AWS Certified Solutions Architect - Associate Practice Test

SAA-C03 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

Title: AWS Certified Solutions Architect - Associate (SAA-C03)

Test Detail:
The AWS Certified Solutions Architect - Associate (SAA-C03) exam validates the knowledge and skills required to design and deploy scalable, highly available, and fault-tolerant systems on the Amazon Web Services (AWS) platform. This certification is designed for individuals who work as solutions architects and are responsible for designing and implementing AWS-based applications.

Course Outline:
The AWS Certified Solutions Architect - Associate course provides participants with comprehensive knowledge and hands-on experience in designing and deploying applications on AWS. The following is a general outline of the key areas covered in the certification program:

- Design secure access to AWS resources
- Access controls and management across multiple accounts
- AWS federated access and identity services
- AWS Identity and Access Management [IAM]
- AWS IAM Identity Center
- AWS global infrastructure
- Availability Zones
- AWS Regions
- AWS security best practices
- principle of least privilege
- The AWS shared responsibility model
- Applying AWS security best practices to IAM users and root users
- multi-factor authentication [MFA]
- Designing a flexible authorization model
- IAM users
- groups
- roles
- policies
- Designing a role-based access control strategy
- AWS Security Token Service [AWS STS]
- role switching
- cross-account access
- Designing a security strategy for multiple AWS accounts
- AWS Control Tower
- service control policies [SCPs]
- Determining the appropriate use of resource policies for AWS services
- Determining when to federate a directory service with IAM roles

- Design secure workloads and applications.
- Application configuration and credentials security
- AWS service endpoints
- Control ports, protocols, and network traffic on AWS
- Secure application access
- Security services with appropriate use cases
- Amazon Cognito
- Amazon GuardDuty
- Amazon Macie
- Threat vectors external to AWS
- DDoS
- SQL injection
- Designing VPC architectures with security components
- security groups
- route tables
- network ACLs
- NAT gateways
- Determining network segmentation strategies
- using public subnets
- private subnets
- Integrating AWS services to secure applications
-AWS Shield
- AWS WAF
- IAM Identity Center
- AWS Secrets Manager
- Securing external network connections to and from the AWS Cloud
- VPN
- AWS Direct Connect

- Determine appropriate data security controls
- Data access and governance
- Data recovery
- Data retention and classification
- Encryption and appropriate key management
- Aligning AWS technologies to meet compliance requirements
- Encrypting data at rest
- AWS Key Management Service [AWS KMS]
- Encrypting data in transit
- AWS Certificate Manager [ACM] using TLS
- Implementing access policies for encryption keys
- Implementing data backups and replications
- Implementing policies for data access, lifecycle, and protection
- Rotating encryption keys and renewing certificates

- Design scalable and loosely coupled architectures
- API creation and management
- Amazon API Gateway
- REST API
- AWS managed services with appropriate use cases
- AWS Transfer Family
- Amazon Simple Queue Service [Amazon SQS]
- Secrets Manager
- Caching strategies
- Design principles for microservices
- stateless workloads compared with stateful workloads
- Event-driven architectures
- Horizontal scaling and vertical scaling
- How to appropriately use edge accelerators
- content delivery network [CDN]
- How to migrate applications into containers
- Load balancing concepts
- Application Load Balancer
- Multi-tier architectures
- Queuing and messaging concepts
- publish/subscribe
- Serverless technologies and patterns
- AWS Fargate
- AWS Lambda

- Storage types with associated characteristics
- object
- file
- block
- The orchestration of containers
- Amazon Elastic Container Service [Amazon ECS]
- Amazon Elastic Kubernetes Service [Amazon EKS])
- When to use read replicas
- Workflow orchestration
- AWS Step Functions
- Designing event-driven
- microservice and multi-tier architectures based on requirements
- Determining scaling strategies for components used in an architecture design
- Determining the AWS services required to achieve loose coupling based on requirements
- Determining when to use containers
- Determining when to use serverless technologies and patterns
- Recommending appropriate compute, storage, networking, and database technologies based on requirements
- Using purpose-built AWS services for workloads

