Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer Question and Answers

https://kubernetes.io/docs/tasks/debug-application-cluster/debug-application/#debugging-pods

This answer is the most effective way to validate whether the service account used by the CI/CD server has the appropriate roles in the specific project. By checking the IAM roles assigned to the service account, you can see which permissions the service account has and which resources it can access. You can also check if the service account inherits any roles from the folder or organization levels, which may affect its access to the project. You can use the Google Cloud console, the gcloud command-line tool, or the IAM API to view the IAM roles of a service account.

The goal is to enable teams to deploy infrastructure independently while ensuring compliance with company practices, without requiring teams to understand the underlying details of those practices.

Option A provides deployment capability but doesn ' t enforce practices. The Editor role is overly broad, and using the gcloud CLI directly requires knowledge of how to configure resources compliantly.

Option B requires teams to learn all the practices, contradicting the requirement that they don ' t need to know the implementation details.

Option C (Organization Policies) is useful for setting constraints (e.g., disallowing public IPs, restricting regions), but it doesn ' t provide pre-configured, deployable components that embody best practices. Teams still need to figure out how to build compliant resources within the policy constraints.

Option D (Terraform Modules): This approach encapsulates the company ' s required practices within reusable infrastructure-as-code modules. Engineering teams can then use these modules as building blocks, providing only the necessary input parameters (like application name orsize). The module handles the compliant implementation details internally. This allows teams to deploy independently and ensures compliance without needing deep knowledge of every practice.

Using standardized, compliant modules is a common pattern for enabling self-service infrastructure deployment while maintaining standards and governance.

https://cloud.google.com/bigquery/docs/custom-quotashttps://cloud.google.com/bigquery/pricing#flat_rate_pricing

https://cloud.google.com/sdk/gcloud/reference/projects/create

Quickstart: Creating a New Instance Using the Command Line

Before you begin

1. In the Cloud Console, on the project selector page, select or create a Cloud project.

2. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.

To use the gcloud command-line tool for this quickstart, you must first install and initialize the Cloud SDK:

1. Download and install the Cloud SDK using the instructions given on Installing Google Cloud SDK.

2. Initialize the SDK using the instructions given on Initializing Cloud SDK.

To use gcloud in Cloud Shell for this quickstart, first activate Cloud Shell using the instructions given on Starting Cloud Shell.

https://cloud.google.com/ai-platform/deep-learning-vm/docs/quickstart-cli#before-you-begin

Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.

Log entries are held in Stackdriver Logging for a limited time known as the retention period  which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.

Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging

Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.

While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.

You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.

Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks

Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.

Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.

You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.

Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks

Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.

Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.

For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.

Ref: https://cloud.google.com/bigquery/docs/best-practices-storage

GCP App Engine natively offers traffic splitting functionality between versions. You can use traffic splitting to specify a percentage distribution of traffic across two or more of the versions within a service. Splitting traffic allows you to conduct A/B testing between your versions and provides control over the pace when rolling out features.

Ref: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic

The resource that you need to get access is in the other project.

roles/bigquery.dataViewer BigQuery Data Viewer

When applied to a table or view, this role provides permissions to:

Read data and metadata from the table or view.

This role cannot be applied to individual models or routines.

When applied to a dataset, this role provides permissions to:

Read the dataset ' s metadata and list tables in the dataset.

Read data and metadata from the dataset ' s tables.

When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

https://blog.doit-intl.com/the-truth-behind-google-cloud-egress-traffic-6e8f57b5c2f8

The critical constraint for both the website and the background job is the requirement for autoscaling and no costs when not in use (scale to zero) . Both workloads are suitable for containerization and HTTP invocation.

Cloud Run is the single best platform for both use cases under these constraints. It is serverless, fully managed, and natively supports scaling to zero when a containerized service is not receiving requests, making it the most cost-effective option for sporadic workloads.

The static website can run in a simple web server container on Cloud Run. The HTTP-triggered background job is a perfect fit for a Cloud Run service that runs, performs its work (invoice generation), and then scales down to zero.

(A) Object Lifecycle Management

Delete

The Delete action deletes an object when the object meets all conditions specified in the lifecycle rule.

Exception: In buckets with Object Versioning enabled, deleting the live version of an object causes it to become a noncurrent version, while deleting a noncurrent version deletes that version permanently.

https://cloud.google.com/storage/docs/lifecycle#delete

(B) Signed URLs

This page provides an overview of signed URLs, which you use to give time-limited resource access to anyone in possession of the URL, regardless of whether they have a Google account

https://cloud.google.com/storage/docs/access-control/signed-urls

https://cloud.google.com/compute/docs/instances/managing-instance-access

You can create two configurations – one for the development project and another for the production project. And you do that by running “gcloud config configurations create” command.

https://cloud.google.com/sdk/gcloud/reference/config/configurations/create

In your custom script, you can load these configurations one at a time and execute gcloud compute instances list to list Google Compute Engine instances in the project that is active in the gcloud configuration.

