Amazon AWS SAA-C03 Elastic Scaling, Load Balancing, Containers, Serverless, and Microservices Practice Test 2

Topic 08 Practice Test 2 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

An operations review finds repeated over- and under-scaling. The requirement is to replace manual reactions with target-based closed-loop scaling. Which design choice best satisfies this requirement? Choose ONE.

EC2 Auto Scaling scheduled scaling
EC2 Auto Scaling target tracking
EC2 Auto Scaling predictive scaling
Auto Scaling lifecycle hook

Correct Answer(s)

Rationale

EC2 Auto Scaling scheduled scaling establishes time based capacity changes at known for manual scaling caused oscillation because operators react too late to a changing utilization metric. The requested action is replace manual reactions with target-based closed-loop scaling. Under the scenario, EC2 Auto Scaling scheduled scaling operates elsewhere.
EC2 Auto Scaling target tracking supplies metric driven desired capacity adjustment for manual scaling caused oscillation because operators react too late to a changing utilization metric. The requested action is replace manual reactions with target-based closed-loop scaling. Because the evidence aligns with EC2 Auto Scaling target tracking, it meets the operating need.
EC2 Auto Scaling predictive scaling handles forecast based capacity planning from historical for manual scaling caused oscillation because operators react too late to a changing utilization metric. The requested action is replace manual reactions with target-based closed-loop scaling. For this workload, EC2 Auto Scaling predictive scaling is the wrong control.
Auto Scaling lifecycle hook offers pause instance launch or termination transitions for manual scaling caused oscillation because operators react too late to a changing utilization metric. The requested action is replace manual reactions with target-based closed-loop scaling. In the requested path, Auto Scaling lifecycle hook cannot meet this need.

Question 2

A batch platform has a fixed Monday processing window. The requirement is to pre-scale the group using the known weekly timetable. Which design choice best satisfies this requirement? Choose ONE.

EC2 Auto Scaling target tracking
EC2 Auto Scaling predictive scaling
Auto Scaling warm pool
EC2 Auto Scaling scheduled scaling

Correct Answer(s)

Rationale

EC2 Auto Scaling target tracking provides metric driven desired capacity adjustment for capacity arrives after a recurring weekly batch begins because reactive metrics trigger only after load rises. The requested action is pre-scale the group using the known weekly timetable. Given this case, EC2 Auto Scaling target tracking misses this case.
EC2 Auto Scaling predictive scaling acts as forecast based capacity planning from historical for capacity arrives after a recurring weekly batch begins because reactive metrics trigger only after load rises. The requested action is pre-scale the group using the known weekly timetable. For this design, EC2 Auto Scaling predictive scaling targets another requirement.
Auto Scaling warm pool enables pre initialized instances kept near ready for capacity arrives after a recurring weekly batch begins because reactive metrics trigger only after load rises. The requested action is pre-scale the group using the known weekly timetable. In this decision, Auto Scaling warm pool solves a different problem.
EC2 Auto Scaling scheduled scaling applies time based capacity changes at known for capacity arrives after a recurring weekly batch begins because reactive metrics trigger only after load rises. The requested action is pre-scale the group using the known weekly timetable. Because the evidence aligns with EC2 Auto Scaling scheduled scaling, it addresses this requirement.

Question 3

A service reports recurring scale-out latency at the same time each day. The requirement is to use predictive forecasts to prepare capacity before the ramp. Which design choice best satisfies this requirement? Choose ONE.

EC2 Auto Scaling predictive scaling
EC2 Auto Scaling target tracking
EC2 Auto Scaling scheduled scaling
Auto Scaling warm pool

Correct Answer(s)

Rationale

EC2 Auto Scaling predictive scaling delivers forecast based capacity planning from historical for reactive scaling consistently lags a repeating morning ramp even though the pattern is learned from prior weeks. The requested action is use predictive forecasts to prepare capacity before the ramp. Because the evidence aligns with EC2 Auto Scaling predictive scaling, it is the direct fit.
EC2 Auto Scaling target tracking functions as metric driven desired capacity adjustment for reactive scaling consistently lags a repeating morning ramp even though the pattern is learned from prior weeks. The requested action is use predictive forecasts to prepare capacity before the ramp. For the stated goal, EC2 Auto Scaling target tracking does not fit here.
EC2 Auto Scaling scheduled scaling supports time based capacity changes at known for reactive scaling consistently lags a repeating morning ramp even though the pattern is learned from prior weeks. The requested action is use predictive forecasts to prepare capacity before the ramp. With the stated need, EC2 Auto Scaling scheduled scaling addresses another layer.
Auto Scaling warm pool implements pre initialized instances kept near ready for reactive scaling consistently lags a repeating morning ramp even though the pattern is learned from prior weeks. The requested action is use predictive forecasts to prepare capacity before the ramp. With that requirement, Auto Scaling warm pool fails this constraint.