- Design highly available and/or fault-tolerant architectures
- AWS global infrastructure
- Availability Zones
- AWS Regions
- Amazon Route 53
- AWS managed services with appropriate use cases
- Amazon Comprehend
- Amazon Polly
- Basic networking concepts
- route tables
- Disaster recovery (DR) strategies
- backup and restore
- pilot light
- warm standby
- active-active failover
- recovery point objective [RPO]
- recovery time objective [RTO])
- Distributed design patterns
- Failover strategies
- Immutable infrastructure
- Load balancing concepts
- Application Load Balancer
- Proxy concepts
- Amazon RDS Proxy

- Service quotas and throttling
- how to configure the service quotas for a workload in a standby environment
- Storage options and characteristics
- durability
- replication
- Workload visibility
- AWS X-Ray
- Determining automation strategies to ensure infrastructure integrity
- Determining the AWS services required to provide a highly available and/or fault-tolerant architecture across AWS Regions or Availability Zones
- Identifying metrics based on business requirements to deliver a highly available solution
- Implementing designs to mitigate single points of failure
- Implementing strategies to ensure the durability and availability of data
- backups
- Selecting an appropriate DR strategy to meet business requirements
- Using AWS services that Improve the reliability of legacy applications and applications not built for the cloud
- when application changes are not possible
- Using purpose-built AWS services for workloads

- Determine high-performing and/or scalable storage solutions
- Hybrid storage solutions to meet business requirements
- Storage services with appropriate use cases
- Amazon S3
- Amazon Elastic File System [Amazon EFS]
- Amazon Elastic Block Store [Amazon EBS]
- Storage types with associated characteristics
- object
- file
- block
- Determining storage services and configurations that meet performance demands
- Determining storage services that can scale to accommodate future needs

- Design high-performing and elastic compute solutions

- AWS compute services with appropriate use cases
- AWS Batch
- Amazon EMR
- Fargate
- Distributed computing concepts supported by AWS global infrastructure and edge services
- Queuing and messaging concepts
- publish/subscribe
- Scalability capabilities with appropriate use cases
- Amazon EC2 Auto Scaling
- AWS Auto Scaling
- Serverless technologies and patterns
- Lambda
- Fargate
- The orchestration of containers
- Amazon ECS
- Amazon EKS
- Decoupling workloads so that components can scale independently
- Identifying metrics and conditions to perform scaling actions
- Selecting the appropriate compute options and features (for example, EC2 instance types) to meet business requirements
- Selecting the appropriate resource type and size (for example, the amount of Lambda memory) to meet business requirements

- Determine high-performing database solutions

- AWS global infrastructure
- Availability Zones
- AWS Regions
- Caching strategies and services
- Amazon ElastiCache
- Data access patterns
- read-intensive compared with write-intensive
- Database capacity planning
- capacity units
- instance types
- Provisioned IOPS
- Database connections and proxies
- Database engines with appropriate use cases
- heterogeneous migrations
- homogeneous migrations
- Database replication
- read replicas
- Database types and services
- serverless
- relational compared with non-relational
- in-memory
- Configuring read replicas to meet business requirements
- Designing database architectures
- Determining an appropriate database engine
- MySQL compared with PostgreSQL
- Determining an appropriate database type
- Amazon Aurora
- Amazon DynamoDB
- Integrating caching to meet business requirements

- Determine high-performing and/or scalable network architectures

- Edge networking services with appropriate use cases
- Amazon CloudFront
- AWS Global Accelerator
- How to design network architecture
- subnet tiers
- routing, IP addressing
- Load balancing concepts
- Application Load Balancer
- Network connection options
- AWS VPN
- Direct Connect
- AWS PrivateLink
- Creating a network topology for various architectures
- global
- hybrid
- multi-tier
- Determining network configurations that can scale to accommodate future needs
- Determining the appropriate placement of resources to meet business requirements
- Selecting the appropriate load balancing strategy

