Amazon AWS SAA-C03 Elastic Scaling, Load Balancing, Containers, Serverless, and Microservices Practice Test 1

 

Topic 08 Practice Test 1 covers Elastic Scaling, Load Balancing, Containers, Serverless, and Microservices for AWS SAA-C03. For broader exam preparation, review the AWS SAA-C03 Exam Dumps. Every option includes focused technical reasoning explaining both the AWS architecture concept and its fit to the scenario.

Question 1

A web tier sees irregular traffic throughout the day. The requirement is to adjust desired capacity automatically from a target metric. Which design choice best satisfies this requirement? Choose ONE.

  1. EC2 Auto Scaling scheduled scaling
  2. EC2 Auto Scaling predictive scaling
  3. EC2 Auto Scaling target tracking
  4. Auto Scaling lifecycle hook

Correct Answer(s)

 

C

Rationale

  1. EC2 Auto Scaling scheduled scaling gives time based capacity changes at known for request load varies unpredictably and the fleet should hold average CPU near a target. The requested action is adjust desired capacity automatically from a target metric. For this design, EC2 Auto Scaling scheduled scaling targets another requirement.
  2. EC2 Auto Scaling predictive scaling delivers forecast based capacity planning from historical for request load varies unpredictably and the fleet should hold average CPU near a target. The requested action is adjust desired capacity automatically from a target metric. In this decision, EC2 Auto Scaling predictive scaling solves a different problem.
  3. EC2 Auto Scaling target tracking creates metric driven desired capacity adjustment for request load varies unpredictably and the fleet should hold average CPU near a target. The requested action is adjust desired capacity automatically from a target metric. Because the evidence aligns with EC2 Auto Scaling target tracking, it aligns with this constraint.
  4. Auto Scaling lifecycle hook functions as pause instance launch or termination transitions for request load varies unpredictably and the fleet should hold average CPU near a target. The requested action is adjust desired capacity automatically from a target metric. For the stated goal, Auto Scaling lifecycle hook does not fit here.

 

Question 2

An internal portal follows a highly predictable weekday schedule. The requirement is to raise desired capacity shortly before the fixed daily surge. Which design choice best satisfies this requirement? Choose ONE.

  1. EC2 Auto Scaling scheduled scaling
  2. EC2 Auto Scaling target tracking
  3. EC2 Auto Scaling predictive scaling
  4. Auto Scaling warm pool

Correct Answer(s)

 

A

Rationale

  1. EC2 Auto Scaling scheduled scaling controls time based capacity changes at known for traffic always jumps at 08:00 on business days and the start time is known. The requested action is raise desired capacity shortly before the fixed daily surge. Because the evidence aligns with EC2 Auto Scaling scheduled scaling, it resolves the stated need.
  2. EC2 Auto Scaling target tracking supplies metric driven desired capacity adjustment for traffic always jumps at 08:00 on business days and the start time is known. The requested action is raise desired capacity shortly before the fixed daily surge. With the stated need, EC2 Auto Scaling target tracking addresses another layer.
  3. EC2 Auto Scaling predictive scaling establishes forecast based capacity planning from historical for traffic always jumps at 08:00 on business days and the start time is known. The requested action is raise desired capacity shortly before the fixed daily surge. With that requirement, EC2 Auto Scaling predictive scaling fails this constraint.
  4. Auto Scaling warm pool handles pre initialized instances kept near ready for traffic always jumps at 08:00 on business days and the start time is known. The requested action is raise desired capacity shortly before the fixed daily surge. For this operating model, Auto Scaling warm pool does not resolve it.

 

Question 3

A consumer site has cyclical traffic with meaningful history. The requirement is to forecast future capacity needs from repeating load patterns. Which design choice best satisfies this requirement? Choose ONE.

  1. EC2 Auto Scaling target tracking
  2. EC2 Auto Scaling scheduled scaling
  3. Auto Scaling warm pool
  4. EC2 Auto Scaling predictive scaling

Correct Answer(s)

 

