Practice Free SAA-C03 Exam Online Questions
A transaction processing company has weekly scripted batch jobs that run on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group. The number of transactions can vary but the beseline CPU utilization that is noted on each run is at least 60%. The company needs to provision the capacity 30 minutes before the jobs run.
Currently engineering complete this task by manually modifying the Auto Scaling group parameters. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. The company needs an automated way to modify the Auto Scaling group’s capacity.
Which solution will meet these requiements with the LEAST operational overhead?
- A . Ceate a dynamic scalling policy for the Auto Scaling group. Configure the policy to scale based on the CPU utilization metric to 60%.
- B . Create a scheduled scaling polcy for the Auto Scaling group. Set the appropriate desired capacity, minimum capacity, and maximum capacity. Set the recurrence to weekly. Set the start time to 30 minutes. Before the batch jobs run.
- C . Create a predictive scaling policy for the Auto Scaling group. Configure the policy to scale based on forecast. Set the scaling metric to CPU utilization. Set the target value for the metric to 60%. In the Policy, set the instances to pre-launch 30 minutes before the jobs run.
- D . Create an Amazon EventBridge event to invoke an AWS Lamda function when the CPU utilization metric value for the Auto Scaling group reaches 60%. Configure the Lambda function to increase the Auto Scaling group’s desired capacity and maximum capacity by 20%.
C
Explanation:
This option is the most efficient because it uses a predictive scaling policy for the Auto Scaling group, which is a type of scaling policy that uses machine learning to predict capacity requirements based on historical data from CloudWatch1. It also configures the policy to scale based on forecast, which enables the Auto Scaling group to adjust its capacity in advance of traffic changes. It also sets the scaling metric to CPU utilization and the target value for the metric to 60%, which aligns with the baseline CPU utilization that is noted on each run. It also sets the instances to pre-launch 30 minutes before the jobs run, which ensures that enough capacity is provisioned before the weekly scripted batch jobs start. This solution meets the requirement of provisioning the capacity 30 minutes before the jobs run with the least operational overhead.
Option A is less efficient because it uses a dynamic scaling policy for the Auto Scaling group, which is a type of scaling policy that adjusts your Auto Scaling group’s capacity in response to changing demand2. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic.
Option B is less efficient because it uses a scheduled scaling policy for the Auto Scaling group, which is a type of scaling policy that lets you scale your Auto Scaling group based on a schedule that you create3. However, this does not provide a way to scale based on forecast or CPU utilization, as it only scales based on predefined metrics and policies.
Option D is less efficient because it uses an Amazon EventBridge event to invoke an AWS Lambda function when the CPU utilization metric value for the Auto Scaling group reaches 60%, which is a way to trigger serverless functions based on events. However, this does not provide a way to provision the capacity 30 minutes before the jobs run, as it only reacts to changing traffic.
A company has developed an API using Amazon API Gateway REST API and AWS Lambda.
How can latency be reduced for users worldwide?
- A . Deploy the REST API as an edge-optimized API endpoint. Enable caching. Enable content encoding to compress data in transit.
- B . Deploy the REST API as a Regional API endpoint. Enable caching. Enable content encoding to compress data in transit.
- C . Deploy the REST API as an edge-optimized API endpoint. Enable caching. Configure reserved concurrency for Lambda functions.
- D . Deploy the REST API as a Regional API endpoint. Enable caching. Configure reserved concurrency for Lambda functions.
A
Explanation:
Edge-optimized API endpoints route requests through CloudFront, reducing latency for global users.
Option A correctly implements edge-optimization, caching, and compression to minimize latency.
Options B and D do not use edge optimization, leading to higher latency for global users.
Reserved concurrency in Options C and D improves backend scaling but does not address global latency directly.
A company is deploying an application that processes streaming data in near-real time. The company plans to use Amazon EC2 instances for the workload. The network architecture must be configurable to provide the lowest possible latency between nodes
Which combination of network solutions will meet these requirements? (Select TWO)
- A . Enable and configure enhanced networking on each EC2 instance
- B . Group the EC2 instances in separate accounts
- C . Run the EC2 instances in a cluster placement group
- D . Attach multiple elastic network interfaces to each EC2 instance
- E . Use Amazon Elastic Block Store (Amazon EBS) optimized instance types.
AC
Explanation:
These options are the most suitable ways to configure the network architecture to provide the lowest possible latency between nodes.
Option A enables and configures enhanced networking on each EC2 instance, which is a feature that improves the network performance of the instance by providing higher bandwidth, lower latency, and lower jitter. Enhanced networking uses single root I/O virtualization (SR-IOV) or Elastic Fabric Adapter (EFA) to provide direct access to the network hardware. You can enable and configure enhanced networking by choosing a supported instance type and a compatible operating system, and installing the required drivers.
Option C runs the EC2 instances in a cluster placement group, which is a logical grouping of instances within a single Availability Zone that are placed close together on the same underlying hardware. Cluster placement groups provide the lowest network latency and the highest network throughput among the placement group options. You can run the EC2 instances in a cluster placement group by creating a placement group and launching the instances into it.
