Performance efficiency pillar in AWS - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the AWS service that helps improve performance by caching content closer to users.

AWS

Use Amazon [1] to cache content at edge locations for faster delivery.

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

ARDS

BS3

CEC2

DCloudFront

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing S3 which is for storage, not caching.

Selecting EC2 which is for compute, not content delivery.

Amazon CloudFront is a content delivery network (CDN) service that caches content at edge locations to improve performance.

2fill in blank

medium

Complete the code to select the AWS service that automatically adjusts compute capacity to maintain steady performance.

AWS

Use AWS [1] to automatically add or remove EC2 instances based on demand.

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

ALambda

BCloudWatch

CAuto Scaling

DElastic Beanstalk

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing Lambda which is serverless compute, not scaling EC2 instances.

Selecting CloudWatch which monitors but does not scale.

AWS Auto Scaling adjusts the number of EC2 instances automatically to meet demand and maintain performance.

3fill in blank

hard

Fix the error in the code to correctly define a performance-efficient database instance type.

AWS

db_instance = boto3.client('rds').create_db_instance(DBInstanceClass='[1]', Engine='mysql')

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

Adb.m5.large

Bt2.micro

Cdb.t2.micro

Dm5.large

Attempts:

3 left

💡 Hint

Common Mistakes

Using EC2 instance types without 'db.' prefix.

Choosing very small instance types that may not meet performance needs.

The correct DB instance class for RDS must start with 'db.' followed by the instance type, such as 'db.m5.large'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps AWS regions to their latency in milliseconds, filtering only regions with latency less than 100.

AWS

latency_map = {region: latency for region, latency in latencies.items() if latency [1] [2]

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

A<

B100

C>

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' which would filter wrong regions.

Using 50 instead of 100 which changes the filter criteria.

The comprehension filters regions where latency is less than 100 milliseconds.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps instance IDs to their CPU utilization, filtering only instances with CPU utilization greater than 70%.

AWS

high_cpu_instances = { [1] : metrics[[2]] for [3] in metrics if metrics[[2]] [4] 70 }

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

Ainstance_id

B'CPUUtilization'

Cinstance

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable names for keys or loop variable.

Not using quotes around 'CPUUtilization'.

The comprehension maps each instance_id to its CPU utilization, filtering those with utilization greater than 70%.

Practice

1. What is the main goal of the AWS Performance Efficiency pillar?

easy

A. To make sure your system runs fast and adapts to changes

B. To secure your data from unauthorized access

C. To reduce the cost of your cloud resources

D. To organize your cloud resources in a neat way

Solution

1. Step 1: Understand the purpose of the Performance Efficiency pillar

   This pillar focuses on how well your system performs and adapts to changes in demand. Only To make sure your system runs fast and adapts to changes talks about running fast and adapting, which matches the pillar's goal.

2. Final Answer:

   To make sure your system runs fast and adapts to changes -> Option A

3. Quick Check:

   Performance Efficiency = Fast and adaptable system [OK]

Hint: Performance means speed and adaptability [OK]

Common Mistakes:

• Confusing performance with security
• Thinking cost reduction is the main goal
• Mixing organization with performance

2. Which AWS service helps automatically adjust the number of servers to meet demand?

easy

A. Amazon S3

B. Amazon RDS

C. AWS IAM

D. AWS Auto Scaling

Solution

1. Step 1: Identify the service that adjusts server count automatically

   AWS Auto Scaling changes the number of servers based on demand. Eliminate unrelated services: Amazon S3 stores files, Amazon RDS manages databases, and AWS IAM controls access, so they don't adjust server numbers.

2. Final Answer:

   AWS Auto Scaling -> Option D

3. Quick Check:

   Auto Scaling = Adjust servers automatically [OK]

Hint: Auto Scaling means automatic server adjustment [OK]

Common Mistakes:

• Choosing storage or database services instead
• Confusing IAM with scaling
• Not knowing what Auto Scaling does

3. Given this AWS setup: An application uses an Elastic Load Balancer (ELB) with Auto Scaling enabled. What happens when user traffic suddenly doubles?

medium

A. The application crashes due to overload

B. The ELB rejects extra traffic to keep servers stable

C. Auto Scaling adds more servers and ELB distributes traffic evenly

D. Auto Scaling removes servers to save costs

Solution

1. Step 1: Understand ELB and Auto Scaling roles

   ELB spreads traffic across servers; Auto Scaling adds servers when demand rises. Auto Scaling will add servers to handle more users, and ELB will balance the load.

2. Final Answer:

   Auto Scaling adds more servers and ELB distributes traffic evenly -> Option C

3. Quick Check:

   More traffic = More servers + balanced load [OK]

Hint: More traffic triggers Auto Scaling and ELB balances load [OK]

Common Mistakes:

• Thinking ELB rejects traffic
• Assuming the app crashes immediately
• Believing Auto Scaling removes servers on high load

4. You set up Auto Scaling but notice your application still slows down under heavy load. What is a likely cause?

medium

A. Auto Scaling is not configured with proper scaling policies

B. You used an Elastic Load Balancer correctly

C. Your database is too fast

D. You enabled monitoring and alerts

Solution

1. Step 1: Check Auto Scaling configuration

   If scaling policies are missing or incorrect, Auto Scaling won't add servers when needed. Using ELB correctly helps performance, a fast database is good, and monitoring helps detect issues, so they are not causes.

2. Final Answer:

   Auto Scaling is not configured with proper scaling policies -> Option A

3. Quick Check:

   Wrong scaling policies cause slow response [OK]

Hint: Check scaling policies if Auto Scaling fails [OK]

Common Mistakes:

• Blaming ELB when it's set up right
• Thinking a fast database causes slowdowns
• Ignoring the importance of scaling policies

5. You want to improve performance for a web app with unpredictable traffic spikes. Which combination best follows the Performance Efficiency pillar?

hard

A. Use fixed-size servers and manual traffic routing

B. Use Auto Scaling with Elastic Load Balancer and fast SSD storage

C. Use a single large server and no monitoring

D. Use slow storage but increase server size manually

Solution

1. Step 1: Identify best practices for unpredictable traffic

   Auto Scaling adjusts servers automatically, ELB balances traffic, and fast SSD storage speeds data access. Use Auto Scaling with Elastic Load Balancer and fast SSD storage uses all these features, while others rely on manual or slow methods.

2. Final Answer:

   Use Auto Scaling with Elastic Load Balancer and fast SSD storage -> Option B

3. Quick Check:

   Auto Scaling + ELB + fast storage = best performance [OK]

Hint: Combine Auto Scaling, ELB, and fast storage for spikes [OK]

Common Mistakes:

• Choosing fixed or manual scaling
• Ignoring load balancing
• Using slow storage for performance