Predictive scaling overview in AWS - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does AWS Predictive Scaling forecast traffic?

AWS Predictive Scaling uses historical data to forecast future traffic patterns. Which method best describes how it predicts scaling needs?

A It uses machine learning models analyzing past usage trends to predict future demand.

B It randomly scales instances based on current CPU usage without historical data.

C It duplicates the current number of instances every hour regardless of traffic.

D It scales only when manual intervention triggers alarms.

💡 Hint

Think about how a system can prepare ahead by learning from past patterns.

Predictive Scaling uses machine learning to analyze historical traffic and forecast future needs, allowing proactive scaling.

❓ Architecture

intermediate

Which AWS service combination enables Predictive Scaling for EC2 Auto Scaling groups?

To implement Predictive Scaling for an EC2 Auto Scaling group, which combination of AWS services is required?

A Amazon EC2 with AWS Elastic Beanstalk and Amazon SNS.

B Amazon S3 with AWS Lambda and Amazon RDS.

C Amazon EC2 Auto Scaling with Amazon CloudWatch and AWS Auto Scaling predictive policies.

D AWS Glue with Amazon Athena and Amazon QuickSight.

💡 Hint

Focus on services related to scaling and monitoring EC2 instances.

Predictive Scaling uses EC2 Auto Scaling groups with CloudWatch metrics and predictive scaling policies to forecast and adjust capacity.

❓ service_behavior

advanced

What happens if Predictive Scaling forecast is inaccurate?

If AWS Predictive Scaling forecasts demand incorrectly, what is the expected behavior of the Auto Scaling group?

A The Auto Scaling group deletes all instances and restarts with default capacity.

B The Auto Scaling group stops scaling until manual intervention occurs.

C The Auto Scaling group permanently disables scaling policies.

D The Auto Scaling group falls back to reactive scaling based on real-time CloudWatch alarms.

💡 Hint

Consider how AWS ensures availability even if predictions are off.

If predictions are wrong, Auto Scaling uses reactive scaling triggered by CloudWatch alarms to adjust capacity dynamically.

❓ security

advanced

Which IAM permission is essential for enabling Predictive Scaling on an Auto Scaling group?

To allow AWS Predictive Scaling to function, which IAM permission must be granted to the Auto Scaling service role?

A "s3:ListBucket" to access S3 buckets for logs.

B "autoscaling:PutScheduledUpdateGroupAction" to create scheduled scaling actions.

C "ec2:TerminateInstances" to delete instances manually.

D "lambda:InvokeFunction" to trigger Lambda functions on scaling events.

💡 Hint

Think about permissions related to scheduling scaling actions.

Predictive Scaling creates scheduled scaling actions, so the service role needs permission to put these actions.

✅ Best Practice

expert

How should you configure Predictive Scaling to handle sudden unexpected traffic spikes?

Given that Predictive Scaling forecasts based on historical data, what is the best practice to ensure your application handles sudden unexpected traffic spikes?

A Combine Predictive Scaling with reactive scaling policies triggered by CloudWatch alarms for immediate response.

B Rely solely on Predictive Scaling and disable reactive scaling to avoid conflicts.

C Set Predictive Scaling to scale down aggressively to save costs during spikes.

D Manually increase instance count before expected spikes and disable all automatic scaling.

💡 Hint

Consider how to cover both forecasted and unforecasted demand.

Combining predictive and reactive scaling ensures the system can handle both expected and unexpected traffic changes effectively.

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