Question 4

A post-incident review identifies single-zone fleet placement as the failure domain. The requirement is to expand the group across independent Availability Zones. Which design choice best satisfies this requirement? Choose ONE.

EC2 Auto Scaling target tracking
ECS Availability Zone spread placement
Multi-AZ Auto Scaling group
ALB target groups and health checks

Correct Answer(s)

Rationale

EC2 Auto Scaling target tracking handles metric driven desired capacity adjustment for a subnet outage removed every instance because the Auto Scaling group was constrained to one zone. The requested action is expand the group across independent Availability Zones. For this operating model, EC2 Auto Scaling target tracking does not resolve it.
ECS Availability Zone spread placement offers task placement distributed across fault domains for a subnet outage removed every instance because the Auto Scaling group was constrained to one zone. The requested action is expand the group across independent Availability Zones. Under that constraint, ECS Availability Zone spread placement serves another purpose.
Multi-AZ Auto Scaling group establishes instance distribution across multiple Availability Zones for a subnet outage removed every instance because the Auto Scaling group was constrained to one zone. The requested action is expand the group across independent Availability Zones. Because the evidence aligns with Multi-AZ Auto Scaling group, it satisfies this design.
ALB target groups and health checks creates Layer 7 distribution to healthy application for a subnet outage removed every instance because the Auto Scaling group was constrained to one zone. The requested action is expand the group across independent Availability Zones. With this evidence, ALB target groups and health checks changes another mechanism.

Question 5

An ALB already detects application failures that the group ignores. The requirement is to enable the relevant load-balancer health checks for the Auto Scaling group. Which design choice best satisfies this requirement? Choose ONE.

ALB target groups and health checks
Auto Scaling lifecycle hook
Auto Scaling health-check replacement
EC2 Auto Scaling target tracking

Correct Answer(s)

Rationale

ALB target groups and health checks acts as Layer 7 distribution to healthy application for unhealthy application nodes stay in the fleet because only EC2 status checks are considered. The requested action is enable the relevant load-balancer health checks for the Auto Scaling group. Under this evidence, ALB target groups and health checks does not match.
Auto Scaling lifecycle hook enables pause instance launch or termination transitions for unhealthy application nodes stay in the fleet because only EC2 status checks are considered. The requested action is enable the relevant load-balancer health checks for the Auto Scaling group. Under these conditions, Auto Scaling lifecycle hook is not the remedy.
Auto Scaling health-check replacement provides automatic replacement of unhealthy group instances for unhealthy application nodes stay in the fleet because only EC2 status checks are considered. The requested action is enable the relevant load-balancer health checks for the Auto Scaling group. Because the evidence aligns with Auto Scaling health-check replacement, it aligns with this constraint.
EC2 Auto Scaling target tracking controls metric driven desired capacity adjustment for unhealthy application nodes stay in the fleet because only EC2 status checks are considered. The requested action is enable the relevant load-balancer health checks for the Auto Scaling group. For the required behavior, EC2 Auto Scaling target tracking leaves the issue intact.

Question 6

A worker service can lose in-flight jobs if instances terminate immediately. The requirement is to pause termination long enough for a cleanup workflow. Which design choice best satisfies this requirement? Choose ONE.

