Cloud Run for containerized services in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to deploy a container image to Cloud Run.

GCP

gcloud run deploy my-service --image [1] --platform managed

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

Aus-central1

Bmy-service

Cdocker build

Dgcr.io/my-project/my-image:latest

Attempts:

3 left

💡 Hint

Common Mistakes

Using the service name instead of the image URL.

Using a region name where the image URL is expected.

The --image flag requires the container image URL to deploy to Cloud Run.

2fill in blank

medium

Complete the code to specify the region for Cloud Run deployment.

GCP

gcloud run deploy my-service --image gcr.io/my-project/my-image:latest --platform managed --region [1]

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

Aus-central1

Bmy-service

Clatest

Ddocker

Attempts:

3 left

💡 Hint

Common Mistakes

Using the service name instead of a region.

Using a tag like 'latest' where a region is expected.

The --region flag sets the geographic location where Cloud Run deploys your service.

3fill in blank

hard

Fix the error in the command to allow unauthenticated access to the Cloud Run service.

GCP

gcloud run deploy my-service --image gcr.io/my-project/my-image:latest --platform managed [1]

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

A--no-allow-unauthenticated

B--allow-unauthenticated

C--public-access

D--enable-unauthenticated

Attempts:

3 left

💡 Hint

Common Mistakes

Using the negative flag --no-allow-unauthenticated which denies public access.

Using incorrect or non-existent flags.

The flag --allow-unauthenticated enables public access to your Cloud Run service.

4fill in blank

hard

Fill both blanks to set the maximum number of instances and memory limit for the Cloud Run service.

GCP

gcloud run deploy my-service --image gcr.io/my-project/my-image:latest --platform managed --max-instances [1] --memory [2]

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

A5

B512Mi

C1Gi

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory values without units.

Setting max instances to a string instead of a number.

--max-instances limits the number of container instances to 5.
--memory sets the memory limit per instance to 512Mi (megabytes).

5fill in blank

hard

Fill all three blanks to create a Cloud Run service with environment variables and concurrency settings.

GCP

gcloud run deploy my-service --image gcr.io/my-project/my-image:latest --platform managed --set-env-vars [1] --concurrency [2] --region [3]

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

AENV=production

B80

Cus-east1

DDEBUG=true

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiple environment variables without commas.

Setting concurrency to a string instead of a number.

--set-env-vars sets environment variables like ENV=production.
--concurrency sets how many requests each instance can handle, here 80.
--region specifies the deployment region.

Practice

1. What is the main benefit of using Cloud Run for your containerized applications?

easy

A. It needs you to configure load balancers yourself.

B. It automatically scales your app based on traffic without managing servers.

C. It only works with virtual machines, not containers.

D. It requires you to manage virtual machines manually.

Solution

1. Step 1: Understand Cloud Run's serverless nature

   Cloud Run runs containers without requiring you to manage servers or infrastructure.

2. Step 2: Recognize automatic scaling feature

   Cloud Run automatically adjusts the number of container instances based on incoming traffic.

3. Final Answer:

   It automatically scales your app based on traffic without managing servers. -> Option B

4. Quick Check:

   Serverless + auto-scale = D [OK]

Hint: Cloud Run = serverless + auto scaling [OK]

Common Mistakes:

• Thinking you must manage servers manually
• Confusing Cloud Run with VM services
• Assuming manual load balancer setup is needed

2. Which of the following is the correct command to deploy a container image to Cloud Run?

easy

A. gcloud run deploy --image=gcr.io/my-project/my-image

B. gcloud compute instances create --image=gcr.io/my-project/my-image

C. kubectl run deploy --image=gcr.io/my-project/my-image

D. docker run deploy --image=gcr.io/my-project/my-image

Solution

1. Step 1: Identify the correct CLI tool for Cloud Run deployment

   The gcloud run deploy command is used to deploy container images to Cloud Run.

2. Step 2: Confirm the correct syntax

   The command includes the --image flag followed by the container image URL.

3. Final Answer:

   gcloud run deploy --image=gcr.io/my-project/my-image -> Option A

4. Quick Check:

   Cloud Run deploy uses gcloud run deploy [OK]

Hint: Use 'gcloud run deploy' to deploy containers [OK]

Common Mistakes:

• Using compute or kubectl commands instead of gcloud run
• Confusing docker run with Cloud Run deployment
• Missing the --image flag

3. Given this Cloud Run service configuration snippet, what will happen when traffic increases?

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: my-service
spec:
  template:
    spec:
      containers:
      - image: gcr.io/my-project/my-image
        resources:
          limits:
            cpu: 1
            memory: 512Mi

medium

A. The service will scale down to zero immediately.

B. Cloud Run will reject new requests once CPU limit is reached.

C. You must manually add more instances in the configuration.

D. Cloud Run will automatically create more container instances to handle the traffic.

Solution

1. Step 1: Understand Cloud Run's scaling behavior

   Cloud Run automatically scales container instances based on incoming traffic demand.

2. Step 2: Recognize resource limits do not prevent scaling

   Resource limits set max CPU/memory per instance but do not stop Cloud Run from adding instances.

3. Final Answer:

   Cloud Run will automatically create more container instances to handle the traffic. -> Option D

4. Quick Check:

   Auto scaling = more instances with traffic [OK]

Hint: Cloud Run auto scales instances, resource limits per instance [OK]

Common Mistakes:

• Thinking resource limits block scaling
• Assuming manual scaling is required
• Confusing scale down with scale up

4. You deployed a container to Cloud Run but get an error: Container failed to start. What is a common cause to check first?

medium

A. The container image does not expose the port Cloud Run expects.

B. You forgot to create a virtual machine instance.

C. Cloud Run requires a YAML file to deploy containers.

D. The container image must be in Docker Hub only.

Solution

1. Step 1: Understand Cloud Run port requirements

   Cloud Run expects the container to listen on port 8080 by default or the port set in the PORT environment variable.

2. Step 2: Check container image configuration

   If the container does not expose or listen on the expected port, it will fail to start properly.

3. Final Answer:

   The container image does not expose the port Cloud Run expects. -> Option A

4. Quick Check:

   Port mismatch causes start failure [OK]

Hint: Check container listens on port 8080 [OK]

Common Mistakes:

• Assuming VM instances are needed
• Thinking YAML is mandatory for deployment
• Believing container must be on Docker Hub

5. You want to deploy a containerized web app on Cloud Run that must handle sudden traffic spikes and also keep costs low when idle. Which setup is best?

hard

A. Deploy on Compute Engine with fixed VM instances.

B. Deploy on Cloud Run with minimum instances set to 5 to handle spikes.

C. Deploy on Cloud Run with autoscaling enabled and minimum instances set to zero.

D. Deploy on Cloud Run with manual scaling to 3 instances.

Solution

1. Step 1: Understand Cloud Run autoscaling and cost model

   Cloud Run automatically scales instances up and down based on traffic, and setting minimum instances to zero means no cost when idle.

2. Step 2: Compare options for cost and scalability

   Setting minimum instances above zero incurs cost even when idle; Compute Engine fixed VMs do not scale automatically; manual scaling requires more management.

3. Final Answer:

   Deploy on Cloud Run with autoscaling enabled and minimum instances set to zero. -> Option C

4. Quick Check:

   Auto scale + zero min instances = cost efficient [OK]

Hint: Zero min instances saves cost, autoscale handles spikes [OK]

Common Mistakes:

• Setting minimum instances above zero increases cost
• Using fixed VMs lacks automatic scaling
• Manual scaling requires extra management