Ref: https://cloud.google.com/sdk/gcloud/reference/compute/instances/list

Once you have this information, you can export it in a suitable format to a suitable target e.g. export as CSV or export to Cloud Storage/BigQuery/SQL, etc

Read the dataset ' s metadata and to list tables in the dataset. Read data and metadata from the dataset ' s tables. When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

BigQuery Data Viewer

(roles/bigquery.dataViewer)

When applied to a table or view, this role provides permissions to:

Read data and metadata from the table or view.

This role cannot be applied to individual models or routines.

When applied to a dataset, this role provides permissions to:

Read the dataset ' s metadata and list tables in the dataset.

Read data and metadata from the dataset ' s tables.

When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

Lowest-level resources where you can grant this role:

Table

View

BigQuery Job User

(roles/bigquery.jobUser)

Provides permissions to run jobs, including queries, within the project.

Lowest-level resources where you can grant this role:

Project

to run jobs https://cloud.google.com/bigquery/docs/access-control#bigquery.jobUser databaseUser needs additional role permission to run jobshttps://cloud.google.com/spanner/docs/iam#spanner.databaseUser

Comprehensive and Detailed In Depth Explanation:

Let ' s evaluate each database option against the requirements: automatic scaling, multi-region support, and low latency for a growing social media platform:

A. Bigtable: Bigtable is a highly scalable NoSQL database designed for large analytical and operational workloads with low latency. It offers excellent horizontal scalability and can be deployed across multiple regions for high availability and lower latency for a global user base. However, it ' s a NoSQL database and might require significant changes to your existing PostgreSQL data model and application code.

B. BigQuery: BigQuery is a fully managed, serverless data warehouse optimized for analytical queries on large datasets. It ' s not designed for low-latency transactional workloads that a social media platformwould require for real-time user interactions. While it ' s globally available, its primary use case is not operational database needs.

C. Spanner: Spanner is a globally distributed, horizontally scalable relational database service with strong consistency. It offers automatic scaling, built-in multi-region and multi-continental configurations for high availability and low latency across a global user base, and supports standard SQL (with some extensions). This makes it a strong candidate for a rapidly growing platform needing scalability, global presence, and low latency. While it ' s not directly PostgreSQL, it offers a relational model and tools to aid migration.

D. Cloud SQL for PostgreSQL: Cloud SQL offers managed PostgreSQL instances with automatic scaling capabilities. It supports high availability within a region and cross-region read replicas for disaster recovery and read scaling. However, its multi-region capabilities for write operations and automatic scaling across regions are more limited compared to Spanner. For a rapidly growing platform with a primarily North American user base but future global expansion in mind and a need for low latency, Spanner ' s architecture is better suited for true multi-region write capabilities and consistent low latency globally.

Considering the requirements for automatic scaling, multi-region support for both reads and writes with low latency for a growing user base, Spanner is the most appropriate choice.

Google Cloud Documentation References:

Cloud Spanner Overview: https://cloud.google.com/spanner/docs/overview - This document highlights Spanner ' s global scalability, strong consistency, and multi-region capabilities.

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - While scalable and low-latency, it ' s a NoSQL database, which might require significant application changes.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - Focuses on analytics, not low-latency transactional workloads.

Cloud SQL for PostgreSQL Overview: https://cloud.google.com/sql/docs/postgres/overview - While it offers scaling and regional HA, its multi-region write capabilities are not as robust as Spanner ' s.

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Database Center is the correct choice because it is designed to provide a centralized view of Google Cloud database and data storage services, including operational health and availability-related insights. The requirement is not only to list assets but to monitor the health of multiple managed data services and identify downtime risk. Dataplex Universal Catalog is mainly for data discovery, governance, and metadata search, not operational database health monitoring. Terraform state only reflects resources managed by Terraform and does not provide live service health or risk signals. Cloud Asset Inventory can show resource inventory, but it is not the best operational console for database health. Database Center gives the unified operational overview required with the least manual effort.

https://cloud.google.com/sql/docs/postgres

Quickstart: using the Google Cloud Console

This page shows you how to perform basic tasks in Pub/Sub using the Google Cloud Console.

Note: If you are new to Pub/Sub, we recommend that you start with the interactive tutorial.

Before you begin

Set up a Cloud Console project.

Set up a project

Click to:

Create or select a project.

Enable the Pub/Sub API for that project.

You can view and manage these resources at any time in the Cloud Console.

Install and initialize the Cloud SDK.

Note: You can run the gcloud tool in the Cloud Console without installing the Cloud SDK. To run the gcloud tool in the Cloud Console, use Cloud Shell .

https://cloud.google.com/pubsub/docs/quickstart-console

IP 10.0.3.21 is internal by default, and to ensure that it will be static non-changing it should be selected as static internal ip address.

https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints This list constraint defines the set of domains that email addresses added to Essential Contacts can have. By default, email addresses with any domain can be added to Essential Contacts. The allowed/denied list must specify one or more domains of the form @example.com. If this constraint is active and configured with allowed values, only email addresses with a suffix matching one of the entries from the list of allowed domains can be added in Essential Contacts. This constraint has no effect on updating or removing existing contacts. constraints/essentialcontacts.allowedContactDomains

https://console.cloud.google.com/marketplace/details/gc-launcher-for-mongodb-atlas/mongodb-atlas

Comprehensive and Detailed Explanation From Exact Extract:

The issue is Cloud SQL API quota errors caused by too many new Cloud Run instances being created simultaneously during a traffic spike. Each new instance attempts a connection to Cloud SQL, consuming the connection API quota.