Auto Scaling lifecycle hook
Auto Scaling health-check replacement
Auto Scaling warm pool
EC2 Auto Scaling target tracking

Correct Answer(s)

Rationale

Auto Scaling lifecycle hook functions as pause instance launch or termination transitions for terminating instances must drain a custom work queue before shutdown. The requested action is pause termination long enough for a cleanup workflow. Because the evidence aligns with Auto Scaling lifecycle hook, it resolves the stated need.
Auto Scaling health-check replacement supports automatic replacement of unhealthy group instances for terminating instances must drain a custom work queue before shutdown. The requested action is pause termination long enough for a cleanup workflow. In this architecture, Auto Scaling health-check replacement does not satisfy it.
Auto Scaling warm pool implements pre initialized instances kept near ready for terminating instances must drain a custom work queue before shutdown. The requested action is pause termination long enough for a cleanup workflow. Under the scenario, Auto Scaling warm pool operates elsewhere.
EC2 Auto Scaling target tracking serves as metric driven desired capacity adjustment for terminating instances must drain a custom work queue before shutdown. The requested action is pause termination long enough for a cleanup workflow. For this workload, EC2 Auto Scaling target tracking is the wrong control.

Question 7

A group has fast metrics but slow software startup. The requirement is to reduce scale-out readiness delay with pre-warmed group capacity. Which design choice best satisfies this requirement? Choose ONE.

EC2 Auto Scaling scheduled scaling
Auto Scaling lifecycle hook
Lambda provisioned concurrency
Auto Scaling warm pool

Correct Answer(s)

Rationale

EC2 Auto Scaling scheduled scaling offers time based capacity changes at known for target tracking requests more capacity quickly but initialization, not instance allocation, dominates readiness time. The requested action is reduce scale-out readiness delay with pre-warmed group capacity. In the requested path, EC2 Auto Scaling scheduled scaling cannot meet this need.
Auto Scaling lifecycle hook creates pause instance launch or termination transitions for target tracking requests more capacity quickly but initialization, not instance allocation, dominates readiness time. The requested action is reduce scale-out readiness delay with pre-warmed group capacity. Given this case, Auto Scaling lifecycle hook misses this case.
Lambda provisioned concurrency gives pre initialized execution environments for low for target tracking requests more capacity quickly but initialization, not instance allocation, dominates readiness time. The requested action is reduce scale-out readiness delay with pre-warmed group capacity. For this design, Lambda provisioned concurrency targets another requirement.
Auto Scaling warm pool handles pre initialized instances kept near ready for target tracking requests more capacity quickly but initialization, not instance allocation, dominates readiness time. The requested action is reduce scale-out readiness delay with pre-warmed group capacity. Because the evidence aligns with Auto Scaling warm pool, it fits this case.

Question 8

An ALB fronts several private application instances. The requirement is to correct the ALB target-group health check so unhealthy targets are removed from routing. Which design choice best satisfies this requirement? Choose ONE.

Network Load Balancer
ALB target groups and health checks
Auto Scaling health-check replacement
API Gateway with Lambda

Correct Answer(s)

Rationale

Network Load Balancer enables Layer 4 distribution for TCP or for clients receive errors because a failed application node still receives requests. The requested action is correct the ALB target-group health check so unhealthy targets are removed from routing. In this decision, Network Load Balancer solves a different problem.
ALB target groups and health checks acts as Layer 7 distribution to healthy application for clients receive errors because a failed application node still receives requests. The requested action is correct the ALB target-group health check so unhealthy targets are removed from routing. Because the evidence aligns with ALB target groups and health checks, it matches the stated evidence.
Auto Scaling health-check replacement controls automatic replacement of unhealthy group instances for clients receive errors because a failed application node still receives requests. The requested action is correct the ALB target-group health check so unhealthy targets are removed from routing. For the stated goal, Auto Scaling health-check replacement does not fit here.
API Gateway with Lambda supplies managed request front door for serverless for clients receive errors because a failed application node still receives requests. The requested action is correct the ALB target-group health check so unhealthy targets are removed from routing. With the stated need, API Gateway with Lambda addresses another layer.

Question 9

A custom binary protocol uses long-lived TCP sessions. The requirement is to replace it with a Layer-4 Network Load Balancer. Which design choice best satisfies this requirement? Choose ONE.