D

Rationale

  1. EC2 Auto Scaling target tracking applies metric driven desired capacity adjustment for demand follows recurring daily patterns but exact magnitude changes and historical data is useful. The requested action is forecast future capacity needs from repeating load patterns. Under that constraint, EC2 Auto Scaling target tracking serves another purpose.
  2. EC2 Auto Scaling scheduled scaling provides time based capacity changes at known for demand follows recurring daily patterns but exact magnitude changes and historical data is useful. The requested action is forecast future capacity needs from repeating load patterns. With this evidence, EC2 Auto Scaling scheduled scaling changes another mechanism.
  3. Auto Scaling warm pool acts as pre initialized instances kept near ready for demand follows recurring daily patterns but exact magnitude changes and historical data is useful. The requested action is forecast future capacity needs from repeating load patterns. Under this evidence, Auto Scaling warm pool does not match.
  4. EC2 Auto Scaling predictive scaling serves as forecast based capacity planning from historical for demand follows recurring daily patterns but exact magnitude changes and historical data is useful. The requested action is forecast future capacity needs from repeating load patterns. Because the evidence aligns with EC2 Auto Scaling predictive scaling, it fits this case.

 

Question 4

A stateless application currently runs its whole fleet in one zone. The requirement is to place Auto Scaling capacity in subnets from multiple Availability Zones. Which design choice best satisfies this requirement? Choose ONE.

  1. EC2 Auto Scaling target tracking
  2. Multi-AZ Auto Scaling group
  3. ECS Availability Zone spread placement
  4. ALB target groups and health checks

Correct Answer(s)

 

B

Rationale

  1. EC2 Auto Scaling target tracking delivers metric driven desired capacity adjustment for an EC2 application must keep serving if one Availability Zone loses its instances. The requested action is place Auto Scaling capacity in subnets from multiple Availability Zones. Under these conditions, EC2 Auto Scaling target tracking is not the remedy.
  2. Multi-AZ Auto Scaling group gives instance distribution across multiple Availability Zones for an EC2 application must keep serving if one Availability Zone loses its instances. The requested action is place Auto Scaling capacity in subnets from multiple Availability Zones. Because the evidence aligns with Multi-AZ Auto Scaling group, it matches the stated evidence.
  3. ECS Availability Zone spread placement functions as task placement distributed across fault domains for an EC2 application must keep serving if one Availability Zone loses its instances. The requested action is place Auto Scaling capacity in subnets from multiple Availability Zones. For the required behavior, ECS Availability Zone spread placement leaves the issue intact.
  4. ALB target groups and health checks supports Layer 7 distribution to healthy application for an EC2 application must keep serving if one Availability Zone loses its instances. The requested action is place Auto Scaling capacity in subnets from multiple Availability Zones. In this architecture, ALB target groups and health checks does not satisfy it.

 

Question 5

A fleet can remain running at the EC2 layer while the application process is broken. The requirement is to use load-balancer health information with Auto Scaling replacement. Which design choice best satisfies this requirement? Choose ONE.

  1. Auto Scaling health-check replacement
  2. ALB target groups and health checks
  3. Auto Scaling lifecycle hook
  4. EC2 Auto Scaling target tracking

Correct Answer(s)

 

A

Rationale

  1. Auto Scaling health-check replacement supplies automatic replacement of unhealthy group instances for instances that fail load-balancer health checks should be removed and replaced without operator action. The requested action is use load-balancer health information with Auto Scaling replacement. Because the evidence aligns with Auto Scaling health-check replacement, it meets the operating need.
  2. ALB target groups and health checks establishes Layer 7 distribution to healthy application for instances that fail load-balancer health checks should be removed and replaced without operator action. The requested action is use load-balancer health information with Auto Scaling replacement. Under the scenario, ALB target groups and health checks operates elsewhere.
  3. Auto Scaling lifecycle hook handles pause instance launch or termination transitions for instances that fail load-balancer health checks should be removed and replaced without operator action. The requested action is use load-balancer health information with Auto Scaling replacement. For this workload, Auto Scaling lifecycle hook is the wrong control.
  4. EC2 Auto Scaling target tracking offers metric driven desired capacity adjustment for instances that fail load-balancer health checks should be removed and replaced without operator action. The requested action is use load-balancer health information with Auto Scaling replacement. In the requested path, EC2 Auto Scaling target tracking cannot meet this need.

 

Question 6

A fleet requires external configuration during each launch. The requirement is to pause launch so automation can finish registration before continuing. Which design choice best satisfies this requirement? Choose ONE.