- High-performing data ingestion and transformation solutions

- Data analytics and visualization services with appropriate use cases
- Amazon Athena
- AWS Lake Formation
- Amazon QuickSight
- Data ingestion patterns
- frequency
- Data transfer services with appropriate use cases
- AWS DataSync
- AWS Storage Gateway
- Data transformation services with appropriate use cases
- AWS Glue
- Secure access to ingestion access points
- Sizes and speeds needed to meet business requirements
- Streaming data services with appropriate use cases
- Amazon Kinesis
- Building and securing data lakes
- Designing data streaming architectures
- Designing data transfer solutions
- Implementing visualization strategies
- Selecting appropriate compute options for data processing
- Amazon EMR
- Selecting appropriate configurations for ingestion
- Transforming data between formats
.csv to .parquet

- Design cost-optimized storage solutions

- Access options
- an S3 bucket with Requester Pays object storage
- AWS cost management service features
- cost allocation tags
- multi-account billing
- AWS cost management tools with appropriate use cases
- AWS Cost Explorer
- AWS Budgets
- AWS Cost
- Usage Report
- AWS storage services with appropriate use cases
- Amazon FSx
- Amazon EFS
- Amazon S3
- Amazon EBS
- Backup strategies
- Block storage options
- hard disk drive [HDD] volume types
- solid state drive [SSD] volume types
- Data lifecycles
- Hybrid storage options
- DataSync
- Transfer Family
- Storage Gateway
- Storage access patterns
- Storage tiering
- cold tiering for object storage
- Storage types with associated characteristics
- object
- file
- block
- Designing appropriate storage strategies
- batch uploads to Amazon S3 compared with individual uploads
- Determining the correct storage size for a workload
- Determining the lowest cost method of transferring data for a workload to AWS storage
- Determining when storage auto scaling is required
- Managing S3 object lifecycles
- Selecting the appropriate backup and/or archival solution
- Selecting the appropriate service for data migration to storage services
- Selecting the appropriate storage tier
- Selecting the correct data lifecycle for storage
- Selecting the most cost-effective storage service for a workload

- Design cost-optimized compute solutions
- AWS cost management service features
- cost allocation tags
- multi-account billing
- AWS cost management tools with appropriate use cases
- Cost Explorer
- AWS Budgets
- AWS Cost
- Usage Report
- AWS global infrastructure
- Availability Zones
- AWS Regions
- AWS purchasing options
- Spot Instances
- Reserved Instances
- Savings Plans
- Distributed compute strategies
- edge processing
- Hybrid compute options
- AWS Outposts
- AWS Snowball Edge
- Instance types, families, and sizes
- memory optimized
- compute optimized
- virtualization
- Optimization of compute utilization
- containers
- serverless computing
- microservices
- Scaling strategies
- auto scaling
- hibernation
- Determining an appropriate load balancing strategy
- Application Load Balancer [Layer 7] compared with Network Load Balancer [Layer 4] compared with Gateway Load Balancer
- Determining appropriate scaling methods and strategies for elastic workloads
- horizontal compared with vertical
- EC2 hibernation
- Determining cost-effective AWS compute services with appropriate use cases
- Lambda
- Amazon EC2
- Fargate
- Determining the required availability for different classes of workloads
- production workloads
- non-production workloads
- Selecting the appropriate instance family for a workload
- Selecting the appropriate instance size for a workload


- Design cost-optimized database solutions
- AWS cost management service features
- cost allocation tags
- multi-account billing
- AWS cost management tools with appropriate use cases
- Cost Explorer
- AWS Budgets
- AWS Cost and Usage Report
- Caching strategies
- Data retention policies
- Database capacity planning
- capacity units
- Database connections and proxies
- Database engines with appropriate use cases
- heterogeneous migrations
- homogeneous migrations
- Database replication
- read replicas
- Database types and services
- relational compared with non-relational
- Aurora
- DynamoDB

