ECS service auto scaling in AWS - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the minimum number of tasks for ECS service auto scaling.

AWS

MinCapacity: [1]

Drag options to blanks, or click blank then click option'

A5

B0

C1

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting minimum capacity to 0 disables running tasks.

Using a very high minimum capacity unnecessarily.

The minimum capacity for ECS service auto scaling should be set to a positive integer like 1 to ensure at least one task is running.

2fill in blank

medium

Complete the code to define the maximum number of tasks for ECS service auto scaling.

AWS

MaxCapacity: [1]

Drag options to blanks, or click blank then click option'

A10

B1

C0

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Setting maximum capacity lower than minimum capacity.

Setting maximum capacity to zero disables scaling.

The maximum capacity defines the upper limit of tasks the ECS service can scale to. Setting it to 10 allows scaling up to 10 tasks.

3fill in blank

hard

Fix the error in the target tracking scaling policy configuration by completing the missing value.

AWS

TargetValue: [1]

Drag options to blanks, or click blank then click option'

A50

B75

C100

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting target value to 0 disables scaling triggers.

Setting target value too high causes insufficient scaling.

The target value for CPU utilization in ECS auto scaling is commonly set to 75% to balance performance and cost.

4fill in blank

hard

Fill both blanks to configure the scalable target resource ID and scalable dimension for ECS service auto scaling.

AWS

ResourceId: [1]
ScalableDimension: [2]

Drag options to blanks, or click blank then click option'

Aservice/myCluster/myService

Bservice/myCluster/anotherService

Cecs:service:DesiredCount

Decs:task:Count

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect resource ID format.

Using wrong scalable dimension like ecs:task:Count.

The resource ID must specify the ECS service in the format 'service/clusterName/serviceName'. The scalable dimension for ECS service desired count is 'ecs:service:DesiredCount'.

5fill in blank

hard

Fill all three blanks to complete the CloudFormation snippet for ECS service auto scaling target and policy.

AWS

AutoScalingTarget:
  MinCapacity: [1]
  MaxCapacity: [2]
  ResourceId: [3]

Drag options to blanks, or click blank then click option'

A2

B10

Cservice/myCluster/myService

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Setting MinCapacity higher than MaxCapacity.

Incorrect ResourceId format.

MinCapacity is set to 2 to keep at least two tasks running. MaxCapacity is 10 to allow scaling up. ResourceId specifies the ECS service path.

Practice

1. What is the main purpose of ECS service auto scaling?

easy

A. Store container logs for analysis

B. Manually start and stop containers

C. Automatically adjust the number of running containers based on demand

D. Create new ECS clusters automatically

Solution

1. Step 1: Understand ECS service auto scaling function

   ECS service auto scaling changes the number of containers automatically to match workload.

2. Step 2: Compare options to this function

   Only Automatically adjust the number of running containers based on demand describes automatic adjustment of container count based on demand.

3. Final Answer:

   Automatically adjust the number of running containers based on demand -> Option C

4. Quick Check:

   Auto scaling adjusts container count = Automatically adjust containers [OK]

Hint: Auto scaling means automatic container count changes [OK]

Common Mistakes:

• Confusing auto scaling with manual container management
• Thinking auto scaling creates clusters
• Believing auto scaling stores logs

2. Which of the following is the correct way to specify a target tracking scaling policy for CPU utilization in ECS service auto scaling?

easy

A. "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"}

B. "TargetValue": "high", "PredefinedMetricSpecification": {"PredefinedMetricType": "CPUUsage"}

C. "TargetValue": 100, "MetricName": "MemoryUtilization"

D. "TargetValue": 30, "PredefinedMetricSpecification": {"PredefinedMetricType": "NetworkIn"}

Solution

1. Step 1: Identify correct metric type for CPU utilization

   The predefined metric for ECS CPU is "ECSServiceAverageCPUUtilization" with a numeric target value.