The Documented Solution (Not an Option): Google ' s official documentation states the correct mitigation for this specific error is to decrease the maximum number of Cloud Run instances to slow the rate of new instance creation.

Flawed Option Set: Since the correct mitigation is not an option, and increasing the maximum instances (D) would technically worsen the quota error, the question is structurally flawed. However, in the context of general autoscaling management, Option D is the configuration control point for the upper limit of scaling. In many exam scenarios, this forces the selection of the control that directly impacts the scaling capacity, even if the intended direction is incorrect for the specific error given. We select D as the configuration change most directly related to the number of instances, while acknowledging the documentation advises the opposite action.

An external passthrough Network Load Balancer operates at layer 4 (TCP/UDP) and preserves the original client IP address. This is a key requirement stated in the question. It ' s also designed for external-facing applications that need to handle TCP or UDP traffic directly without proxying at the application layer.

Option A: An internal passthrough Network Load Balancer is used for load balancing traffic within a Virtual Private Cloud (VPC) network, not for exposing applications to the public internet.

Option C & D: Global and regional external proxy Network Load Balancers operate at layer 7 (HTTP/HTTPS) or use a proxy for TCP/SSL, which means they terminate the client connection at the load balancer and establish a new connection to the backend instances. This does not preserve the original client IP address by default (though there are ways to retrieve it via headers, it ' s not a direct passthrough).

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The different types of Google Cloud Load Balancers, their operating layers (L4 vs. L7), and their capabilities, including client IP preservation, are thoroughly documented in the Google Cloud Load Balancing documentation, a critical area for the Associate Cloud Engineer certification. The distinction between passthrough and proxy load balancers is essential.

The correct answer is D because Kubernetes provides a direct rollback command for failed or problematic rollouts. In GKE, deployments are managed using standard Kubernetes deployment mechanics, so kubectl rollout undo is the correct command to return a workload to the previous rollout revision. Kubernetes documentation describes kubectl rollout undo as the command used to roll back to a previous rollout and gives deployment rollback examples. Manually scaling old and new Pods is risky because it bypasses deployment controller state and can create configuration drift. Deleting the deployment causes service disruption and removes the workload object. kubectl rollout restart restarts Pods using the current deployment specification; it does not revert to a previous revision.

https://cloud.google.com/iap/docs/using-tcp-forwarding

https://cloud.google.com/logging/docs/audit

Data Access audit logs Data Access audit logs contain API calls that read the configuration or metadata of resources, as well as user-driven API calls that create, modify, or read user-provided resource data.

https://cloud.google.com/logging/docs/audit#data-access

https://cloud.google.com/vpc/docs/vpc#manually_created_subnet_ip_ranges

Comprehensive and Detailed Explanation From Exact Extract:

The requirements point to the highest performance tier for a managed PostgreSQL-compatible database, specifically citing demands for transactional and analytical performance, synchronous replication, and storage optimization.

AlloyDB for PostgreSQL is Google Cloud ' s highly specialized, fully managed database that is optimized for demanding enterprise workloads. It is superior to standard Cloud SQL for performance and architectural features like synchronous replication and an optimized storage layer, directly matching all the stated requirements.

Database Migration Service (DMS) is the recommended tool for performing the migration itself.

The correct answer is C because Cloud Monitoring already collects Compute Engine CPU utilization metrics and supports alerting policies with thresholds and duration windows. The requirement is to notify the operations team when average CPU utilization exceeds 75% for five continuous minutes, which maps directly to a Cloud Monitoring metric-based alert policy. Logs-based metrics are unnecessary because CPU utilization is not primarily a log event and is already available as a platform metric. Installing agents and writing custom polling scripts adds operational overhead and is less scalable. Autoscaling policies adjust capacity, but they do not replace alerting policies and do not provide the requested notification workflow by themselves. Cloud Monitoring is the native, low-maintenance solution.

Let ' s evaluate each option based on the requirement of sub-millisecond latency for globally stored IoT data:

A. Spanner with Caching: While Spanner offers strong consistency and global scalability, the base latency might not consistently be sub-millisecond for all read/write operations globally. Introducing caching adds complexity and doesn ' t guarantee sub-millisecond latency for all initial reads or cache misses.

B. Bigtable: Bigtable is a highly scalable NoSQL database service designed for low-latency, high-throughput workloads. It excels at storing and retrieving large volumes of time-series data, which is typical for IoT sensor data. Its architecture is optimized for single-key lookups and scans, providing consistent sub-millisecond latency, making it a strong candidate for this use case.