- Designing appropriate backup and retention policies
- snapshot frequency
- Determining an appropriate database engine
- MySQL compared with PostgreSQL
- Determining cost-effective AWS database services with appropriate use cases
- DynamoDB compared with Amazon RDS
- serverless
- Determining cost-effective AWS database types
- time series format
- columnar format
- Migrating database schemas and data to different locations and/or different database engines


- Design cost-optimized database solutions
- AWS cost management service features
- cost allocation tags
- multi-account billing
- AWS cost management tools with appropriate use cases
- Cost Explorer
- AWS Budgets
- AWS Cost and Usage Report
- Load balancing concepts
- Application Load Balancer
- NAT gateways
- NAT instance costs compared with NAT gateway costs
- Network connectivity
- private lines
- dedicated lines
- VPNs
- Network routing, topology, and peering
- AWS Transit Gateway
- VPC peering
- Network services with appropriate use cases
- DNS

- Configuring appropriate NAT gateway types for a network
- a single shared NAT gateway compared with NAT gateways for each Availability Zone
- Configuring appropriate network connections
- Direct Connect compared with VPN compared with internet
- Configuring appropriate network routes to minimize network transfer costs
- Region to Region
- Availability Zone to Availability Zone
- private to public
- Global Accelerator
- VPC endpoints
- Determining strategic needs for content delivery networks (CDNs) and edge caching
- Reviewing existing workloads for network optimizations
- Selecting an appropriate throttling strategy
- Selecting the appropriate bandwidth allocation for a network device
- a single VPN compared with multiple VPNs
- Direct Connect speed