  1. Auto Scaling health-check replacement
  2. Auto Scaling warm pool
  3. Auto Scaling lifecycle hook
  4. EC2 Auto Scaling target tracking

Correct Answer(s)

 

C

Rationale

  1. Auto Scaling health-check replacement provides automatic replacement of unhealthy group instances for new instances need a bootstrap registration step before they enter service. The requested action is pause launch so automation can finish registration before continuing. Given this case, Auto Scaling health-check replacement misses this case.
  2. Auto Scaling warm pool acts as pre initialized instances kept near ready for new instances need a bootstrap registration step before they enter service. The requested action is pause launch so automation can finish registration before continuing. For this design, Auto Scaling warm pool targets another requirement.
  3. Auto Scaling lifecycle hook applies pause instance launch or termination transitions for new instances need a bootstrap registration step before they enter service. The requested action is pause launch so automation can finish registration before continuing. Because the evidence aligns with Auto Scaling lifecycle hook, it addresses this requirement.
  4. EC2 Auto Scaling target tracking enables metric driven desired capacity adjustment for new instances need a bootstrap registration step before they enter service. The requested action is pause launch so automation can finish registration before continuing. In this decision, EC2 Auto Scaling target tracking solves a different problem.

 

Question 7

A legacy service cannot meet scaling latency with cold launches. The requirement is to keep pre-initialized EC2 instances in a warm pool. Which design choice best satisfies this requirement? Choose ONE.

  1. EC2 Auto Scaling scheduled scaling
  2. Auto Scaling warm pool
  3. Auto Scaling lifecycle hook
  4. Lambda provisioned concurrency

Correct Answer(s)

 

B

Rationale

  1. EC2 Auto Scaling scheduled scaling functions as time based capacity changes at known for instance boot and application initialization take many minutes during sudden scale-out. The requested action is keep pre-initialized EC2 instances in a warm pool. For the stated goal, EC2 Auto Scaling scheduled scaling does not fit here.
  2. Auto Scaling warm pool delivers pre initialized instances kept near ready for instance boot and application initialization take many minutes during sudden scale-out. The requested action is keep pre-initialized EC2 instances in a warm pool. Because the evidence aligns with Auto Scaling warm pool, it is the direct fit.
  3. Auto Scaling lifecycle hook supports pause instance launch or termination transitions for instance boot and application initialization take many minutes during sudden scale-out. The requested action is keep pre-initialized EC2 instances in a warm pool. With the stated need, Auto Scaling lifecycle hook addresses another layer.
  4. Lambda provisioned concurrency implements pre initialized execution environments for low for instance boot and application initialization take many minutes during sudden scale-out. The requested action is keep pre-initialized EC2 instances in a warm pool. With that requirement, Lambda provisioned concurrency fails this constraint.

 

Question 8

A web service exposes a dedicated readiness URL. The requirement is to use an Application Load Balancer target group with health checks. Which design choice best satisfies this requirement? Choose ONE.

  1. Network Load Balancer
  2. Auto Scaling health-check replacement
  3. API Gateway with Lambda
  4. ALB target groups and health checks

Correct Answer(s)

 

D

Rationale

  1. Network Load Balancer handles Layer 4 distribution for TCP or for HTTP requests must be sent only to targets that pass an application health endpoint. The requested action is use an Application Load Balancer target group with health checks. For this operating model, Network Load Balancer does not resolve it.
  2. Auto Scaling health-check replacement offers automatic replacement of unhealthy group instances for HTTP requests must be sent only to targets that pass an application health endpoint. The requested action is use an Application Load Balancer target group with health checks. Under that constraint, Auto Scaling health-check replacement serves another purpose.
  3. API Gateway with Lambda creates managed request front door for serverless for HTTP requests must be sent only to targets that pass an application health endpoint. The requested action is use an Application Load Balancer target group with health checks. With this evidence, API Gateway with Lambda changes another mechanism.
  4. ALB target groups and health checks establishes Layer 7 distribution to healthy application for HTTP requests must be sent only to targets that pass an application health endpoint. The requested action is use an Application Load Balancer target group with health checks. Because the evidence aligns with ALB target groups and health checks, it satisfies this design.

 

Question 9

A telemetry collector receives high-rate UDP datagrams. The requirement is to place the service behind a Network Load Balancer with the appropriate listener. Which design choice best satisfies this requirement? Choose ONE.