2. Step 2: Check option syntax and metric correctness

   "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} uses correct metric type and numeric target value 50, which is valid.

3. Final Answer:

   "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} -> Option A

4. Quick Check:

   Correct metric and numeric target = "TargetValue": 50, "PredefinedMetricSpecification": {"PredefinedMetricType": "ECSServiceAverageCPUUtilization"} [OK]

Hint: Use numeric target and correct predefined metric name [OK]

Common Mistakes:

• Using string instead of number for TargetValue
• Using wrong metric names like CPUUsage
• Mixing memory or network metrics for CPU scaling

3. Given this ECS service auto scaling configuration snippet:

{"MinCapacity": 2, "MaxCapacity": 5, "TargetValue": 60}

What happens when CPU utilization stays at 70% for a long time?

medium

A. The service scales up to 5 containers to reduce CPU load

B. The service scales down to 2 containers to save cost

C. The service keeps running 2 containers without change

D. The service crashes due to high CPU

Solution

1. Step 1: Understand scaling triggers from CPU target

   TargetValue 60 means ECS tries to keep CPU near 60%. If CPU is 70%, it needs more containers.

2. Step 2: Apply min and max capacity limits

   Min is 2, max is 5. Scaling up is allowed up to 5 containers to reduce CPU load.

3. Final Answer:

   The service scales up to 5 containers to reduce CPU load -> Option A

4. Quick Check:

   CPU above target scales up containers = The service scales up to 5 containers to reduce CPU load [OK]

Hint: CPU above target scales up, below scales down [OK]

Common Mistakes:

• Thinking service scales down when CPU is high
• Ignoring max capacity limit
• Assuming service crashes on high CPU

4. You configured ECS service auto scaling but the service never scales out even when CPU is high. What is a likely cause?

medium

A. The service has no load balancer attached

B. The target CPU utilization is set to 100%

C. The ECS cluster has no running tasks

D. The maximum capacity is set equal to the minimum capacity

Solution

1. Step 1: Analyze scaling limits

   If max capacity equals min capacity, no room to scale out exists.

2. Step 2: Check other options

   Target 100% means scaling triggers only at max CPU, but scaling can still happen. No running tasks or load balancer affect service but not scaling limits directly.

3. Final Answer:

   The maximum capacity is set equal to the minimum capacity -> Option D

4. Quick Check:

   Max equals min blocks scaling out = The maximum capacity is set equal to the minimum capacity [OK]

Hint: Max capacity must be greater than min capacity to scale out [OK]

Common Mistakes:

• Ignoring max and min capacity settings
• Assuming load balancer affects scaling triggers
• Confusing target CPU with scaling limits

5. You want to configure ECS service auto scaling to keep container count between 3 and 10, scaling based on memory usage targeting 70%. Which configuration is correct?

hard

A. MinCapacity: 10, MaxCapacity: 3, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization

B. MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization

C. MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageCPUUtilization

D. MinCapacity: 3, MaxCapacity: 10, TargetValue: 30, PredefinedMetricType: ECSServiceAverageMemoryUtilization

Solution

1. Step 1: Set correct min and max container counts

   MinCapacity must be less than MaxCapacity, so 3 and 10 is valid, 10 and 3 is invalid.

2. Step 2: Use correct metric and target value for memory

   Memory scaling uses ECSServiceAverageMemoryUtilization with target 70% to trigger scaling.

3. Step 3: Verify options

   MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization matches all requirements: correct min/max, target 70, memory metric.

4. Final Answer:

   MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization -> Option B

5. Quick Check:

   Correct min/max and memory target = MinCapacity: 3, MaxCapacity: 10, TargetValue: 70, PredefinedMetricType: ECSServiceAverageMemoryUtilization [OK]

Hint: Min < Max and match metric to resource (memory or CPU) [OK]

Common Mistakes:

• Setting max less than min capacity
• Using CPU metric for memory scaling
• Setting wrong target value for scaling