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SAA-C03
AWS Certified Solutions Architect - Associate
https://killexams.com/pass4sure/exam-detail/SAA-C03
Question: 84
A Solutions Architect is building a cloud infrastructure where EC2 instances require access to various AWS services such as S3 and Redshift. The Architect will also need to provide access to system
administrators so they can deploy and test their changes.
Which configuration should be used to ensure that the access to the resources is secured and not compromised? (Select TWO.)
1. Store the AWS Access Keys in the EC2 instance.
2. Assign an IAM role to the Amazon EC2 instance.
3. Store the AWS Access Keys in AC
1. Enable Multi-Factor Authentication.
2. Assign an IAM user for each Amazon EC2 Instance.
Answer: B,D Explanation:
In this scenario, the correct answers are:
* Enable Multi-Factor Authentication
* Assign an IAM role to the Amazon EC2 instance
Always remember that you should associate IAM roles to EC2 instances and not an IAM user, for the purpose of accessing other AWS services. IAM roles are designed so that your applications can securely make API requests from your instances, without requiring you to manage the security credentials that the applications use. Instead of creating and distributing your AWS credentials, you can delegate permission to make API requests using IAM roles.
AWS Multi-Factor Authentication (MFA) is a simple best practice that adds an extra layer of protection on top of your user name and password. With MFA enabled, when a user signs in to an AWS website, they will be prompted for their user name and password (the first factor-what they know), as well as for an authentication code from their AWS MFA device (the second factor-what they have). Taken together, these multiple factors provide increased security for your AWS account settings and resources. You can enable MFA for your AWS account and for individual IAM users you have created under your account. MFA can also be used to control access to AWS service APIs.
Storing the AWS Access Keys in the EC2 instance is incorrect. This is not recommended by AWS as it can be compromised. Instead of storing access keys on an EC2 instance for use by applications that run on the instance and make AWS API requests, you can use an IAM role to provide temporary access keys for these applications.
Assigning an IAM user for each Amazon EC2 Instance is incorrect because there is no need to create an IAM user for this scenario since IAM roles already provide greater flexibility and easier management. Storing the AWS Access Keys in ACM is incorrect because ACM is just a service that lets you easily provision, manage, and deploy public and private SSL/TLS certificates for use with AWS services and your internal connected resources. It is not used as a secure storage for your access keys. References:
https://aws.amazon.com/iam/details/mfa/ https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html Check out this AWS IAM Cheat Sheet:
https://tutorialsdojo.com/aws-identity-and-access-management-iam/
Question: 85
A company needs to deploy at least 2 EC2 instances to support the normal workloads of its application and automatically scale up to 6 EC2 instances to handle the peak load. The architecture must be highly available and fault- tolerant as it is processing mission-critical workloads.
As the Solutions Architect of the company, what should you do to meet the above requirement?
1. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 4. Deploy 2 instances in Availability Zone A and 2 instances in Availability Zone B.
2. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 4 and the maximum capacity to 6. Deploy 2 instances in Availability Zone A and another 2 instances in Availability Zone B.
3. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Deploy 4 instances in Availability Zone A.
4. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Use 2 Availability Zones and deploy 1 instance for each AZ.
Answer: B Explanation:
Amazon EC2 Auto Scaling helps ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You create collections of EC2 instances, called Auto Scaling groups. You can specify the minimum number of instances in each Auto Scaling group, and Amazon EC2 Auto Scaling ensures that your group never goes below this size. You can also specify the maximum number of instances in each Auto Scaling group, and Amazon EC2 Auto Scaling ensures that your group never goes above this size.
To achieve highly available and fault-tolerant architecture for your applications, you must deploy all your instances in different Availability Zones. This will help you isolate your resources if an outage occurs. Take note that to achieve fault tolerance, you need to have redundant resources in place to avoid any system degradation in the event of a server fault or an Availability Zone outage. Having a fault-tolerant architecture entails an extra cost in running additional resources than what is usually needed. This is to ensure that the mission-critical workloads are processed.
Since the scenario requires at least 2 instances to handle regular traffic, you should have 2 instances running all the time even if an AZ outage occurred. You can use an Auto Scaling Group to automatically scale your compute resources across two or more Availability Zones. You have to specify the minimum capacity to 4 instances and the maximum capacity to 6 instances. If each AZ has 2 instances running, even if an AZ fails, your system will still run a minimum of 2 instances.
Hence, the correct answer in this scenario is: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 4 and the maximum capacity to 6. Deploy 2 instances in Availability Zone A and another 2 instances in