  1. ALB target groups and health checks
  2. API Gateway with Lambda
  3. AWS Cloud Map service discovery
  4. Network Load Balancer

Correct Answer(s)

 

D

Rationale

  1. ALB target groups and health checks acts as Layer 7 distribution to healthy application for a scalable service uses UDP and requires load balancing at the transport layer. The requested action is place the service behind a Network Load Balancer with the appropriate listener. Under this evidence, ALB target groups and health checks does not match.
  2. API Gateway with Lambda enables managed request front door for serverless for a scalable service uses UDP and requires load balancing at the transport layer. The requested action is place the service behind a Network Load Balancer with the appropriate listener. Under these conditions, API Gateway with Lambda is not the remedy.
  3. AWS Cloud Map service discovery controls dynamic naming and discovery for service for a scalable service uses UDP and requires load balancing at the transport layer. The requested action is place the service behind a Network Load Balancer with the appropriate listener. For the required behavior, AWS Cloud Map service discovery leaves the issue intact.
  4. Network Load Balancer provides Layer 4 distribution for TCP or for a scalable service uses UDP and requires load balancing at the transport layer. The requested action is place the service behind a Network Load Balancer with the appropriate listener. Because the evidence aligns with Network Load Balancer, it aligns with this constraint.

 

Question 10

A containerized API experiences variable request volume. The requirement is to configure Application Auto Scaling for the ECS service desired count. Which design choice best satisfies this requirement? Choose ONE.

  1. ECS capacity provider with managed scaling
  2. ECS Service Auto Scaling
  3. AWS Fargate
  4. EC2 Auto Scaling target tracking

Correct Answer(s)

 

B

Rationale

  1. ECS capacity provider with managed scaling supports container demand linked to EC2 cluster for an ECS service should add or remove tasks as service CPU utilization changes. The requested action is configure Application Auto Scaling for the ECS service desired count. In this architecture, ECS capacity provider with managed scaling does not satisfy it.
  2. ECS Service Auto Scaling functions as automatic changes to ECS desired task for an ECS service should add or remove tasks as service CPU utilization changes. The requested action is configure Application Auto Scaling for the ECS service desired count. Because the evidence aligns with ECS Service Auto Scaling, it resolves the stated need.
  3. AWS Fargate implements serverless compute capacity for containers for an ECS service should add or remove tasks as service CPU utilization changes. The requested action is configure Application Auto Scaling for the ECS service desired count. Under the scenario, AWS Fargate operates elsewhere.
  4. EC2 Auto Scaling target tracking serves as metric driven desired capacity adjustment for an ECS service should add or remove tasks as service CPU utilization changes. The requested action is configure Application Auto Scaling for the ECS service desired count. For this workload, EC2 Auto Scaling target tracking is the wrong control.

 

Question 11

A team runs ECS on EC2 and wants cluster capacity to follow task demand. The requirement is to use a capacity provider that can scale the backing Auto Scaling group. Which design choice best satisfies this requirement? Choose ONE.

  1. ECS capacity provider with managed scaling
  2. ECS Service Auto Scaling
  3. AWS Fargate
  4. ECS Availability Zone spread placement

Correct Answer(s)

 

A

Rationale

  1. ECS capacity provider with managed scaling handles container demand linked to EC2 cluster for ECS task count grows but the EC2 cluster frequently lacks host capacity for placement. The requested action is use a capacity provider that can scale the backing Auto Scaling group. Because the evidence aligns with ECS capacity provider with managed scaling, it fits this case.
  2. ECS Service Auto Scaling offers automatic changes to ECS desired task for ECS task count grows but the EC2 cluster frequently lacks host capacity for placement. The requested action is use a capacity provider that can scale the backing Auto Scaling group. In the requested path, ECS Service Auto Scaling cannot meet this need.
  3. AWS Fargate creates serverless compute capacity for containers for ECS task count grows but the EC2 cluster frequently lacks host capacity for placement. The requested action is use a capacity provider that can scale the backing Auto Scaling group. Given this case, AWS Fargate misses this case.
  4. ECS Availability Zone spread placement gives task placement distributed across fault domains for ECS task count grows but the EC2 cluster frequently lacks host capacity for placement. The requested action is use a capacity provider that can scale the backing Auto Scaling group. For this design, ECS Availability Zone spread placement targets another requirement.

 

Question 12

A small platform team owns the application containers but not host operations. The requirement is to launch the tasks on AWS Fargate. Which design choice best satisfies this requirement? Choose ONE.

