Deploy the backend application on Amazon EC2 Spot Instances Configure a Spot Fleet with a default al-location strategy. This option is not cost-effective because it requires the company to manage the EC2 instances that run the backend 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, using Spot Instances can introduce the risk of interruptions, as Spot Instances are reclaimed by AWS when the demand for On-Demand Instances increases. The company would need to handle the interruptions gracefully and ensure the availability and reliability of the backend application.

AWS Glue is a serverless data integration service that can crawl, catalog, and prepare data for analysis. AWS Glue can automatically discover the schema and partitioning of the data stored in Apache Parquet format in S3, and create a table in the AWS Glue Data Catalog. Amazon Athena is a serverless interactive query service that can run SQL queries directly on data in S3, without requiring any data loading or transformation. Athena can use the table metadata from the AWS Glue Data Catalog to query the data in S3. By using AWS Glue and Athena, you can analyze the log files in S3 most cost-effectively, as you only pay for the resources consumed by the crawler and the queries, and you do not need to provision or manage any servers or clusters.

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

This solution meets the requirements most securely because it uses IAM roles and the STS AssumeRole API operation to authenticate and authorize the inventory application to access the DynamoDB tables in different accounts. IAM roles are more secure than IAM users or certificates because they do not require long-term credentials or passwords. Instead, IAM roles provide temporary security credentials that are automatically rotated and can be configured with a limited duration. The STS AssumeRole API operation enables you to request temporary credentials for a role that you are allowed to assume. By using this operation, you can delegate access to resources that are in different AWS accounts that you own or that are owned by third parties. The trust policy of the role defines which entities can assume the role, and the permissions policy of the role defines which actions can be performed on the resources. By using this solution, you can avoid hard-coding credentials or certificates in the inventory application, and you can also avoid storing them in Secrets Manager or ACM. You can also leverage the built-in security features of IAM and STS, such as MFA, access logging, and policy conditions.

This solution meets the requirements because it enables the company to observe the performance and behavior of its microservices-based application on Amazon EKS. Amazon CloudWatch Container Insights is a feature that collects, aggregates, and summarizes metrics and logs from containerized applications and microservices. Container Insights integrates with Amazon EKS and Kubernetes to provide metrics at the cluster, node, pod, task, and service level. You can use Container Insights to monitor the CPU, memory, disk, and network utilization of your EKS clusters and identify bottlenecks, latency spikes, and other issues. AWS X-Ray is a service that collects data about requests that your application serves, and provides tools that you can use to view, filter, and gain insights into that data. X-Ray integrates with Amazon EKS and Kubernetes to trace the requests that your microservices make to downstream AWS resources, microservices, databases, and web APIs. You can use X-Ray to analyze the root cause of errors, faults, and performance issues, and visualize the service map of your application.

This option is the most cost-effective and scalable way to allow the Lambda function in the primary account to access the EFS file system in the secondary account. VPC peering enables private connectivity between two VPCs without requiring gateways, VPN connections, or dedicated networkconnections. The Lambda function can use the VPC peering connection to mount the EFS file system as a local file system and access the files as needed. The solution does not incur additional data transfer or storage costs, and it leverages the existing EFS file system without duplicating or moving the data.

Option A is not cost-effective because it requires creating a new EFS file system and using AWS DataSync to copy the data from the original EFS file system. This would incur additional storage and data transfer costs, and it would not provide real-time access to the files.

Option C is not scalable because it requires creating a second Lambda function in the secondary account and configuring cross-account permissions to invoke it from the primary account. This would add complexity and latency to the solution, and it would increase the Lambda invocation costs.

Option D is not feasible because Lambda layers are not designed to store large amounts of data or provide file system access. Lambda layers are used to share common code or libraries across multiple Lambda functions. Moving the contents of the EFS file system to a Lambda layer wouldexceed the size limit of 250 MB for a layer, and it would not allow the Lambda function to read or write files to the layer. References:

What Is VPC Peering?

Using Amazon EFS file systems with AWS Lambda

What Are Lambda Layers?

This solution meets the following requirements:

It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.

It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.

It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.

It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

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 intransit 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 thedata 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.

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?]

