SAA-C03 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverageexisting AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

Auto scaling storage RDS will ease storage issues and migrating Oracle Pl/Sql to Aurora is cumbersome. Also Aurora has auto storage scaling by default.https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PIOPS.StorageTypes.html#USER_PIOPS.Autoscaling

"Replication from the primary DB cluster to all secondaries is handled by the Aurora storage layer rather than by the database engine, so lag time for replicating changes is minimal—typically, less than 1 second. Keeping the database engine out of the replication process meansthat the database engine is dedicated to processing workloads. It also means that you don't need to configure or manage the Aurora MySQL binlog (binary logging) replication."

This option is the most efficient because it uses Cost Explorer, which is a tool that allowsyou to visualize, understand, and manage your AWS costs and usage over time1. You can create a report in CostExplorer that lists AWS billed items by user, using the user name tag as a filter2. You can then download the report as a CSV file and use it for budget planning. Option A is less efficient because it uses Amazon Athena, which is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL3. You would need to set up an Athena table that points to your AWS Cost and Usage Report data in S3, and then run a query to generate the report. This would incur additional costs and complexity. Option C is less efficient because it uses the billing dashboard, which provides a high-level summary of your AWS costsand usage. You can access the bill details from the billing dashboard and download them via bill, but this would not list the billed items by user. You would need to use tags to group your costs by user name, which would require additional steps. Option D is less efficient because it uses AWS Budgets, which is a tool that allows you to plan your service usage, service costs, and instance reservations. You can modify a cost budget in AWS Budgets to alert with Amazon Simple Email Service (Amazon SES), but this would not generate a report of AWS billed items by user. This would only notify you when your actual or forecasted costs exceed or are expected to exceed your budgeted amount.

A public NAT gateway enables instances in a private subnet to send outbound traffic to the internet, while preventing the internet from initiating connections with the instances. A public NAT gateway requires an elastic IP address and a route to the internet gateway for the VPC. A private NAT gateway enables instances in a private subnet to connect to other VPCs or on-premises networks through a transit gateway or a virtual private gateway. A private NAT gateway does not require an elastic IP address or an internet gateway. Both private and public NAT gateways map the source private IPv4 address of the instances to the private IPv4 address of the NAT gateway, but in the case of a public NAT gateway, the internet gateway then maps the private IPv4 address of the public NAT gateway to the elastic IP address associated with the NAT gateway. When sending response traffic to the instances, whether it’s a public or private NAT gateway, the NAT gateway translates the address back to the original source IP address.

Creating public NAT gateways in the same private subnets as the EC2 instances (option A) is not a valid solution, as the NAT gateways would not have a route to the internet gateway. Creating private NAT gateways in the same private subnets as the EC2 instances (option B) is also not a valid solution, as the instances would not be able to access the internet through the private NATgateways. Creating private NAT gateways in public subnets in the same VPCs as the EC2 instances (option D) is not a valid solution either, as the internet gateway would drop the traffic from the private NAT gateways.

Therefore, the only valid solution is to create public NAT gateways in public subnets in the same VPCs as the EC2 instances (option C), as this would allow the instances to access the internet through the public NAT gateways and the internet gateway. References:

NAT gateways - Amazon Virtual Private Cloud

NAT gateway use cases - Amazon Virtual Private Cloud

Amazon Web Services – Introduction to NAT Gateways

What is AWS NAT Gateway? - KnowledgeHut

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

This solution meets the following requirements:

It is private and secure, as it allows the EC2 instances to access the S3 bucket without using the public internet. A VPC endpoint is a gateway that enables you to create a private connection between your VPC and another AWS service, such as S3, within the same Region. A VPC endpoint for S3 provides secure and direct access to S3 buckets and objects using private IP addresses from your VPC. You can also use VPC endpoint policies and S3 bucket policies to control the access to the S3 resources based on the endpoint, the IAM user, the IAM role, or the source IP address.

It is simple and scalable, as it does not require any additional AWS services, gateways, or NAT devices. A VPC endpoint for S3 is a fully managed service that scales automatically with the network traffic. You can create a VPC endpoint for S3 with a few clicks in the VPC console or with a simple API call. You can also use the same VPC endpoint to access multiple S3 buckets in the same Region.

CloudWatch cross-account observability is a feature that allows you to monitor and troubleshoot applications that span multiple accounts within a Region. You canseamlessly search, visualize, and analyze your metrics, logs, traces, and Application Insights applications in any of the linked accounts without account boundaries1. To enable CloudWatch cross-account observability, you need to set up one or more AWS accounts as monitoring accounts and link them with multiple source accounts. A monitoring account is a central AWS account that can view and interact with observability data shared by other accounts. A source account is an individual AWS account that shares observability data and resources with one or more monitoring accounts1. To create links between monitoring accounts and source accounts, you can use the CloudWatch console, the AWS CLI, or the AWS API. You can also use AWS Organizations to link accounts in an organization or organizational unit to the monitoring account1. CloudWatch provides a CloudFormation template that you can deploy in each source account to share observability data with the monitoring account. The template creates a sink resource in the monitoring account and an observability link resource in the source account. The template also creates the necessary IAM roles and policies to allow cross-account access to the observability data2. Therefore, the solution that meets the requirements of the question is to enable CloudWatch cross-account observability for the monitoring account and deploy the CloudFormation template provided by the monitoring account in each AWS account to share the data with the monitoring account.