Option B is not suitable because grouping the EC2 instances in separate accounts does not provide the lowest possible latency between nodes. Separate accounts are used to isolate and organize resources for different purposes, such as security, billing, or compliance. However, they do not affect the network performance or proximity of the instances. Moreover, grouping the EC2 instances in separate accounts would incur additional costs and complexity, and it would require setting up cross-account networking and permissions.
Option D is not suitable because attaching multiple elastic network interfaces to each EC2 instance does not provide the lowest possible latency between nodes. Elastic network interfaces are virtual network interfaces that can be attached to EC2 instances to provide additional network capabilities, such as multiple IP addresses, multiple subnets, or enhanced security. However, they do not affect the network performance or proximity of the instances. Moreover, attaching multiple elastic network
interfaces to each EC2 instance would consume additional resources and limit the instance type choices.
Option E is not suitable because using Amazon EBS optimized instance types does not provide the lowest possible latency between nodes. Amazon EBS optimized instance types are instances that provide dedicated bandwidth for Amazon EBS volumes, which are block storage volumes that can be attached to EC2 instances. EBS optimized instance types improve the performance and consistency of the EBS volumes, but they do not affect the network performance or proximity of the instances. Moreover, using EBS optimized instance types would incur additional costs and may not be necessary for the streaming data workload.
Reference: Enhanced networking on Linux
Placement groups
Elastic network interfaces
Amazon EBS-optimized instances
A solutions architect needs to implement a solution that can handle up to 5,000 messages per second. The solution must publish messages as events to multiple consumers. The messages are up to 500 KB in size. The message consumers need to have the ability to use multiple programming languages to consume the messages with minimal latency. The solution must retain published messages for more than 3 months. The solution must enforce strict ordering of the messages.
Which solution will meet these requirements?
- A . Publish messages to an Amazon Kinesis Data Streams data stream. Enable enhanced fan-out.
Ensure that consumers ingest the data stream by using dedicated throughput. - B . Publish messages to an Amazon Simple Notification Service (Amazon SNS) topic. Ensure that consumers use an Amazon Simple Queue Service (Amazon SQS) FIFO queue to subscribe to the topic.
- C . Publish messages to Amazon EventBridge. Allow each consumer to create rules to deliver messages to the consumer’s own target.
- D . Publish messages to an Amazon Simple Notification Service (Amazon SNS) topic. Ensure that consumers use Amazon Data Firehose to subscribe to the topic.
A
Explanation:
A solutions architect is implementing a complex Java application with a MySQL database. The Java application must be deployed on Apache Tomcat and must be highly available.
What should the solutions architect do to meet these requirements?
- A . Deploy the application in AWS Lambda. Configure an Amazon API Gateway API to connect with the Lambda functions.
- B . Deploy the application by using AWS Elastic Beanstalk. Configure a load-balanced environment and a rolling deployment policy.
- C . Migrate the database to Amazon ElastiCache. Configure the ElastiCache security group to allow access from the application.
- D . Launch an Amazon EC2 instance. Install a MySQL server on the EC2 instance. Configure the application on the server. Create an AMI. Use the AMI to create a launch template with an Auto scaling group.
B
Explanation:
AWS Elastic Beanstalk provides an easy and quick way to deploy, manage, and scale applications. It supports a variety of platforms, including Java and Apache Tomcat. By using Elastic Beanstalk, the solutions architect can upload the Java application and configure the environment to run Apache Tomcat.
A company is developing a new machine learning (ML) model solution on AWS. The models are developed as independent microservices that fetch approximately 1 GB of model data from Amazon $3 at startup and load the data into memory. Users access the models through an asynchronous API. Users can send a request or a batch of requests and specify where the results should be sent.
The company provides models to hundreds of users. The usage patterns for the models are irregular. Some models could be unused for days or weeks. Other models could receive batches of thousands of requests at a time.
Which design should a solutions architect recommend to meet these requirements?
- A . Direct the requests from the API to a Network Load Balancer (NLB). Deploy the models as AWS Lambda functions that are invoked by the NLB.
- B . Direct the requests from the API to an Application Load Balancer (ALB). Deploy the models as Amazon Elastic Container Service (Amazon ECS) services that read from an Amazon Simple Queue Service (Amazon SQS) queue. Use AWS App Mesh to scale the instances of the ECS cluster based on the SQS queue size.
- C . Direct the requests from the API into an Amazon Simple Queue Service (Amazon SQS) queue. Deploy the models as AWS Lambda functions that are invoked by SQS events. Use AWS Auto Scaling to increase the number of vCPUs for the Lambda functions based on the SQS queue size.
- D . Direct the requests from the API into an Amazon Simple Queue Service (Amazon SQS) queue. Deploy the models as Amazon Elastic Container Service (Amazon ECS) services that read from the queue. Enable AWS Auto Scaling on Amazon ECS for both the cluster and copies of the service based on the queue size.