Network Load Balancer
ALB target groups and health checks
API Gateway with Lambda
AWS Cloud Map service discovery

Correct Answer(s)

Rationale

Network Load Balancer supports Layer 4 distribution for TCP or for an HTTP-aware ALB was chosen for a non-HTTP TCP protocol and application routing features are unnecessary. The requested action is replace it with a Layer-4 Network Load Balancer. Because the evidence aligns with Network Load Balancer, it meets the operating need.
ALB target groups and health checks implements Layer 7 distribution to healthy application for an HTTP-aware ALB was chosen for a non-HTTP TCP protocol and application routing features are unnecessary. The requested action is replace it with a Layer-4 Network Load Balancer. With that requirement, ALB target groups and health checks fails this constraint.
API Gateway with Lambda serves as managed request front door for serverless for an HTTP-aware ALB was chosen for a non-HTTP TCP protocol and application routing features are unnecessary. The requested action is replace it with a Layer-4 Network Load Balancer. For this operating model, API Gateway with Lambda does not resolve it.
AWS Cloud Map service discovery applies dynamic naming and discovery for service for an HTTP-aware ALB was chosen for a non-HTTP TCP protocol and application routing features are unnecessary. The requested action is replace it with a Layer-4 Network Load Balancer. Under that constraint, AWS Cloud Map service discovery serves another purpose.

Question 10

An ECS service has a good utilization metric but static desired count. The requirement is to scale the service task count rather than only the host fleet. Which design choice best satisfies this requirement? Choose ONE.

ECS capacity provider with managed scaling
AWS Fargate
ECS Service Auto Scaling
EC2 Auto Scaling target tracking

Correct Answer(s)

Rationale

ECS capacity provider with managed scaling creates container demand linked to EC2 cluster for the EC2 cluster has spare hosts but an ECS service remains fixed at two tasks during load spikes. The requested action is scale the service task count rather than only the host fleet. With this evidence, ECS capacity provider with managed scaling changes another mechanism.
AWS Fargate gives serverless compute capacity for containers for the EC2 cluster has spare hosts but an ECS service remains fixed at two tasks during load spikes. The requested action is scale the service task count rather than only the host fleet. Under this evidence, AWS Fargate does not match.
ECS Service Auto Scaling offers automatic changes to ECS desired task for the EC2 cluster has spare hosts but an ECS service remains fixed at two tasks during load spikes. The requested action is scale the service task count rather than only the host fleet. Because the evidence aligns with ECS Service Auto Scaling, it addresses this requirement.
EC2 Auto Scaling target tracking delivers metric driven desired capacity adjustment for the EC2 cluster has spare hosts but an ECS service remains fixed at two tasks during load spikes. The requested action is scale the service task count rather than only the host fleet. Under these conditions, EC2 Auto Scaling target tracking is not the remedy.

Question 11

An ECS cluster uses an Auto Scaling group but no capacity-provider integration. The requirement is to connect the service to an EC2 capacity provider with managed scaling. Which design choice best satisfies this requirement? Choose ONE.

ECS Service Auto Scaling
ECS capacity provider with managed scaling
AWS Fargate
ECS Availability Zone spread placement

Correct Answer(s)

Rationale

ECS Service Auto Scaling controls automatic changes to ECS desired task for operators manually resize EC2 hosts whenever pending ECS tasks accumulate. The requested action is connect the service to an EC2 capacity provider with managed scaling. For the required behavior, ECS Service Auto Scaling leaves the issue intact.
ECS capacity provider with managed scaling enables container demand linked to EC2 cluster for operators manually resize EC2 hosts whenever pending ECS tasks accumulate. The requested action is connect the service to an EC2 capacity provider with managed scaling. Because the evidence aligns with ECS capacity provider with managed scaling, it is the direct fit.
AWS Fargate supplies serverless compute capacity for containers for operators manually resize EC2 hosts whenever pending ECS tasks accumulate. The requested action is connect the service to an EC2 capacity provider with managed scaling. In this architecture, AWS Fargate does not satisfy it.
ECS Availability Zone spread placement establishes task placement distributed across fault domains for operators manually resize EC2 hosts whenever pending ECS tasks accumulate. The requested action is connect the service to an EC2 capacity provider with managed scaling. Under the scenario, ECS Availability Zone spread placement operates elsewhere.

Question 12

A containerized web service has no host-specific requirements. The requirement is to move the service to a compatible Fargate launch model. Which design choice best satisfies this requirement? Choose ONE.

