Google Cloud

Build the Docker image from the application source.

Push the built image to Google Artifact Registry.

Deploy the container image as a Cloud Run service.

Verify the deployed service responds with HTTP 200.

Author the function source code and requirements file.

Deploy the function to Google Cloud Functions.

Set the function trigger to HTTP or Pub/Sub event.

Invoke the deployed function and verify the response.

Define the VM instance template with machine type, disk, and network.

Create a VM instance from the template.

Open necessary ports for HTTP, HTTPS, and SSH traffic.

Verify the VM is reachable via SSH.

Provision a GKE cluster with auto-scaling enabled.

Build the application Docker image.

Push the image to Google Container Registry.

Apply deployment, service, and ingress manifests to the cluster.

Wait for the deployment rollout to complete successfully.

Generate the App Engine configuration file with runtime, scaling, and environment settings.

Deploy the application to App Engine.

Shift 100% of traffic to the new version.

Confirm the application is live and responding correctly.

Create a new Cloud Storage bucket with a specified location and storage class.

Grant read/write access to the appropriate service accounts.

Upload files to the bucket.

Configure object lifecycle rules for automatic deletion or archival.

Provision a Filestore instance with the specified tier and capacity.

Mount the Filestore share on a client VM.

Write and read a test file to confirm the mount is working.

Create a backup schedule for the Filestore instance.

Create a Firestore database in Native mode.

Write Firestore security rules to control read/write access.

Create a sample document in a Firestore collection.

Run a structured query against the Firestore collection.

Create a composite index to optimize query performance.

Provision a Cloud SQL PostgreSQL instance with HA configuration.

Create the application database on the Cloud SQL instance.

Establish a connection using Cloud SQL Auth Proxy.

Execute database migrations to set up the schema.

Run a test query to confirm the migration was successful.

Create a BigQuery dataset in the specified location.

Load data from Cloud Storage into a BigQuery table.

Execute an analytical SQL query against the loaded data.

Export query results to Cloud Storage as a CSV file.

Set up a scheduled query to run daily.

Provision a Bigtable instance with the specified cluster configuration.

Create a table with column families.

Insert sample rows into the table.

Read back the inserted rows to verify writes.

Add a replication cluster for high availability.

Provision a Cloud Spanner instance with the desired node count.

Create a database within the Spanner instance.

Apply DDL statements to create tables and indexes.

Insert sample rows into the Spanner table.

Execute a SQL query to retrieve data from Spanner.

Upload and register the training dataset in Vertex AI.

Launch a custom training job on Vertex AI.

Deploy the trained model to a Vertex AI endpoint.

Send a prediction request to the deployed endpoint.

Enable model monitoring for data drift and prediction quality.

Set up the Gemini API key from environment or Secret Manager.

Send a text prompt to the Gemini API for generation.

Extract and validate the generated content from the API response.

Open a streaming connection for real-time token generation.

Upload the source document to Cloud Storage.

Create a Document AI processor of the desired type.

Send the document to the processor for analysis.

Parse the processor output and extract key-value pairs, tables, and entities.

Upload or reference an image for Vision AI analysis.

Request multiple annotation types: labels, text, faces, objects.

Parse the annotation response and extract structured label, text, and face detection results.

Format and return the extracted data as a structured JSON response.

Submit text content to the Natural Language API.

Determine the overall sentiment score and magnitude of the text.

Identify and extract named entities such as people, places, and organizations.

Classify the text into predefined content categories.

Create a Pub/Sub topic for publishing messages.

Create a pull subscription on the topic.

Publish a test message to the topic.

Pull the message from the subscription and acknowledge receipt.

Set up a dead-letter topic for messages that fail processing.

Create a Cloud Tasks queue with rate limiting configuration.

Enqueue a new task targeting the handler endpoint.

The handler endpoint processes the task payload.

Cloud Tasks automatically retries failed tasks with exponential backoff.

Generate the Cloud Build configuration file with build steps.

Submit the build to Cloud Build.

Execute the test suite as part of the build pipeline.

Push the built image to Artifact Registry.

Deploy the new image to the target environment.

Create a Docker repository in Artifact Registry.

Configure Docker to authenticate with Artifact Registry.

Tag and push the container image to the repository.

Run a vulnerability scan on the pushed image.

Pull the image from the repository to verify it is accessible.

Enable an authentication provider such as Email/Password or Google sign-in.

Create a new user account with email and password.

Authenticate the user and receive an ID token.

Verify the ID token on the server side using Firebase Admin SDK.

Exchange a refresh token for a new ID token.

Create a new service account for the application.

Bind IAM roles to the service account.

Create and download a service account key file.

Verify the service account has the expected permissions.

Create a backend service to serve as the CDN origin.

Enable Cloud CDN on the backend service.

Configure the cache TTL and cache key policy.

Purge cached content by path pattern.

Make a request and measure response latency to confirm CDN is serving.

Create a backend service with instance groups.

Create an HTTP health check for the backend.

Create a URL map to route requests to the backend.

Create a global forwarding rule to accept incoming traffic.

Send a request to the load balancer IP and confirm a successful response.