The solution that will meet the requirements is to create an AWS Lambda function that uses Amazon Rekognition to detect unwanted content, and create a Lambda function URL that the web application invokes when new photos are uploaded. This solution does not involve training a machine learning model, as Amazon Rekognition is a fully managed service that provides pre-trained computer vision models for image and video analysis. Amazon Rekognition can detect unwanted content such as explicit or suggestive adult content, violence, weapons, drugs, and more. By using AWS Lambda, the company can create a serverless function that can be triggered by an HTTP request from the web application. The Lambda function can use the Amazon Rekognition API to analyze the uploaded photos and return a response indicating whether they contain unwanted content or not.

The other solutions are not as effective as the first one because they either involve training a machine learning model, do not support image analysis, or do not work with photos. Creating and deploying a model by using Amazon SageMaker Autopilot involves training a machine learning model, which is not required for the scenario. Amazon SageMaker Autopilot is a service that automatically creates, trains, and tunes the best machine learning models for classification or regression based on the data provided by the user. Creating an Amazon CloudFront function that uses Amazon Comprehend to detect unwanted content does not support image analysis, as Amazon Comprehend is a natural language processing service that analyzes text, not images. Amazon Comprehend can extract insights and relationships from text such as language, sentiment, entities, topics, and more. Creating an AWS Lambda function that uses Amazon Rekognition Video todetect unwanted content does not work with photos, as Amazon Rekognition Video is designed for analyzing video streams, not static images. AmazonRekognition Video can detect activities, objects, faces, celebrities, text, and more in video streams.

The most suitable solution for optimizing the user experience by providing the lowest latency for content uploads is to upload and store content in Amazon S3 and use S3 Transfer Acceleration for the uploads. This solution will enable the company to leverage the AWS global network and edge locations to speed up the data transfer between the users and the S3 buckets.

Amazon S3 is a storage service that provides scalable, durable, and highly available object storage for any type of data.Amazon S3 allows users to store and retrieve datafrom anywhere on the web, and offers various features such as encryption, versioning, lifecycle management, and replication1.

S3 Transfer Acceleration is a feature of Amazon S3 that helps users transfer data to and from S3 buckets more quickly. S3 Transfer Acceleration works by using optimized network paths and Amazon’s backbone network to accelerate data transfer speeds.Userscan enable S3 Transfer Acceleration for their buckets and use a distinct URL to access them, such as .s3-accelerate.amazonaws.com2.

The other options are not correct because they either do not provide the lowest latency or are not suitable for the use case. Uploading and storing content in Amazon S3 and using Amazon CloudFront for the uploads is not correct because this solution is not designed for optimizing uploads, but rather for optimizing downloads. Amazon CloudFront is a content delivery network (CDN) that helps users distribute their content globally with low latency and high transfer speeds.CloudFront works by caching the content at edge locations around the world, so that users can access it quickly and easily from anywhere3. Uploading content to Amazon EC2 instances in the Region that is closest to the user and copying the data to Amazon S3 is not correct because this solution adds unnecessary complexity and cost to the process. Amazon EC2 is a computing service that provides scalable and secure virtual servers in the cloud.Users can launch, stop, or terminate EC2 instances as needed, and choose from various instance types, operating systems, and configurations4. Uploading and storing content in Amazon S3 in the Region that is closest to the user and using multiple distributions of Amazon CloudFront is not correct because this solution is not cost-effective or efficient for the use case. As mentioned above, Amazon CloudFront is a CDN that helps users distribute their content globally with low latency and high transfer speeds.However, creating multiple CloudFront distributions for each Region would incur additional charges and management overhead, and would not be necessary since 90% of the content is consumed within the same Region where it is uploaded3.

This combination of actions will provide high availability and minimum latency for global users by using AWS Global Accelerator and Application Load Balancers. AWS GlobalAccelerator is a networking service that helps you improve the availability, performance, and security of your internet-facing applications by using the AWS global network. It provides two global static public IPs that act as a fixed entry point to your application endpoints, such as Application Load Balancers, in multiple Regions1. Global Accelerator uses the AWS backbone network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure. It also offers TCP and UDP support, traffic encryption, and DDoS protection2. Application Load Balancers are external load balancers that distribute incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones. They support both HTTP and HTTPS (SSL/TLS) protocols, and offer advanced features such as content-based routing, health checks, and integration with other AWS services3. By creatingexternal Application Load Balancers in front of the application in each Region, you can ensure that the application can handle varying load patterns and scale on demand. By creating an AWS Global Accelerator accelerator to route traffic to the load balancers in each Region, you can leverage the performance, security, and availability of the AWS global network to deliver the best possible user experience.