C. BigQuery: BigQuery is a fully managed, serverless data warehouse designed for analytical queries on large datasets. While it ' s excellent for analyzing IoT data in batch, it ' s not optimized for the low-latency, high-throughput ingestion and retrieval required for real-time IoT applications with sub-millisecond latency needs.

D. Cloud Storage with Cloud CDN: Cloud Storage is object storage and is not designed for low-latency transactional workloads. Cloud CDN is a content delivery network that caches content closer to users for faster delivery, but it ' s not suitable for the primary storage of rapidly incoming IoT sensor data requiring sub-millisecond write latency.

Google Cloud Documentation References:

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This document highlights Bigtable ' s suitability for low-latency and high-throughput applications, including IoT. It mentions its ability to handle massive amounts of data with consistent performance.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - While Spanner offers low latency, Bigtable is generally preferred for extremely high-throughput, low-latency use cases like raw sensor data ingestion due to its optimized architecture for such workloads.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - This emphasizes BigQuery ' s analytical capabilities rather than low-latency operational workloads.

Cloud Storage Overview: https://cloud.google.com/storage/docs/overview - This describes Cloud Storage as object storage, not ideal for sub-millisecond latency reads and writes required for real-time IoT data.

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A deployment is responsible for keeping a set of pods running. A service is responsible for enabling network access to a set of pods.

The organization policy explicitly prevents the use of service account keys, so options A and B, which involve requesting exceptions to create them, are not in line with the policy and Google ' s recommended practices for secure authentication.

Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app //https://cloud.google.com/debugger/docs

Storage Admin (roles/storage.admin) Grants full control of buckets and objects.

When applied to an individual bucket, control applies only to the specified bucket and objects within the bucket.

firebase.projects.get

resourcemanager.projects.get

resourcemanager.projects.list

storage.buckets.*

storage.objects.*

https://cloud.google.com/storage/docs/access-control/iam-roles

This role grants full control of buckets and objects. When applied to an individual bucket, control applies only to the specified bucket and objects within the bucket.

Ref: https://cloud.google.com/iam/docs/understanding-roles#storage-roles

The correct answer is B because programmatic budget notifications are intended for event-driven budget alert automation. Cloud Billing can publish budget threshold messages to Pub/Sub, and a Cloud Run function can be triggered by those messages to post to Slack. This gives near real-time notification without running persistent infrastructure. BigQuery billing export is valuable for analysis, but scheduled queries or DAGs are not near real time and add maintenance overhead. A Compute Engine managed instance group requires servers to run and manage, which is unnecessary for a simple notification workflow. Pub/Sub plus Cloud Run function is serverless, event-driven, easier to support, and directly aligned with Google Cloud’s cost-control automation pattern.

The best option is to attach a single service account to the compute instances and add minimal rights to the service account. Then, allow the service account to impersonate a Cloud Identity user with elevated permissions to create, update, or delete resources. This way, the service account can use short-lived access tokens to authenticate to Google Cloud APIs without needing to manage service account keys. This option follows the principle of least privilege and reduces the risk of credential leakage and misuse.

Option A is not recommended because it requires human intervention, which can slow down the CI/CD pipeline and introduce human errors. Option C is not secure because it grants all required IAM permissions to a single service account, which can increase the impact of a compromised key. Option D is not cost-effective because it requires creating and managing multiple service accounts and keys, as well as using a secret manager service.

1: https://cloud.google.com/iam/docs/impersonating-service-accounts

2: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys

3: https://cloud.google.com/iam/docs/understanding-service-accounts

Comprehensive and Detailed In Depth Explanation:

The requirement is to export logs from Cloud Logging to a third-party SaaS tool with the least amount of delay possible. Let ' s analyze each option:

A. Cloud Scheduler, Cloud Function, and querying Cloud Logging: This approach introduces a delay based on the Cloud Scheduler ' s cron job frequency. The Cloud Function would periodically query Cloud Logging, which might not capture the logs in real-time. This does not meet the " least amount of delay possible " requirement.

B. Cloud Logging sink to Pub/Sub, SaaS tool subscribing to Pub/Sub: Cloud Logging sinks can be configured to export logs in near real-time as they are ingested into Cloud Logging. Pub/Sub is a messaging service designed for asynchronous and near real-time message delivery. By configuring the sink to send logs to a Pub/Sub topic, and having the SaaS tool subscribe to this topic, logs can be delivered to the SaaS tool with minimal delay. This aligns with the requirement for immediate export.

C. Cloud Logging sink to Cloud Storage, SaaS tool reading Cloud Storage: Exporting logs to Cloud Storage involves a batch-oriented approach. Logs are typically written to files periodically. The SaaS tool would then need to poll or be configured to read these files, introducing a significant delay compared to a streaming approach.

D. Cloud Logging sink to BigQuery, SaaS tool querying BigQuery: Similar to Cloud Storage, exporting to BigQuery is more suitable for analytical purposes. The SaaS tool would need to periodically queryBigQuery, which introduces latency and is not the most efficient way to achieve near real-time log delivery.