ECS capacity provider with managed scaling
ECS Availability Zone spread placement
Multi-AZ Auto Scaling group
AWS Fargate

Correct Answer(s)

Rationale

ECS capacity provider with managed scaling serves as container demand linked to EC2 cluster for pending tasks result from an undersized EC2 cluster and the business wants to remove host-capacity management entirely. The requested action is move the service to a compatible Fargate launch model. For this workload, ECS capacity provider with managed scaling is the wrong control.
ECS Availability Zone spread placement applies task placement distributed across fault domains for pending tasks result from an undersized EC2 cluster and the business wants to remove host-capacity management entirely. The requested action is move the service to a compatible Fargate launch model. In the requested path, ECS Availability Zone spread placement cannot meet this need.
Multi-AZ Auto Scaling group provides instance distribution across multiple Availability Zones for pending tasks result from an undersized EC2 cluster and the business wants to remove host-capacity management entirely. The requested action is move the service to a compatible Fargate launch model. Given this case, Multi-AZ Auto Scaling group misses this case.
AWS Fargate implements serverless compute capacity for containers for pending tasks result from an undersized EC2 cluster and the business wants to remove host-capacity management entirely. The requested action is move the service to a compatible Fargate launch model. Because the evidence aligns with AWS Fargate, it satisfies this design.

Question 13

An ECS-on-EC2 service needs stronger fault-domain distribution. The requirement is to rebalance task placement across Availability Zones. Which design choice best satisfies this requirement? Choose ONE.

Multi-AZ Auto Scaling group
ECS capacity provider with managed scaling
AWS Fargate
ECS Availability Zone spread placement

Correct Answer(s)

Rationale

Multi-AZ Auto Scaling group gives instance distribution across multiple Availability Zones for a zone failure removed most service replicas because tasks had clustered on hosts in that zone. The requested action is rebalance task placement across Availability Zones. For this design, Multi-AZ Auto Scaling group targets another requirement.
ECS capacity provider with managed scaling delivers container demand linked to EC2 cluster for a zone failure removed most service replicas because tasks had clustered on hosts in that zone. The requested action is rebalance task placement across Availability Zones. In this decision, ECS capacity provider with managed scaling solves a different problem.
AWS Fargate functions as serverless compute capacity for containers for a zone failure removed most service replicas because tasks had clustered on hosts in that zone. The requested action is rebalance task placement across Availability Zones. For the stated goal, AWS Fargate does not fit here.
ECS Availability Zone spread placement creates task placement distributed across fault domains for a zone failure removed most service replicas because tasks had clustered on hosts in that zone. The requested action is rebalance task placement across Availability Zones. Because the evidence aligns with ECS Availability Zone spread placement, it aligns with this constraint.

Question 14

A small event task has no host affinity or long-lived process requirement. The requirement is to move the handler to Lambda so concurrent invocations scale with demand. Which design choice best satisfies this requirement? Choose ONE.

Lambda reserved concurrency
AWS Lambda automatic scaling
Lambda provisioned concurrency
API Gateway with Lambda

Correct Answer(s)

Rationale

Lambda reserved concurrency supplies function specific concurrency reservation and maximum for operators are manually launching workers even though each job can run as an isolated function invocation. The requested action is move the handler to Lambda so concurrent invocations scale with demand. With the stated need, Lambda reserved concurrency addresses another layer.
AWS Lambda automatic scaling controls concurrency created automatically for incoming invocations for operators are manually launching workers even though each job can run as an isolated function invocation. The requested action is move the handler to Lambda so concurrent invocations scale with demand. Because the evidence aligns with AWS Lambda automatic scaling, it resolves the stated need.
Lambda provisioned concurrency establishes pre initialized execution environments for low for operators are manually launching workers even though each job can run as an isolated function invocation. The requested action is move the handler to Lambda so concurrent invocations scale with demand. With that requirement, Lambda provisioned concurrency fails this constraint.
API Gateway with Lambda handles managed request front door for serverless for operators are manually launching workers even though each job can run as an isolated function invocation. The requested action is move the handler to Lambda so concurrent invocations scale with demand. For this operating model, API Gateway with Lambda does not resolve it.

Question 15

A function burst can overwhelm a fixed-capacity dependency. The requirement is to cap the function with reserved concurrency. Which design choice best satisfies this requirement? Choose ONE.