This solution meets the requirements because it uses SCPs to restrict the actions that can be performed on cost usage tags in the organization. SCPs are a type of organization policy that you can use to manage permissions in your organization. SCPs specify the maximum permissions for an organization, organizational unit (OU), or account. You can use SCPs to enforce consistent tag policies across your organization and prevent unauthorized or accidental changes to your tags. You can also create exceptions for authorized principals, such as administrators or auditors, who need to modify tags for legitimate purposes.

The solution that meets the requirements with the least operational overhead is to migrate the web tier to Amazon EC2 instances in public subnets, migrate the application tier to EC2 instances in private subnets, and migrate the database tier to Amazon RDS for MySQL in private subnets. This solution allows the company to migrate its three-tier application to AWS by making minimal changes to the architecture, as it preserves the same web, application, anddatabase tiers and uses the same MySQL database engine. The solution also provides a database solution that can restore data to a specific point in time, as Amazon RDS for MySQL supports automated backups and point-in-time recovery. This solution also reduces the operational overhead by using managed servicessuch as Amazon EC2 and Amazon RDS, which handle tasks such as provisioning, patching, scaling, and monitoring.

The other solutions do not meet the requirements as well as the first one because they either involve more changes to the architecture, do not provide point-in-time recovery, or do not follow best practices for security and availability. Migrating the database tier to Amazon Aurora MySQL would require changing the database engine and potentially modifying the application code to ensure compatibility. Migrating the web tier and the application tier to public subnets would expose them to more security risks and reduce their availability in case of a subnet failure. Migrating the database tier to public subnets would also compromise its security and performance. References:

Migrate Your Application Database to Amazon RDS

Amazon RDS for MySQL

Amazon Aurora MySQL

Amazon VPC

This option is the most cost-effective way to reduce the S3 storage costs in this situation. Incomplete multipart uploads are parts of objects that are not completed or aborted by the application. They consume storage space and incur charges until they are deleted. By enabling an S3 Lifecycle policy that deletes incomplete multipart uploads, you can automatically remove them after a specified period of time (such as one day) and free up the storage space. This will reduce the S3 storage costs and also improve the performance of the application by avoiding unnecessary retries or errors.

Option A is not correct because switching from multipart uploads to Amazon S3 Transfer Acceleration will not reduce the S3 storage costs. Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to and from S3 by using the AWS edge network. It is useful for improving the upload speed of large objects over long distances, but it does not affect the storage space or charges. In fact, it may increase the costs by adding a data transfer fee for using the feature.

Option C is not correct because configuring S3 inventory to prevent objects from being archived too quickly will not reduce the S3 storage costs. Amazon S3 Inventory is a feature that provides a report of the objects and their metadata in an S3 bucket. It is useful for managing and auditingthe S3 objects, but it does not affect the storage space or charges. In fact, it may increase the costs by generating additional S3 objects for the inventory reports.

Option D is not correct because configuring Amazon CloudFront to reduce the number of objects stored in Amazon S3 will not reduce the S3 storage costs. Amazon CloudFront is a content delivery network (CDN) that distributes the S3 objects to edge locations for faster and lower latency access. It is useful for improving the download speed and availability of the S3 objects, but it does not affect the storage space or charges. In fact, it may increase the costs by adding a data transfer fee for using the service. References:

Managing your storage lifecycle

Using multipart upload

Amazon S3 Transfer Acceleration

Amazon S3 Inventory

What Is Amazon CloudFront?

An Application Load Balancer (ALB) is a type of Elastic Load Balancer (ELB) that distributes incoming application traffic across multiple targets, such as EC2 instances, containers, Lambda functions, and IP addresses, in multiple Availability Zones1. An ALB can be internet-facing or internal. An internet-facing ALB has a public DNS name that clients can use to send requests over the internet1. An internal ALB has a private DNS name that clients can use to send requests within a VPC1. This solution meets the requirements of the question because:

It allows external internet traffic to connect to the web server on ports 80 and 443, as the ALB listens for requests on these ports and forwards them to the EC2 instance in the private subnet1.

It does not violate the corporate security mandate, as the EC2 instance is launched in a private subnet and does not have a public IPaddress or a route to an internet gateway2.

It reduces the operational overhead, as the ALB is a fully managed service that handles the tasks of load balancing, health checking, scaling, and security1.

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices.Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.