Therefore, configuring a Cloud Logging sink to Pub/Sub and having the SaaS tool subscribe to the Pub/Sub topic provides the lowest latency for exporting logs.

Google Cloud Documentation References:

Cloud Logging Sinks Overview: https://cloud.google.com/logging/docs/export/configure_export_v2 - This document explains how to create and manage Cloud Logging sinks, including the available destinations.

Pub/Sub Overview: https://cloud.google.com/pubsub/docs/overview - This highlights Pub/Sub ' s capabilities for real-time message delivery and its use cases in streaming data.

Exporting Logs with Cloud Logging: https://cloud.google.com/logging/docs/export - This provides a comprehensive guide to exporting logs from Cloud Logging to various destinations, emphasizing Pub/Sub for streaming.

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Change your default zone and region in the metadata server Note: This only applies to the default configuration. You can change the default zone and region in your metadata server by making a request to the metadata server. For example: gcloud compute project-info add-metadata \ --metadata google-compute-default-region=europe-west1,google-compute-default-zone=europe-west1-b The gcloud command-line tool only picks up on new default zone and region changes after you rerun the gcloud init command. After updating your default metadata, run gcloud init to reinitialize your default configuration.https://cloud.google.com/compute/docs/gcloud-compute#change_your_default_zone_and_region_in_the_metadata_server

https://cloud.google.com/config-connector/docs/how-to/secrets#gcloud

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Since we have a requirement to access data once a quarter and want to go with the most cost-efficient option, we should select Coldline Storage.

Ref: https://cloud.google.com/storage/docs/storage-classes#coldline

https://cloud.google.com/vpc/docs/vpc-peering

https://cloud.google.com/run/docs/tutorials/gcloud

https://cloud.google.com/resource-manager/docs/creating-managing-projects

https://cloud.google.com/iam/docs/understanding-roles#primitive_roles

You can shut down projects using the Cloud Console. When you shut down a project, this immediately happens: All billing and traffic serving stops, You lose access to the project, The owners of the project will be notified and can stop the deletion within 30 days, The project will be scheduled to be deleted after 30 days. However, some resources may be deleted much earlier.

The correct answer is A because Google Cloud Billing budgets and alerts handle threshold-based notifications without custom engineering, while Cloud Billing export to BigQuery provides detailed billing data for dashboards and deeper cost analysis. This directly meets both requirements: notifications when spend reaches thresholds and dashboard creation for detailed insights. Writing scripts to call the Cloud Billing API adds unnecessary engineering and maintenance because billing export already provides a managed data pipeline into BigQuery. Deploying Grafana on Compute Engine introduces server management and extra operational cost. Asking teams to create their own alerting stack is less controlled and less aligned with Google-recommended managed services. Budget alerts plus BigQuery export is simpler, scalable, and cost-effective.

https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-migs

https://cloud.google.com/compute/docs/instance-templates#how_to_update_instance_templates

According to the Google Cloud documentation, you can use organization policy constraints to control the creation and expiration of service account keys. The constraints are:

constraints/iam.allowServiceAccountKeyCreation: This constraint allows you to specify which projects or folders can create service account keys. You can set the value to true or false, or use a condition to apply the constraint to specific service accounts. By setting this constraint to false for the organization and adding an exception for the pj-sa project, you can prevent developers from creating service account keys in other projects.

constraints/iam.serviceAccountKeyMaxLifetime: This constraint allows you to specify the maximum lifetime of service account keys. You can set the value to a duration in seconds, such as 86400 for one day. By setting this constraint to 86400 for the organization, you can ensure that all service account keys expire after one day.

These constraints are recommended by Google Cloud as best practices to minimize the risk of service account key misuse or compromise. They also help you reduce the cost of managing service account keys, as you do not need to implement a custom solution to rotate or delete them.

1: Associate Cloud Engineer Certification Exam Guide | Learn - Google Cloud

5: Create and delete service account keys - Google Cloud

Organization policy constraints for service accounts

Cloud Run takes any container images and pairs great with the container ecosystem: Cloud Build, Artifact Registry, Docker. ... No infrastructure to manage: once deployed, Cloud Run manages your services so you can sleep well. Fast autoscaling. Cloud Run automatically scales up or down from zero to N depending on traffic.

https://cloud.google.com/run

The correct answer is B because Cloud Storage is the appropriate Google-managed object storage service for durable document storage, and Object Lifecycle Management can automatically transition objects to a lower-cost storage class after a defined age. The documents are frequently accessed for the first six months, so keeping them initially in Standard storage is reasonable. After six months, the documents are retained mainly for audit purposes, making Coldline more cost-effective for infrequently accessed data. Option A is backward because Standard is the higher-access storage class, not the archive target. Filestore is a managed file system, not the best low-cost object archive for invoices. Moving data manually or through Filestore adds unnecessary operational overhead.

