Predictive scaling overview in AWS - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to enable predictive scaling for an Auto Scaling group.

AWS

aws autoscaling put-scaling-policy --auto-scaling-group-name my-asg --policy-name predictive-policy --policy-type [1]

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

ASimpleScaling

BPredictiveScaling

CTargetTrackingScaling

DStepScaling

Attempts:

3 left

💡 Hint

Common Mistakes

Using SimpleScaling instead of PredictiveScaling

Confusing TargetTrackingScaling with predictive scaling

The policy type must be PredictiveScaling to enable predictive scaling.

2fill in blank

medium

Complete the code to specify the maximum capacity for predictive scaling.

AWS

aws autoscaling put-scaling-policy --auto-scaling-group-name my-asg --policy-name predictive-policy --policy-type PredictiveScaling --predictive-scaling-configuration MaxCapacityBehavior=[1]

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

AHonorMaxCapacity

BIgnoreMaxCapacity

CHonorDesiredCapacity

DIgnoreDesiredCapacity

Attempts:

3 left

💡 Hint

Common Mistakes

Using IgnoreMaxCapacity which can cause scaling beyond limits

Confusing desired capacity behavior with max capacity

HonorMaxCapacity ensures the predictive scaling respects the maximum capacity set for the group.

3fill in blank

hard

Fix the error in the predictive scaling configuration to set the scheduling buffer time.

AWS

aws autoscaling put-scaling-policy --auto-scaling-group-name my-asg --policy-name predictive-policy --policy-type PredictiveScaling --predictive-scaling-configuration SchedulingBufferTime=[1]

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

Afive_minutes

B"300"

C0.3

D300

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of an integer

Using a float or descriptive string instead of seconds

The SchedulingBufferTime must be an integer number of seconds, so 300 (5 minutes) is correct.

4fill in blank

hard

Fill both blanks to configure predictive scaling with a customized metric and target value.

AWS

aws autoscaling put-scaling-policy --auto-scaling-group-name my-asg --policy-name predictive-policy --policy-type PredictiveScaling --predictive-scaling-configuration PredefinedMetricSpecification=[1], TargetValue=[2]

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

A"ASGAverageCPUUtilization"

B50.0

C"ALBRequestCountPerTarget"

D100.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using ALBRequestCountPerTarget metric with a CPU target value

Setting target value as a string instead of a number

Use ASGAverageCPUUtilization as the predefined metric and set the target value to 50.0 percent.

5fill in blank

hard

Fill all three blanks to create a predictive scaling policy with a customized metric, target value, and disable scale-in.

AWS

aws autoscaling put-scaling-policy --auto-scaling-group-name my-asg --policy-name predictive-policy --policy-type PredictiveScaling --predictive-scaling-configuration PredefinedMetricSpecification=[1], TargetValue=[2], DisableScaleIn=[3]

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

A"ASGAverageNetworkIn"

B75.0

Ctrue

Dfalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using false instead of true for DisableScaleIn

Mixing metric and target value types

Use ASGAverageNetworkIn as the metric, set target value to 75.0, and disable scale-in by setting true.

Practice

1. What is the main purpose of AWS Predictive Scaling?

easy

A. To prepare servers ahead of traffic changes using machine learning

B. To manually add servers when traffic increases

C. To reduce server count only after traffic drops

D. To monitor server health without changing capacity

Solution

1. Step 1: Understand Predictive Scaling's goal

   Predictive Scaling uses machine learning to forecast traffic and adjust server capacity before changes happen.

2. Step 2: Compare options to this goal

   Only To prepare servers ahead of traffic changes using machine learning describes preparing servers ahead of traffic changes using machine learning.

3. Final Answer:

   To prepare servers ahead of traffic changes using machine learning -> Option A

4. Quick Check:

   Predictive Scaling = Prepare servers ahead [OK]

Hint: Predictive means it acts before traffic changes [OK]

Common Mistakes:

• Thinking it reacts only after traffic changes
• Confusing manual scaling with predictive scaling
• Believing it only monitors without scaling

2. Which AWS service feature enables Predictive Scaling?

easy

A. Server health monitoring

B. Manual server adjustment

C. Static server allocation

D. Machine learning forecasting

Solution

1. Step 1: Identify the technology behind Predictive Scaling

   Predictive Scaling uses machine learning to forecast future traffic and adjust capacity.

2. Step 2: Match options to this technology

   Only Machine learning forecasting mentions machine learning forecasting, which is the key feature.

3. Final Answer:

   Machine learning forecasting -> Option D

4. Quick Check:

   Predictive Scaling uses machine learning [OK]

Hint: Look for machine learning in options [OK]

Common Mistakes:

• Choosing manual or static options
• Confusing monitoring with forecasting
• Ignoring the predictive aspect

3. Given a sudden traffic spike predicted at 3 PM, what will Predictive Scaling do?

medium

A. Add servers after 3 PM when traffic spikes

B. Increase server capacity before 3 PM to handle traffic

C. Remove servers before 3 PM to save costs

D. Keep server count constant regardless of traffic

Solution

1. Step 1: Understand timing of scaling actions

   Predictive Scaling acts before traffic changes to prepare resources.

2. Step 2: Analyze options for timing and action

   Only Increase server capacity before 3 PM to handle traffic describes increasing capacity before the predicted spike.

3. Final Answer:

   Increase server capacity before 3 PM to handle traffic -> Option B

4. Quick Check:

   Predictive Scaling acts before spikes [OK]

Hint: Predictive means before event, not after [OK]

Common Mistakes:

• Choosing to add servers after spike
• Thinking it reduces servers before traffic
• Assuming no change in server count

4. You configured Predictive Scaling but notice servers are not added before traffic spikes. What is a likely cause?

medium

A. Predictive Scaling is disabled or misconfigured

B. Traffic never changes in your app

C. Manual scaling overrides predictive scaling

D. Server health checks are failing

Solution

1. Step 1: Identify why predictive scaling might not act

   If servers are not added before spikes, predictive scaling may be off or set up incorrectly.

2. Step 2: Evaluate other options

   Traffic changes and manual scaling do not prevent predictive scaling from working if enabled; health checks affect server health but not scaling triggers.

3. Final Answer:

   Predictive Scaling is disabled or misconfigured -> Option A

4. Quick Check:

   Misconfiguration stops predictive scaling [OK]

Hint: Check if predictive scaling is enabled first [OK]

Common Mistakes:

• Blaming traffic patterns
• Ignoring configuration settings
• Confusing health checks with scaling triggers

5. How does Predictive Scaling help balance cost and performance in AWS?

hard

A. By adding servers only after traffic increases to avoid waste

B. By permanently running maximum servers to handle any traffic

C. By forecasting traffic and adjusting capacity before changes to keep apps fast and save money

D. By disabling scaling during low traffic periods

Solution

1. Step 1: Understand cost and performance goals

   Good scaling keeps apps fast and avoids paying for unused servers.

2. Step 2: Match Predictive Scaling behavior

   It forecasts traffic and adjusts capacity before changes, balancing speed and cost.

3. Step 3: Evaluate options

   Only By forecasting traffic and adjusting capacity before changes to keep apps fast and save money describes forecasting and proactive adjustment to balance cost and performance.

4. Final Answer:

   By forecasting traffic and adjusting capacity before changes to keep apps fast and save money -> Option C

5. Quick Check:

   Forecasting balances cost and speed [OK]

Hint: Forecast first, then scale to save money and stay fast [OK]

Common Mistakes:

• Thinking scaling happens only after traffic rises
• Assuming always max servers run
• Believing scaling disables during low traffic