Independent service pipelines in Microservices - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a microservice that listens to its own message queue.

Microservices

def start_service():
    queue = get_message_queue('[1]')
    while True:
        message = queue.receive()
        process_message(message)

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

Acommon_queue

Bshared_queue

Cglobal_queue

Dindependent_queue

Attempts:

3 left

💡 Hint

Common Mistakes

Using a shared queue causes message processing conflicts.

Using a global queue breaks service independence.

The microservice must listen to its own independent queue to ensure isolated processing pipelines.

2fill in blank

medium

Complete the code to deploy each microservice pipeline independently using containers.

Microservices

def deploy_pipeline(service_name):
    container = create_container(image=service_name)
    container.[1]()

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

Astop

Bstart

Cpause

Drestart

Attempts:

3 left

💡 Hint

Common Mistakes

Using stop instead of start prevents the service from running.

Restart is invalid before the container is started.

Starting the container launches the independent service pipeline.

3fill in blank

hard

Fix the error in the code to ensure each microservice pipeline scales independently.

Microservices

def scale_service(service, replicas):
    if replicas < 1:
        raise ValueError('Replicas must be at least [1]')
    service.set_replicas(replicas)

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

A0

B2

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Allowing zero replicas causes the service to stop.

Setting replicas less than one causes runtime errors.

At least one replica is needed to run the service pipeline.

4fill in blank

hard

Fill both blanks to configure independent pipelines with separate databases and APIs.

Microservices

pipeline_config = {
    'database': '[1]',
    'api_endpoint': '[2]'
}

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

Auser_db

Bshared_db

C/user/api

D/common/api

Attempts:

3 left

💡 Hint

Common Mistakes

Using shared_db breaks data isolation.

Using common API endpoints causes routing conflicts.

Each pipeline uses its own database and API endpoint for independence.

5fill in blank

hard

Fill all three blanks to implement independent logging, monitoring, and alerting for each pipeline.

Microservices

def setup_pipeline_monitoring(pipeline):
    pipeline.logger = Logger('[1]')
    pipeline.monitor = Monitor('[2]')
    pipeline.alert_system = AlertSystem('[3]')

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

Apipeline_logs

Bpipeline_metrics

Cpipeline_alerts

Dshared_logs

Attempts:

3 left

💡 Hint

Common Mistakes

Using shared_logs mixes logs from different pipelines.

Not configuring alerts per pipeline delays issue detection.

Each pipeline must have its own logs, metrics, and alerts to maintain independence.