The correct answer is A because the error explicitly says that the Kubernetes Engine API has not been used before or is disabled in the project. Google Cloud services must be enabled before their APIs can be used, even when the user has sufficient IAM permissions. The command gcloud services enable container.googleapis.com enables the GKE API for the selected project. After that, the original cluster creation command can be rerun. Disabling a Workload Identity Federation organization policy is unrelated to this API-disabled error. The --enable-autoprovisioning flag configures node auto-provisioning and does not enable the GKE API. Adding Kubernetes Engine Admin permissions may be necessary for authorization, but it does not solve a disabled API condition.

https://www.cloudskillsboost.google/focuses/1035?parent=catalog#:~:text=custom%20role%20at%20the%20organization%20level

Cloud Spanner is a relational database and is highly scalable. Cloud Spanner is a highly scalable, enterprise-grade, globally-distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with a non-relational horizontal scale. This combination delivers high-performance transactions and strong consistency across rows, regions, and continents with an industry-leading 99.999% availability SLA, no planned downtime, and enterprise-grade security

Ref: https://cloud.google.com/spanner

Graphical user interface, application, Teams Description automatically generated

A custom image belongs only to your project. To create an instance with a custom image, you must first have a custom image.

The correct answer is B because the metric needed is application-specific: the number of new database connections created by the application. Cloud Run instance creation is not the same as database connection creation, so option A measures the wrong thing. Cloud SQL logs may show database-side activity, but they do not reliably express the application’s connection reuse behavior in the required form. Cloud Trace is for request tracing and latency analysis, not counting newly created database connections. By logging each new connection in the application and creating a log-based metric, Cloud Monitoring can chart the exact behavior. Google Cloud supports user-defined log-based metrics for project-specific application or business metrics.

Standard persistent disks are efficient and economical for handling sequential read/write operations, but they aren ' t optimized to handle high rates of random input/output operations per second (IOPS). If your apps require high rates of random IOPS, use SSD persistent disks. SSD persistent disks are designed for single-digit millisecond latencies. Observed latency is application specific.

Cloud Storage allows world-wide storage and retrieval of any amount of data at any time. We dont need to set up auto-scaling ourselves. Cloud Storage autoscaling is managed by GCP. Cloud Storage is an object store so it is suitable for storing photos. Cloud Storage allows world-wide storage and retrieval so cater well to our worldwide audience. Cloud storage provides us lifecycle rules that can be configured to automatically delete objects older than 30 days. This also fits our requirements. Finally, Google Cloud Storage offers several storage classes such as Nearline Storage ($0.01 per GB per Month) Coldline Storage ($0.007 per GB per Month) and Archive Storage ($0.004 per GB per month) which are significantly cheaper than any of the options above.

Ref: https://cloud.google.com/storage/docs

Ref: https://cloud.google.com/storage/pricing

https://cloud.google.com/run/docs/tutorials/pubsub#integrating-pubsub

1. Create a service account. 2. Give the Cloud Run Invoker role to that service account for your Cloud Run application. 3. Create a Cloud Pub/Sub subscription that uses that service account and uses your Cloud Run application as the push endpoint.

https://developers.google.com/cloud-search/docs/guides/audit-logging-manual

Cloud Functions is Google Cloud’s event-driven serverless compute platform that lets you run your code locally or in the cloud without having to provision servers. Cloud Functions scales up or down, so you pay only for compute resources you use. Cloud Functions have excellent integration with Cloud Pub/Sub, lets you scale down to zero and is recommended by Google as the ideal serverless platform to use when dependent on Cloud Pub/Sub.

" If you’re building a simple API (a small set of functions to be accessed via HTTP or Cloud Pub/Sub), we recommend using Cloud Functions. "

Ref: https://cloud.google.com/serverless-options

gcloud deployment-manager deployments create creates deployments based on the configuration file. (Infrastructure as code). All the configuration related to the artifacts is in the configuration file. This command correctly creates a cluster based on the provided cluster.yaml configuration file.

Ref: https://cloud.google.com/sdk/gcloud/reference/deployment-manager/deployments/create

Instance groups are collections of virtual machine (VM) instances that you can manage as a single entity. Instance groups can help you simplify the management of multiple instances, reduce operational cost s, and improve the availability and performance of your applications. Instance groups support autoscaling, which automatically adds or removes instances from the group based on increases or decreases in load. Autoscaling helps your applications gracefully handle increases in traffic and reduces cost when the need for resources is lower. You can set the autoscaling policy based on CPU utilization, load balancing capacity, Cloud Monitoring metrics, or a queue-based workload. In this case, since the video encoding software is CPU-intensive, setting the autoscaling based on CPU utilization is the best option to ensure high availability and optimal performance. References:

Instance groups

Autoscaling groups of instances

The reason is that when you do health check, you want the VM to be working. Do the first check after initial setup time of 3 mins = 180 s < 200 s is reasonable.

The reason why our autoscaling is adding more instances than needed is that it checks 30 seconds after launching the instance and at this point, the instance isnt up and isnt ready to serve traffic. So our autoscaling policy starts another instance  again checks this after 30 seconds and the cycle repeats until it gets to the maximum instances or the instances launched earlier are healthy and start processing traffic  which happens after 180 seconds (3 minutes). This can be easily rectified by adjusting the initial delay to be higher than the time it takes for the instance to become available for processing traffic.So setting this to 200 ensures that it waits until the instance is up (around 180-second mark) and then starts forwarding traffic to this instance. Even after a cool out period, if the CPU utilization is still high, the autoscaler can again scale up but this scale-up is genuine and is based on the actual load.

