Predictive scaling overview in AWS - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Predictive Scaling in AWS?

Predictive Scaling is a feature that automatically adjusts the number of running servers before traffic changes happen, based on past usage patterns.

intermediate

How does Predictive Scaling differ from Reactive Scaling?

Predictive Scaling plans ahead using historical data to add or remove servers before demand changes, while Reactive Scaling responds only after demand changes occur.

beginner

What data does Predictive Scaling use to forecast demand?

It uses historical traffic and usage data from your application to predict future resource needs.

beginner

Why is Predictive Scaling beneficial for cloud infrastructure?

It helps keep applications running smoothly by preparing resources ahead of time, avoiding slowdowns or wasted resources.

intermediate

Can Predictive Scaling work with other AWS scaling features?

Yes, it works alongside reactive scaling to handle unexpected changes and ensures efficient resource use.

What does Predictive Scaling primarily use to adjust resources?

A Historical usage data

B Random guesses

C Manual input only

D Fixed schedules

Predictive Scaling uses historical usage data to forecast future demand and adjust resources accordingly.

Which is a key advantage of Predictive Scaling over Reactive Scaling?

A It reacts faster after demand changes

B It only works for databases

C It predicts demand before it happens

D It requires no data

Predictive Scaling forecasts demand ahead of time, allowing resources to be ready before traffic changes.

Predictive Scaling in AWS is best described as:

A Manual server management

B Automatic resource adjustment based on predictions

C A security feature

D A storage optimization tool

It automatically adjusts resources based on predicted future demand.

What happens if actual demand differs from the prediction in Predictive Scaling?

A Reactive scaling can adjust resources as needed

B The system ignores the difference

C The application shuts down

D Resources are fixed and cannot change

Reactive scaling complements predictive scaling by handling unexpected demand changes.

Which AWS service commonly uses Predictive Scaling?

A Amazon RDS snapshots

B Amazon S3

C AWS Lambda

D Amazon EC2 Auto Scaling

Amazon EC2 Auto Scaling uses Predictive Scaling to manage server capacity.

Explain how Predictive Scaling helps manage cloud resources efficiently.

Think about planning ahead to avoid surprises.

Describe the difference between Predictive Scaling and Reactive Scaling in AWS.

One plans ahead, the other reacts.

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