  1. ECS capacity provider with managed scaling
  2. ECS Availability Zone spread placement
  3. AWS Fargate
  4. Multi-AZ Auto Scaling group

Correct Answer(s)

 

C

Rationale

  1. ECS capacity provider with managed scaling enables container demand linked to EC2 cluster for a team wants to run ECS tasks without provisioning or managing EC2 container hosts. The requested action is launch the tasks on AWS Fargate. In this decision, ECS capacity provider with managed scaling solves a different problem.
  2. ECS Availability Zone spread placement controls task placement distributed across fault domains for a team wants to run ECS tasks without provisioning or managing EC2 container hosts. The requested action is launch the tasks on AWS Fargate. For the stated goal, ECS Availability Zone spread placement does not fit here.
  3. AWS Fargate acts as serverless compute capacity for containers for a team wants to run ECS tasks without provisioning or managing EC2 container hosts. The requested action is launch the tasks on AWS Fargate. Because the evidence aligns with AWS Fargate, it matches the stated evidence.
  4. Multi-AZ Auto Scaling group supplies instance distribution across multiple Availability Zones for a team wants to run ECS tasks without provisioning or managing EC2 container hosts. The requested action is launch the tasks on AWS Fargate. With the stated need, Multi-AZ Auto Scaling group addresses another layer.

 

Question 13

A service has enough EC2 capacity in several zones. The requirement is to use a spread placement strategy across the Availability Zone attribute. Which design choice best satisfies this requirement? Choose ONE.

  1. Multi-AZ Auto Scaling group
  2. ECS Availability Zone spread placement
  3. ECS capacity provider with managed scaling
  4. AWS Fargate

Correct Answer(s)

 

B

Rationale

  1. Multi-AZ Auto Scaling group implements instance distribution across multiple Availability Zones for replicated ECS tasks on EC2 should avoid concentrating in one Availability Zone. The requested action is use a spread placement strategy across the Availability Zone attribute. With that requirement, Multi-AZ Auto Scaling group fails this constraint.
  2. ECS Availability Zone spread placement supports task placement distributed across fault domains for replicated ECS tasks on EC2 should avoid concentrating in one Availability Zone. The requested action is use a spread placement strategy across the Availability Zone attribute. Because the evidence aligns with ECS Availability Zone spread placement, it meets the operating need.
  3. ECS capacity provider with managed scaling serves as container demand linked to EC2 cluster for replicated ECS tasks on EC2 should avoid concentrating in one Availability Zone. The requested action is use a spread placement strategy across the Availability Zone attribute. For this operating model, ECS capacity provider with managed scaling does not resolve it.
  4. AWS Fargate applies serverless compute capacity for containers for replicated ECS tasks on EC2 should avoid concentrating in one Availability Zone. The requested action is use a spread placement strategy across the Availability Zone attribute. Under that constraint, AWS Fargate serves another purpose.

 

Question 14

A bursty event processor has independent short-running requests. The requirement is to use Lambda concurrency scaling without managing servers. Which design choice best satisfies this requirement? Choose ONE.

  1. Lambda reserved concurrency
  2. Lambda provisioned concurrency
  3. API Gateway with Lambda
  4. AWS Lambda automatic scaling

Correct Answer(s)

 

D

Rationale

  1. Lambda reserved concurrency creates function specific concurrency reservation and maximum for a stateless event handler should scale invocation capacity automatically as parallel events increase. The requested action is use Lambda concurrency scaling without managing servers. With this evidence, Lambda reserved concurrency changes another mechanism.
  2. Lambda provisioned concurrency gives pre initialized execution environments for low for a stateless event handler should scale invocation capacity automatically as parallel events increase. The requested action is use Lambda concurrency scaling without managing servers. Under this evidence, Lambda provisioned concurrency does not match.
  3. API Gateway with Lambda delivers managed request front door for serverless for a stateless event handler should scale invocation capacity automatically as parallel events increase. The requested action is use Lambda concurrency scaling without managing servers. Under these conditions, API Gateway with Lambda is not the remedy.
  4. AWS Lambda automatic scaling offers concurrency created automatically for incoming invocations for a stateless event handler should scale invocation capacity automatically as parallel events increase. The requested action is use Lambda concurrency scaling without managing servers. Because the evidence aligns with AWS Lambda automatic scaling, it addresses this requirement.

 

Question 15

Several functions share the same regional concurrency pool. The requirement is to assign reserved concurrency to bound the noisy function and protect capacity. Which design choice best satisfies this requirement? Choose ONE.