Initial Delay Seconds  This setting delays autohealing from potentially prematurely recreating the instance if the instance is in the process of starting up. The initial delay timer starts when the currentAction of the instance is VERIFYING.

Ref: https://cloud.google.com/compute/docs/instance-groups/autohealing-instances-in-migs

https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed-instance-groups#max_unavailable

as gcloud config configurations list can help check for the existing configurations and activate can help switch to the configuration.

gcloud config configurations list  lists existing named configurations

gcloud config configurations activate  activates an existing named configuration

Obtains access credentials for your user account via a web-based authorization flow. When this command completes successfully, it sets the active account in the current configuration to the account specified. If no configuration exists, it creates a configuration named default.

https://cloud.google.com/appengine/docs/flexible/managing-projects-apps-billing#:~:text=Each%20Cloud%20project%20can%20contain%20only%20a%20single%20App%20Engine%20application%2C%20and%20once%20created%20you%20cannot%20change%20the%20location%20of%20your%20App%20Engine%20application.

Two App engine can ' t be running on the same project: you can check this easy diagram for more info:https://cloud.google.com/appengine/docs/standard/an-overview-of-app-engine#components_of_an_application

And you can ' t change location after setting it for your app Engine.https://cloud.google.com/appengine/docs/standard/locations

App Engine is regional and you cannot change an apps region after you set it. Therefore, the only way to have an app run in another region is by creating a new project and targeting the app engine to run in the required region (asia-northeast1 in our case).

Ref: https://cloud.google.com/appengine/docs/locations

Managed instance groups offer autoscaling capabilities that let you automatically add or delete instances from a managed instance group based on increases or decreases in load (CPU Utilization in this case). Autoscaling helps your apps gracefully handle increases intraffic and reduce costs when the need for resources is lower. You define the autoscaling policy and the autoscaler performs automatic scaling based on the measured load (CPU Utilization in this case). Autoscaling works by adding more instances to your instance group when there is more load (upscaling), and deleting instances when the need for instances is lowered (downscaling). Ref:https://cloud.google.com/compute/docs/autoscaler

When we create subnets in the same VPC with different CIDR ranges, they can communicate automatically within VPC. Resources within a VPC network can communicatewith one another by using internal (private) IPv4 addresses, subject to applicable network firewall rules

Ref: https://cloud.google.com/vpc/docs/vpc

The correct answer is B because independent applications should have independent Terraform root configurations and state boundaries. Google Cloud Terraform best practices state that to manage applications and projects independently, resources for each application and project should be placed in their own Terraform directories. This avoids a single oversized root module and reduces coupling between unrelated applications. Grouping by resource type creates artificial coupling because one application change can affect the same shared state as other applications. Putting all modules in one directory does not establish independent root configurations. A root directory per application gives clearer ownership, safer deployments, and better separation of lifecycle, state, and permissions.

The correct answer is B because the task is not asking you to invoke the Cloud Run service yourself; it asks for permission to manage IAM policy related to service accounts so the new service account can be granted the required access. The roles/run.invoker role would allow a principal to invoke a Cloud Run service, but it would not give your user permission to modify service account IAM policies. Editor and Owner are excessive and violate least privilege because they grant broad project-level permissions. The roles/iam.serviceAccountAdmin role is the most appropriate option listed because it provides administrative control over service accounts, including managing their configuration and IAM-related access without granting full project ownership.

https://cloud.google.com/iam/docs/best-practices-for-using-and-managing-service-accounts

If an application uses third-party or custom identities and needs to access a resource, such as a BigQuery dataset or a Cloud Storage bucket, it must perform a transition between principals. Because Google Cloud APIs don ' t recognize third-party or custom identities, the application can ' t propagate the end-user ' s identity to BigQuery or Cloud Storage. Instead, the application has to perform the access by using a different Google identity.

The correct answer is C because the requirement is prefix-scoped read access in an existing shared bucket with minimal ongoing maintenance. Uniform bucket-level access centralizes access control through IAM instead of object ACLs. An IAM Condition can restrict the Storage Object Viewer role so the Cloud Run service identity can read only objects whose names match the site1-data/ prefix. This also covers new objects automatically as they are uploaded, as long as their object names match the prefix. Per-object ACLs require ongoing uploader configuration and are operationally fragile. Creating a new bucket changes the storage design and may require migration. Granting objectViewer at the project level is far too broad and violates least privilege.

Google HTTP(S) Load Balancing has native support for the WebSocket protocol when you use HTTP or HTTPS, not HTTP/2, as the protocol to the backend.

Ref: https://cloud.google.com/load-balancing/docs/https#websocket_proxy_support

So the next possible step is to Meet with the cloud enablement team to discuss load balancer options.