Availability Zone B.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Deploy 4 instances in Availability Zone A is incorrect because the instances are only deployed in a single Availability Zone. It cannot protect your applications and data from datacenter or AZ failures.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Use 2 Availability Zones and deploy 1 instance for each AZ is incorrect. It is required to have 2 instances running all the time. If an AZ outage happened, ASG will launch a new
instance on the unaffected AZ. This provisioning does not happen instantly, which means that for a certain period of time, there will only be 1 running instance left.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 4. Deploy 2 instances in Availability Zone A and 2 instances in Availability Zone B is incorrect. Although this fulfills the requirement of at least 2 EC2 instances and high availability, the maximum capacity setting is wrong. It should be set to 6 to properly handle the peak load. If an AZ outage occurs and the system is at its peak load, the number of running instances in this setup will only be 4 instead of 6 and this will affect the performance of your application. References:
https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html https://docs.aws.amazon.com/documentdb/latest/developerguide/regions-and-azs.html
Check out this AWS Auto Scaling Cheat Sheet: https://tutorialsdojo.com/aws-auto-scaling/ Question: 86
A company is using Amazon S3 to store frequently accessed data. When an object is created or deleted, the S3 bucket will send an event notification to the Amazon SQS queue. A solutions architect needs to create a solution that will notify the development and operations team about the created or deleted objects.
Which of the following would satisfy this requirement?
1. Create an Amazon SNS subject and configure two Amazon SQS queues to subscribe to the topic. Grant Amazon S3 permission to send notifications to Amazon SNS and update the bucket to use the new SNS topic.
2. Create a new Amazon SNS FIFO subject for the other team. Grant Amazon S3 permission to send the notification to the second SNS topic.
3. Set up an Amazon SNS subject and configure two Amazon SQS queues to poll the SNS topic. Grant Amazon S3 permission to send notifications to Amazon SNS and update the bucket to use the new SNS topic.
4. Set up another Amazon SQS queue for the other team. Grant Amazon S3 permission to send a notification to the second SQS queue.
Answer: A Explanation:
The Amazon S3 notification feature enables you to receive notifications when certain events happen in your bucket. To enable notifications, you must first add a notification configuration that identifies the events you want Amazon S3 to publish and the destinations where you want Amazon S3 to send the notifications. You store this configuration in the notification subresource that is associated with a bucket. Amazon S3 supports the following destinations where it can
publish events:
* Amazon Simple Notification Service (Amazon SNS) topic
* Amazon Simple Queue Service (Amazon SQS) queue
* AWS Lambda
In Amazon SNS, the fanout scenario is when a message published to an SNS subject is replicated and pushed to multiple endpoints, such as Amazon SQS queues, HTTP(S) endpoints, and Lambda functions. This allows for parallel asynchronous processing.
For example, you can develop an application that publishes a message to an SNS subject whenever an order is placed for a product. Then, SQS queues that are subscribed to the SNS subject receive identical notifications for the new order. An Amazon Elastic Compute Cloud (Amazon EC2) server instance attached to one of the SQS queues can handle the processing or fulfillment of the order. And you can attach another Amazon EC2 server instance to a data warehouse for analysis of all orders received. Based on the given scenario, the existing setup sends the event notification to an SQS queue. Since you need to send the notification to the development and operations team, you can use a combination of Amazon SNS and SQS. By using the message fanout pattern, you can create a subject and use two Amazon SQS queues to subscribe to the topic. If Amazon SNS receives an event notification, it will
publish the message to both subscribers.
Take note that Amazon S3 event notifications are designed to be delivered at least once and to one destination only. You cannot attach two or more SNS subjects or SQS queues for S3 event notification. Therefore, you must send the event notification to Amazon SNS.
Hence, the correct answer is: Create an Amazon SNS subject and configure two Amazon SQS queues to subscribe to the topic. Grant Amazon S3 permission to send notifications to Amazon SNS and update the bucket to use the new SNS topic.
The option that says: Set up another Amazon SQS queue for the other team. Grant Amazon S3 permission to send a notification to the second SQS queue is incorrect because you can only add 1 SQS or SNS at a time for Amazon S3 events notification. If you need to send the events to multiple subscribers, you should implement a message fanout pattern with Amazon SNS and Amazon SQS.
The option that says: Create a new Amazon SNS FIFO subject for the other team. Grant Amazon S3 permission to send the notification to the second SNS subject is incorrect. Just as mentioned in the previous option, you can only add 1 SQS or SNS at a time for Amazon S3 events notification. In addition, neither Amazon SNS FIFO subject nor Amazon SQS FIFO queue is warranted in this scenario. Both of them can be used together to provide strict message ordering and
message deduplication. The FIFO capabilities of each of these services work together to act as a fully managed service to integrate distributed applications that require data consistency in near-real-time.