  1. Lambda provisioned concurrency
  2. AWS Lambda automatic scaling
  3. Lambda reserved concurrency
  4. ECS Service Auto Scaling

Correct Answer(s)

 

C

Rationale

  1. Lambda provisioned concurrency controls pre initialized execution environments for low for one noisy Lambda function must not consume all account concurrency needed by a critical function. The requested action is assign reserved concurrency to bound the noisy function and protect capacity. For the required behavior, Lambda provisioned concurrency leaves the issue intact.
  2. AWS Lambda automatic scaling supplies concurrency created automatically for incoming invocations for one noisy Lambda function must not consume all account concurrency needed by a critical function. The requested action is assign reserved concurrency to bound the noisy function and protect capacity. In this architecture, AWS Lambda automatic scaling does not satisfy it.
  3. Lambda reserved concurrency enables function specific concurrency reservation and maximum for one noisy Lambda function must not consume all account concurrency needed by a critical function. The requested action is assign reserved concurrency to bound the noisy function and protect capacity. Because the evidence aligns with Lambda reserved concurrency, it is the direct fit.
  4. ECS Service Auto Scaling establishes automatic changes to ECS desired task for one noisy Lambda function must not consume all account concurrency needed by a critical function. The requested action is assign reserved concurrency to bound the noisy function and protect capacity. Under the scenario, ECS Service Auto Scaling operates elsewhere.

 

Question 16

A function has heavy initialization but predictable interactive demand. The requirement is to configure provisioned concurrency for the function alias or version. Which design choice best satisfies this requirement? Choose ONE.

  1. Lambda provisioned concurrency
  2. Lambda reserved concurrency
  3. Auto Scaling warm pool
  4. AWS Lambda automatic scaling

Correct Answer(s)

 

A

Rationale

  1. Lambda provisioned concurrency implements pre initialized execution environments for low for a synchronous latency-sensitive Lambda API cannot tolerate cold-start initialization at peak time. The requested action is configure provisioned concurrency for the function alias or version. Because the evidence aligns with Lambda provisioned concurrency, it satisfies this design.
  2. Lambda reserved concurrency serves as function specific concurrency reservation and maximum for a synchronous latency-sensitive Lambda API cannot tolerate cold-start initialization at peak time. The requested action is configure provisioned concurrency for the function alias or version. For this workload, Lambda reserved concurrency is the wrong control.
  3. Auto Scaling warm pool applies pre initialized instances kept near ready for a synchronous latency-sensitive Lambda API cannot tolerate cold-start initialization at peak time. The requested action is configure provisioned concurrency for the function alias or version. In the requested path, Auto Scaling warm pool cannot meet this need.
  4. AWS Lambda automatic scaling provides concurrency created automatically for incoming invocations for a synchronous latency-sensitive Lambda API cannot tolerate cold-start initialization at peak time. The requested action is configure provisioned concurrency for the function alias or version. Given this case, AWS Lambda automatic scaling misses this case.

 

Question 17

A new service has no requirement to manage web servers. The requirement is to front Lambda functions with Amazon API Gateway. Which design choice best satisfies this requirement? Choose ONE.

  1. ALB target groups and health checks
  2. Network Load Balancer
  3. API Gateway with Lambda
  4. AWS Cloud Map service discovery

Correct Answer(s)

 

C

Rationale

  1. ALB target groups and health checks gives Layer 7 distribution to healthy application for a public JSON API needs a managed HTTPS endpoint and stateless serverless compute that scales per request. The requested action is front Lambda functions with Amazon API Gateway. For this design, ALB target groups and health checks targets another requirement.
  2. Network Load Balancer delivers Layer 4 distribution for TCP or for a public JSON API needs a managed HTTPS endpoint and stateless serverless compute that scales per request. The requested action is front Lambda functions with Amazon API Gateway. In this decision, Network Load Balancer solves a different problem.
  3. API Gateway with Lambda creates managed request front door for serverless for a public JSON API needs a managed HTTPS endpoint and stateless serverless compute that scales per request. The requested action is front Lambda functions with Amazon API Gateway. Because the evidence aligns with API Gateway with Lambda, it aligns with this constraint.
  4. AWS Cloud Map service discovery functions as dynamic naming and discovery for service for a public JSON API needs a managed HTTPS endpoint and stateless serverless compute that scales per request. The requested action is front Lambda functions with Amazon API Gateway. For the stated goal, AWS Cloud Map service discovery does not fit here.

 

Question 18

A Kubernetes application already has enough node capacity. The requirement is to configure a Horizontal Pod Autoscaler for the workload. Which design choice best satisfies this requirement? Choose ONE.