The other options are not valid because:

Service control policies (SCPs) are a type of organization policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing youto ensure your accounts stay within your organization’s access control guidelines3. SCPs do not provide access to CloudWatch in the monitoring account, but rather restrict the actions that users and roles can perform in the source accounts. SCPs are not required toenable CloudWatch cross-account observability, as the CloudFormation template creates the necessary IAM roles and policies for cross-account access2.

IAM users are entities that you create in AWS to represent the people or applications that use them to interact with AWS. IAM users can have permissions to access the resources in your AWS account4. Configuring a new IAM user in the monitoring account and an IAM policy in each AWS account to have access to query and visualize the CloudWatch data in the account is not a valid solution, as it does not enable CloudWatch cross-account observability. This solution would require the IAM user to switch between different accounts to view the observability data, which is not seamless and efficient. Moreover, this solution would not allow the IAM user to search, visualize, and analyze metrics, logs, traces, and Application Insights applications across multiple accounts in a single place1.

Cross-account IAM policies are policies that allow you to delegate access to resources that are in different AWS accounts that you own. You attach a cross-account policy to a user or group in one account, and then specify which accounts theuser or group can access5. Creating a new IAM user in the monitoring account and cross-account IAM policies in each AWS account is not avalid solution, as it does not enable CloudWatch cross-account observability. This solution would also require the IAM user to switch between different accounts to view the observability data, which is not seamless and efficient. Moreover, this solution would not allow the IAM user to search, visualize, and analyze metrics, logs, traces, and Application Insights applications across multiple accounts in a single place1.

 S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.

B. Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.

C. Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.

D. Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.

Recycle Bin is a data recovery feature that enables you to restore accidentally deleted Amazon EBS snapshots and EBS-backed AMIs. When using Recycle Bin, if your resources are deleted, they are retained in the Recycle Bin for a time period that you specify before being permanently deleted. You can restore a resource from the Recycle Bin at any timebefore its retention period expires. This solution has the least operational overhead, as you do not need to create, copy, or upload any additional resources. You can also manage tags and permissions for AMIs in the Recycle Bin. AMIs in the Recycle Bin do not incur any additional charges. References:

Recover AMIs from the Recycle Bin

Recover an accidentally deleted Linux AMI

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.

Deletion protection is a feature of DynamoDB that prevents accidental deletion of tables. When deletion protection is enabled, you cannot delete a table unless you explicitly disable it first. This adds an extra layer of security and reduces the risk of data loss and operational disruption. Deletion protection is easy to enable and disable using the AWS Management Console, the AWSCLI, or the DynamoDB API. This solution has the least operational overhead, as you do not need to create, manage, or invoke any additional resources or services. References:

Using deletion protection to protect your table

Preventing Accidental Table Deletion in DynamoDB

Amazon DynamoDB now supports table deletion protection

A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.

A lazy loading caching strategy only loads data into the cache when it is requested, and updates the cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.

An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.

An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.

The solution that meets the requirements is to develop an on-premises custom identity broker application or process that uses AWS Security Token Service (AWS STS) to get short-lived credentials. This solution allows the company to use its existing LDAP directory service to authenticate its users to the AWS Management Console, without requiring SAML compatibility.The custom identity broker application or process can act as a proxy between the LDAP directory service and AWS STS, and can request temporary security credentials for the users based on their LDAP attributes and roles. The users can then use these credentials to access the AWS Management Console via a sign-in URL generated by the identity broker. This solution also enhances security by using short-lived credentials that expire after a specified duration.

The other solutions do not meet the requirements because they either require SAML compatibility or do not provide access to the AWS Management Console. Enabling AWS IAM Identity Center (AWS Single Sign-On) between AWS and the on-premises LDAP would require the LDAP directory service to support SAML 2.0, which is not the case for this scenario. Creating an IAM policy that uses AWS credentials and integrating the policy into LDAP would not provide access to the AWS Management Console, but only to the AWSAPIs. Setting up a process that rotates the IAM credentials whenever LDAP credentials are updated would also not provide access to the AWS Management Console, but only to the AWS CLI. Therefore, these solutions are not suitable for the given requirements.

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 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?]

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.

This solution meets the requirements because it allows the company to use both predictive scaling and dynamic scaling to optimize the capacity of its Auto Scaling group. Predictive scaling uses machine learning to analyze historical data and forecast future traffic patterns. It then adjusts the desired capacity of the group in advance of the predicted changes. Dynamic scaling uses target tracking to maintain a specified metric (such as CPU utilization) at a target value. It scales the group in or out as needed to keep the metric close to the target. By using both scaling methods, the company can benefitfrom faster, simpler, and more accurate scaling that responds to both forecasted and live changes in utilization.

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.

AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources. AWS WAF allows users to create rules that block, allow, or count web requests based on customizable web security rules. One of the types of rules that can be created is an IP match rule, which allows users to specify a list of IP addresses or IP address ranges that they want to allow or block. By modifying the configuration of AWS WAF to add an IP match condition to block the malicious IP address, the solution architect can prevent the attacker from accessing the website through the CloudFront distribution and the ALB.

The other options are not correct because they do not effectively block the malicious IP address from accessing the website. Modifying the network ACL on the CloudFront distribution or the EC2 instances in the target groups behind the ALB will not work because network ACLs are stateless and do not evaluate traffic at the application layer. Modifying the security groups for the EC2 instances in the target groups behind the ALB will not work because security groups are stateful and only evaluate traffic at the instance level, not at the load balancer level.