D
Explanation:
This answer is correct because it meets the requirements of running the ML models as independent microservices that can handle irregular and unpredictable usage patterns. By directing the requests from the API into an Amazon SQS queue, the company can decouple the request processing from the model execution, and ensure that no requests are lost due to spikes in demand. By deploying the models as Amazon ECS services that read from the queue, the company can leverage containers to isolate and package each model as a microservice, and fetch the model data from S3 at startup. By enabling AWS Auto Scaling on Amazon ECS for both the cluster and copies of the service based on the queue size, the company can automatically scale up or down the number of EC2 instances in the cluster and the number of tasks in each service to match the demand and optimize performance.
Reference:
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html
https://docs.aws.amazon.com/AmazonECS/latest/developerguide/Welcome.html
https://docs.aws.amazon.com/autoscaling/ec2/userguide/autoscaling-ecs.html
A company runs multiple Amazon EC2 Linux instances in a VPC across two Availability Zones. The instances host applications that use a hierarchical directory structure. The applications need to read and write rapidly and concurrently to shared storage.
What should a solutions architect do to meet these requirements?
- A . Create an Amazon S3 bucket. Allow access from all the EC2 instances in the VPC.
- B . Create an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system from each EC2 instance.
- C . Create a file system on a Provisioned IOPS SSD (102) Amazon Elastic Block Store (Amazon EBS) volume. Attach the EBS volume to all the EC2 instances.
- D . Create file systems on Amazon Elastic Block Store (Amazon EBS) volumes that are attached to each EC2 instance. Synchromze the EBS volumes across the different EC2 instances.
B
Explanation:
it allows the EC2 instances to read and write rapidly and concurrently to shared storage across two Availability Zones. Amazon EFS provides a scalable, elastic, and highly available file system that can be mounted from multiple EC2 instances. Amazon EFS supports high levels of throughput and IOPS, and consistent low latencies. Amazon EFS also supports NFSv4 lock upgrading and downgrading, which enables high levels of concurrency.
Reference: Amazon EFS Features
Using Amazon EFS with Amazon EC2
A company stores critical data in Amazon DynamoDB tables in the company’s AWS account. An IT administrator accidentally deleted a DynamoDB table. The deletion caused a significant loss of data and disrupted the company’s operations. The company wants to prevent this type of disruption in the future.
Which solution will meet this requirement with the LEAST operational overhead?
- A . Configure a trail in AWS CloudTrail. Create an Amazon EventBridge rule for delete actions. Create an AWS Lambda function to automatically restore deleted DynamoDB tables.
- B . Create a backup and restore plan for the DynamoDB tables. Recover the DynamoDB table manually.
- C . Configure deletion protection on the DynamoDB tables.
- D . Enable point-in-time recovery on the DynamoDB tables.
C
Explanation:
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 AWS CLI, 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.
Reference: Using deletion protection to protect your table Preventing Accidental Table Deletion in DynamoDB Amazon DynamoDB now supports table deletion protection
A company stores critical data in Amazon DynamoDB tables in the company’s AWS account. An IT administrator accidentally deleted a DynamoDB table. The deletion caused a significant loss of data and disrupted the company’s operations. The company wants to prevent this type of disruption in the future.
Which solution will meet this requirement with the LEAST operational overhead?
- A . Configure a trail in AWS CloudTrail. Create an Amazon EventBridge rule for delete actions. Create an AWS Lambda function to automatically restore deleted DynamoDB tables.
- B . Create a backup and restore plan for the DynamoDB tables. Recover the DynamoDB table manually.
- C . Configure deletion protection on the DynamoDB tables.
- D . Enable point-in-time recovery on the DynamoDB tables.
C
Explanation:
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 AWS CLI, 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.
Reference: Using deletion protection to protect your table Preventing Accidental Table Deletion in DynamoDB Amazon DynamoDB now supports table deletion protection
A solutions architect is investigating compute options for a critical analytics application. The
application uses long-running processes to prepare and aggregate data. The processes cannot be interrupted. The application has a known baseline load. The application needs to handle occasional usage surges.
Which solution will meet these requirements MOST cost-effectively?
- A . Create an Amazon EC2 Auto Scaling group. Set the Min capacity and Desired capacity parameters to the number of instances required to handle the baseline load. Purchase Reserved Instances for the Auto Scaling group.
- B . Create an Amazon EC2 Auto Scaling group. Set the Min capacity, Max capacity, and Desired capacity parameters to the number of instances required to handle the baseline load. Use On-Demand Instances to address occasional usage surges.
- C . Create an Amazon EC2 Auto Scaling group. Set the Min capacity and Desired capacity parameters to the number of instances required to handle the baseline load. Purchase Reserved Instances for the Auto Scaling group. Use the OnDemandPercentageAboveBaseCapacity parameter to configure the launch template to launch Spot Instances.
- D . Re-architect the application to use AWS Lambda functions instead of Amazon EC2 instances.
Purchase a one-year Compute Savings Plan to reduce the cost of Lambda usage.