The option that says: Set up an Amazon SNS subject and configure two Amazon SQS queues to poll the SNS topic. Grant Amazon S3 permission to send notifications to Amazon SNS and update the bucket to use the new SNS subject is incorrect because you can�t poll Amazon SNS. Instead of configuring queues to poll Amazon SNS, you should configure each Amazon SQS queue to subscribe to the SNS topic. References:
https://docs.aws.amazon.com/AmazonS3/latest/dev/ways-to-add-notification-config-to-bucket.html https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html#notification-how-to-overvie w
https://docs.aws.amazon.com/sns/latest/dg/welcome.html Check out this Amazon S3 Cheat Sheet: https://tutorialsdojo.com/amazon-s3/
Amazon SNS Overview: https://youtu.be/ft5R45lEUJ8 Question: 87
An accounting application uses an RDS database configured with Multi-AZ deployments to Improve availability .
What would happen to RDS if the primary database instance fails?
1. The IP address of the primary DB instance is switched to the standby DB instance.
2. The primary database instance will reboot.
3. A new database instance is created in the standby Availability Zone.
4. The canonical name record (CNAME) is switched from the primary to standby instance.
Answer: D Explanation:
In Amazon RDS, failover is automatically handled so that you can resume database operations as quickly as possible without administrative intervention in the event that your primary database instance goes down. When failing over, Amazon RDS simply flips the canonical name record (CNAME) for your DB instance to point at the standby, which is in turn promoted to become the new primary.
The option that says: The IP address of the primary DB instance is switched to the standby DB instance is incorrect since IP addresses are per subnet, and subnets cannot span multiple AZs.
The option that says: The primary database instance will reboot is incorrect since in the event of a failure, there is no database to reboot with.
The option that says: A new database instance is created in the standby Availability Zone is incorrect since with multi-AZ enabled, you already have a standby database in another AZ.
References: https://aws.amazon.com/rds/details/multi-az/ https://aws.amazon.com/rds/faqs/
Amazon RDS Overview: https://youtu.be/aZmpLl8K1UU
Check out this Amazon RDS Cheat Sheet: https://tutorialsdojo.com/amazon-relational-database-service-amazon-rds/ Question: 88
A car dealership website hosted in Amazon EC2 stores car listings in an Amazon Aurora database managed by Amazon RDS. Once a vehicle has been sold, its data must be removed from the current listings and forwarded to a distributed processing system.
Which of the following options can satisfy the given requirement?
1. Create an RDS event subscription and send the notifications to Amazon SQ
2. Configure the SQS queues to fan out the event notifications to multiple Amazon SNS topics. Process the data using Lambda functions.
3. Create an RDS event subscription and send the notifications to AWS Lambda. Configure the Lambda function to fan out the event notifications to multiple Amazon SQS queues to update the processing system.
4. Create a native function or a stored procedure that invokes a Lambda function. Configure the Lambda function to send event notifications to an Amazon SQS queue for the processing system to consume.
5. Create an RDS event subscription and send the notifications to Amazon SN
6. Configure the SNS subject to fan out the event notifications to multiple Amazon SQS queues. Process the data using Lambda
functions.
Answer: C Explanation:
You can invoke an AWS Lambda function from an Amazon Aurora MySQL-Compatible Edition DB cluster with a native function or a stored procedure. This approach can be useful when you want to integrate your database running on Aurora MySQL with other AWS services. For example, you might want to capture data changes whenever a row in a table is modified in your database.
In the scenario, you can trigger a Lambda function whenever a listing is deleted from the database. You can then write the logic of the function to send the listing data to an SQS queue and have different processes consume it.
Hence, the correct answer is: Create a native function or a stored procedure that invokes a Lambda function. Configure the Lambda function to send event notifications to an Amazon SQS queue for the processing system to consume.
RDS events only provide operational events such as DB instance events, DB parameter group events, DB security group events, and DB snapshot events .
What we need in the scenario is to capture data-modifying events (INSERT, DELETE, UPDATE) which can be achieved thru native functions or stored procedures. Hence, the following options are incorrect:
* Create an RDS event subscription and send the notifications to Amazon SQS. Configure the SQS queues to fan out the event notifications to multiple Amazon SNS topics. Process the data using Lambda functions.
* Create an RDS event subscription and send the notifications to AWS Lambda. Configure the Lambda function to fan out the event notifications to multiple Amazon SQS queues to update the processing system.
* Create an RDS event subscription and send the notifications to Amazon SNS. Configure the SNS subject to fan out the event notifications to multiple Amazon SQS queues. Process the data using Lambda functions.
References: https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Integrating.Lambda.h tml
https://aws.amazon.com/blogs/database/capturing-data-changes-in-amazon-aurora-using-aws-lambda/ Amazon Aurora
Overview:
https://youtu.be/iwS1h7rLNBQ
Check out this Amazon Aurora Cheat Sheet: https://tutorialsdojo.com/amazon-aurora/

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