Lambda provisioned concurrency
AWS Lambda automatic scaling
Lambda reserved concurrency
ECS Service Auto Scaling

Correct Answer(s)

Rationale

Lambda provisioned concurrency applies pre initialized execution environments for low for a downstream database can safely accept only a limited number of simultaneous Lambda connections. The requested action is cap the function with reserved concurrency. Under that constraint, Lambda provisioned concurrency serves another purpose.
AWS Lambda automatic scaling provides concurrency created automatically for incoming invocations for a downstream database can safely accept only a limited number of simultaneous Lambda connections. The requested action is cap the function with reserved concurrency. With this evidence, AWS Lambda automatic scaling changes another mechanism.
Lambda reserved concurrency serves as function specific concurrency reservation and maximum for a downstream database can safely accept only a limited number of simultaneous Lambda connections. The requested action is cap the function with reserved concurrency. Because the evidence aligns with Lambda reserved concurrency, it fits this case.
ECS Service Auto Scaling acts as automatic changes to ECS desired task for a downstream database can safely accept only a limited number of simultaneous Lambda connections. The requested action is cap the function with reserved concurrency. Under this evidence, ECS Service Auto Scaling does not match.

Question 16

A serverless API passes throughput tests but fails cold-start latency targets. The requirement is to pre-initialize Lambda environments with provisioned concurrency. Which design choice best satisfies this requirement? Choose ONE.

Lambda provisioned concurrency
Lambda reserved concurrency
Auto Scaling warm pool
AWS Lambda automatic scaling

Correct Answer(s)

Rationale

Lambda provisioned concurrency gives pre initialized execution environments for low for p99 latency spikes occur when new execution environments initialize, although average capacity is sufficient. The requested action is pre-initialize Lambda environments with provisioned concurrency. Because the evidence aligns with Lambda provisioned concurrency, it matches the stated evidence.
Lambda reserved concurrency delivers function specific concurrency reservation and maximum for p99 latency spikes occur when new execution environments initialize, although average capacity is sufficient. The requested action is pre-initialize Lambda environments with provisioned concurrency. Under these conditions, Lambda reserved concurrency is not the remedy.
Auto Scaling warm pool functions as pre initialized instances kept near ready for p99 latency spikes occur when new execution environments initialize, although average capacity is sufficient. The requested action is pre-initialize Lambda environments with provisioned concurrency. For the required behavior, Auto Scaling warm pool leaves the issue intact.
AWS Lambda automatic scaling supports concurrency created automatically for incoming invocations for p99 latency spikes occur when new execution environments initialize, although average capacity is sufficient. The requested action is pre-initialize Lambda environments with provisioned concurrency. In this architecture, AWS Lambda automatic scaling does not satisfy it.

Question 17

A serverless backend already performs all request processing. The requirement is to replace the proxy tier with API Gateway integration to Lambda. Which design choice best satisfies this requirement? Choose ONE.

ALB target groups and health checks
API Gateway with Lambda
Network Load Balancer
AWS Cloud Map service discovery

Correct Answer(s)

Rationale

ALB target groups and health checks establishes Layer 7 distribution to healthy application for an EC2 proxy tier adds operational work to a simple Lambda-backed REST interface. The requested action is replace the proxy tier with API Gateway integration to Lambda. Under the scenario, ALB target groups and health checks operates elsewhere.
API Gateway with Lambda supplies managed request front door for serverless for an EC2 proxy tier adds operational work to a simple Lambda-backed REST interface. The requested action is replace the proxy tier with API Gateway integration to Lambda. Because the evidence aligns with API Gateway with Lambda, it meets the operating need.
Network Load Balancer handles Layer 4 distribution for TCP or for an EC2 proxy tier adds operational work to a simple Lambda-backed REST interface. The requested action is replace the proxy tier with API Gateway integration to Lambda. For this workload, Network Load Balancer is the wrong control.
AWS Cloud Map service discovery offers dynamic naming and discovery for service for an EC2 proxy tier adds operational work to a simple Lambda-backed REST interface. The requested action is replace the proxy tier with API Gateway integration to Lambda. In the requested path, AWS Cloud Map service discovery cannot meet this need.

Question 18

An EKS service is bottlenecked at the application replica layer. The requirement is to scale the pod replica count with HPA rather than adding nodes first. Which design choice best satisfies this requirement? Choose ONE.