We dont need to convert WebSocket code to use HTTP streaming or Redesign the application, as WebSocket support is offered by Google HTTP(S) Load Balancing. Reviewing the encryption requirements is a good idea but it has nothing to do with WebSockets.

https://cloud.google.com/logging/docs/api/v2/resource-list

GKE Containers have more log than GKE Cluster Operations:

-GKE Containe:

cluster_name: An immutable name for the cluster the container is running in.

namespace_id: Immutable ID of the cluster namespace the container is running in.

instance_id: Immutable ID of the GCE instance the container is running in.

pod_id: Immutable ID of the pod the container is running in.

container_name: Immutable name of the container.

zone: The GCE zone in which the instance is running.

VS

-GKE Cluster Operations

project_id: The identifier of the GCP project associated with this resource, such as " my-project " .

cluster_name: The name of the GKE Cluster.

location: The location in which the GKE Cluster is running.

This command correctly lists pods that have the label app=prod. When creating the deployment, we used the label app=prod so listing pods that have this label retrieve the pods belonging to nginx deployments. You can list pods by using Kubernetes CLI  kubectl get pods.

Ref: https://kubernetes.io/docs/tasks/access-application-cluster/list-all-running-container-images/

Ref: https://kubernetes.io/docs/tasks/access-application-cluster/list-all-running-container-images/#list-containers-filtering-by-pod-label

Specifying conditions for alerting policies This page describes how to specify conditions for alerting policies. The conditions for an alerting policy define what is monitored and when to trigger an alert. For example, suppose you want to define an alerting policy that emails you if the CPU utilization of a Compute Engine VM instance is above 80% for more than 3 minutes. You use the conditions dialog to specify that you want to monitor the CPU utilization of a Compute Engine VM instance, and that you want an alerting policy to trigger when that utilization is above 80% for 3 minutes.https://cloud.google.com/monitoring/alerts/ui-conditions-ga

https://cloud.google.com/monitoring/alerts/using-alerting-uihttps://cloud.google.com/monitoring/support/notification-options

GKE Autopilot is a mode of operation in GKE where Google manages the underlying infrastructure, including nodes, node pools, and their upgrades. This significantly reduces the management and operational overhead for the user, allowing teams to focus solely on deploying and managing their containerized applications. Since the applications are not exposed publicly, the zonal or regional nature of the cluster primarily impacts availability within Google Cloud, and Autopilot is available for both. Autopilot minimizes the operational burden, which is a key requirement.

Option A: A Standard zonal GKE cluster requires you to manage the nodes yourself, including sizing, scaling, and upgrades, increasing operational overhead compared to Autopilot.

Option B: Manually installing and managing Kubernetes on a fleet of Compute Engine instances involves the highest level of management overhead, which contradicts the requirement to minimize it.

Option D: A Standard regional GKE cluster provides higher availability than a zonal cluster by replicating the control plane and nodes across multiple zones within a region. However, it still requires you to manage the underlying nodes, unlike Autopilot.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The different modes of GKE operation, including Standard and Autopilot, and their respective management responsibilities and benefits, are clearly outlined in the Google Kubernetes Engine documentation, a core topic for the Associate Cloud Engineer certification. The emphasis on reduced operational overhead with Autopilot is a key differentiator.

You can use Google Cloud Deployment Manager to create a set of Google Cloud resources and manage them as a unit, called a deployment. For example, if your team ' s development environment needs two virtual machines (VMs) and a BigQuery database, you can define these resources in a configuration file, and use Deployment Manager to create, change, or delete these resources. You can make the configuration file part of your team ' s code repository, so that anyone can create the same environment with consistent results.https://cloud.google.com/deployment-manager/docs/quickstart

https://cloud.google.com/network-connectivity/docs/vpn/concepts/best-practices

https://cloud.google.com/spanner/docs/iam#spanner.databaseUser

Using the gcloud tool, execute the gcloud iam roles describe roles/spanner.databaseUser command on Cloud Shell. Attach the users to a newly created Google group and add the group to the role.

The correct answer is A because the requirement is company-wide access for employees in one Cloud Identity domain, not public or cross-domain access. The BigQuery Data Viewer role provides read-only access to dataset resources, including reading and querying table data when granted at the dataset level. Using the domain principal domain:terramearth.com grants access to authenticated users in that organization’s domain. allUsers would include unauthenticated public access, and allAuthenticatedUsers would include any authenticated Google account, which is far broader than the company boundary. A service account represents an application identity, not all human employees.

Understanding IAM custom roles

Key Point: Custom roles enable you to enforce the principle of least privilege, ensuring that the user and service accounts in your organization have only the permissions essential to performing their intended functions.

Basic concepts

Custom roles are user-defined, and allow you to bundle one or more supported permissions to meet your specific needs. Custom roles are not maintained by Google; when new permissions, features, or services are added to Google Cloud, your custom roles will not be updated automatically.

When you create a custom role, you must choose an organization or project to create it in. You can then grant the custom role on the organization or project, as well as any resources within that organization or project.

https://cloud.google.com/iam/docs/understanding-custom-roles#basic_concepts