  1. Kubernetes Horizontal Pod Autoscaler on EKS
  2. EKS node autoscaling
  3. ECS Service Auto Scaling
  4. EC2 Auto Scaling target tracking

Correct Answer(s)

 

A

Rationale

  1. Kubernetes Horizontal Pod Autoscaler on EKS controls pod replica scaling from observed metrics for an EKS deployment should increase pod replicas when CPU demand rises. The requested action is configure a Horizontal Pod Autoscaler for the workload. Because the evidence aligns with Kubernetes Horizontal Pod Autoscaler on EKS, it resolves the stated need.
  2. EKS node autoscaling supplies worker node capacity changes for pending for an EKS deployment should increase pod replicas when CPU demand rises. The requested action is configure a Horizontal Pod Autoscaler for the workload. With the stated need, EKS node autoscaling addresses another layer.
  3. ECS Service Auto Scaling establishes automatic changes to ECS desired task for an EKS deployment should increase pod replicas when CPU demand rises. The requested action is configure a Horizontal Pod Autoscaler for the workload. With that requirement, ECS Service Auto Scaling fails this constraint.
  4. EC2 Auto Scaling target tracking handles metric driven desired capacity adjustment for an EKS deployment should increase pod replicas when CPU demand rises. The requested action is configure a Horizontal Pod Autoscaler for the workload. For this operating model, EC2 Auto Scaling target tracking does not resolve it.

 

Question 19

A Kubernetes deployment has already increased its replica count. The requirement is to add automated worker-node scaling for unschedulable pods. Which design choice best satisfies this requirement? Choose ONE.

  1. Kubernetes Horizontal Pod Autoscaler on EKS
  2. EKS node autoscaling
  3. ECS capacity provider with managed scaling
  4. AWS Fargate

Correct Answer(s)

 

B

Rationale

  1. Kubernetes Horizontal Pod Autoscaler on EKS applies pod replica scaling from observed metrics for EKS pods remain Pending because the cluster has insufficient node capacity despite correct pod scaling. The requested action is add automated worker-node scaling for unschedulable pods. Under that constraint, Kubernetes Horizontal Pod Autoscaler on EKS serves another purpose.
  2. EKS node autoscaling serves as worker node capacity changes for pending for EKS pods remain Pending because the cluster has insufficient node capacity despite correct pod scaling. The requested action is add automated worker-node scaling for unschedulable pods. Because the evidence aligns with EKS node autoscaling, it fits this case.
  3. ECS capacity provider with managed scaling provides container demand linked to EC2 cluster for EKS pods remain Pending because the cluster has insufficient node capacity despite correct pod scaling. The requested action is add automated worker-node scaling for unschedulable pods. With this evidence, ECS capacity provider with managed scaling changes another mechanism.
  4. AWS Fargate acts as serverless compute capacity for containers for EKS pods remain Pending because the cluster has insufficient node capacity despite correct pod scaling. The requested action is add automated worker-node scaling for unschedulable pods. Under this evidence, AWS Fargate does not match.

 

Question 20

A private service has dynamic task addresses and no need for a public load balancer. The requirement is to register service instances in AWS Cloud Map. Which design choice best satisfies this requirement? Choose ONE.

  1. ALB target groups and health checks
  2. API Gateway with Lambda
  3. Network Load Balancer
  4. AWS Cloud Map service discovery

Correct Answer(s)

 

D

Rationale

  1. ALB target groups and health checks delivers Layer 7 distribution to healthy application for microservices need to discover changing backend instance addresses by logical service name. The requested action is register service instances in AWS Cloud Map. Under these conditions, ALB target groups and health checks is not the remedy.
  2. API Gateway with Lambda functions as managed request front door for serverless for microservices need to discover changing backend instance addresses by logical service name. The requested action is register service instances in AWS Cloud Map. For the required behavior, API Gateway with Lambda leaves the issue intact.
  3. Network Load Balancer supports Layer 4 distribution for TCP or for microservices need to discover changing backend instance addresses by logical service name. The requested action is register service instances in AWS Cloud Map. In this architecture, Network Load Balancer does not satisfy it.
  4. AWS Cloud Map service discovery gives dynamic naming and discovery for service for microservices need to discover changing backend instance addresses by logical service name. The requested action is register service instances in AWS Cloud Map. Because the evidence aligns with AWS Cloud Map service discovery, it matches the stated evidence.

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