EKS node autoscaling
ECS Service Auto Scaling
EC2 Auto Scaling target tracking
Kubernetes Horizontal Pod Autoscaler on EKS

Correct Answer(s)

Rationale

EKS node autoscaling provides worker node capacity changes for pending for nodes have spare resources but request latency increases because the deployment remains at two replicas. The requested action is scale the pod replica count with HPA rather than adding nodes first. Given this case, EKS node autoscaling misses this case.
ECS Service Auto Scaling acts as automatic changes to ECS desired task for nodes have spare resources but request latency increases because the deployment remains at two replicas. The requested action is scale the pod replica count with HPA rather than adding nodes first. For this design, ECS Service Auto Scaling targets another requirement.
EC2 Auto Scaling target tracking enables metric driven desired capacity adjustment for nodes have spare resources but request latency increases because the deployment remains at two replicas. The requested action is scale the pod replica count with HPA rather than adding nodes first. In this decision, EC2 Auto Scaling target tracking solves a different problem.
Kubernetes Horizontal Pod Autoscaler on EKS applies pod replica scaling from observed metrics for nodes have spare resources but request latency increases because the deployment remains at two replicas. The requested action is scale the pod replica count with HPA rather than adding nodes first. Because the evidence aligns with Kubernetes Horizontal Pod Autoscaler on EKS, it addresses this requirement.

Question 19

An EKS platform wants infrastructure capacity to follow scheduling demand. The requirement is to use a compatible cluster node autoscaling mechanism. Which design choice best satisfies this requirement? Choose ONE.

EKS node autoscaling
Kubernetes Horizontal Pod Autoscaler on EKS
ECS capacity provider with managed scaling
AWS Fargate

Correct Answer(s)

Rationale

EKS node autoscaling delivers worker node capacity changes for pending for operators manually resize managed node groups whenever pod demand exceeds cluster resources. The requested action is use a compatible cluster node autoscaling mechanism. Because the evidence aligns with EKS node autoscaling, it is the direct fit.
Kubernetes Horizontal Pod Autoscaler on EKS functions as pod replica scaling from observed metrics for operators manually resize managed node groups whenever pod demand exceeds cluster resources. The requested action is use a compatible cluster node autoscaling mechanism. For the stated goal, Kubernetes Horizontal Pod Autoscaler on EKS does not fit here.
ECS capacity provider with managed scaling supports container demand linked to EC2 cluster for operators manually resize managed node groups whenever pod demand exceeds cluster resources. The requested action is use a compatible cluster node autoscaling mechanism. With the stated need, ECS capacity provider with managed scaling addresses another layer.
AWS Fargate implements serverless compute capacity for containers for operators manually resize managed node groups whenever pod demand exceeds cluster resources. The requested action is use a compatible cluster node autoscaling mechanism. With that requirement, AWS Fargate fails this constraint.

Question 20

An ECS microservice changes endpoints as it scales. The requirement is to adopt Cloud Map based service discovery. Which design choice best satisfies this requirement? Choose ONE.

ALB target groups and health checks
API Gateway with Lambda
AWS Cloud Map service discovery
Network Load Balancer

Correct Answer(s)

Rationale

ALB target groups and health checks handles Layer 7 distribution to healthy application for clients use hard-coded container IP addresses that become stale whenever tasks are replaced. The requested action is adopt Cloud Map based service discovery. For this operating model, ALB target groups and health checks does not resolve it.
API Gateway with Lambda offers managed request front door for serverless for clients use hard-coded container IP addresses that become stale whenever tasks are replaced. The requested action is adopt Cloud Map based service discovery. Under that constraint, API Gateway with Lambda serves another purpose.
AWS Cloud Map service discovery establishes dynamic naming and discovery for service for clients use hard-coded container IP addresses that become stale whenever tasks are replaced. The requested action is adopt Cloud Map based service discovery. Because the evidence aligns with AWS Cloud Map service discovery, it satisfies this design.
Network Load Balancer creates Layer 4 distribution for TCP or for clients use hard-coded container IP addresses that become stale whenever tasks are replaced. The requested action is adopt Cloud Map based service discovery. With this evidence, Network Load Balancer changes another mechanism.

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