Free and Premium Google Associate-Cloud-Engineer Dumps Questions Answers

The goal is to create a serverless, scalable, and asynchronous transaction processing system with minimal management overhead.

Serverless Requirement:Options involving installing Kafka on VMs (A, B) or using VM instances for processing (B, C) introduce management overhead associated with VMs (patching, scaling configuration, OS management) and Kafka cluster management, violating the serverless and minimal management criteria.

Asynchronous Requirement:Both Kafka and Pub/Sub can handle asynchronous messaging. However, Pub/Sub is Google Cloud's fully managed, serverless messaging service, inherently minimizing management overhead compared to self-managed Kafka on VMs.

Scalability and Processing:Cloud Run is a fully managed, serverless platform that automatically scales based on traffic, suitable for processing transactions without managing underlying infrastructure. VM instances require manual scaling configuration or managed instance groups, adding overhead.

Combining Pub/Sub for asynchronous message ingestion (fully managed, serverless) and Cloud Run for processing (fully managed, serverless, scalable) directly meets all requirements: serverless, scalable, asynchronous, and minimal management overhead. Option D is the only one that uses fully serverless components for both ingestion and processing.

The requirement is to manage SSH access for a team with minimal operational overhead when team members change.

OS Login: OS Login is the Google-recommended method for managing SSH access to Compute Engine instances. It centralizes control by linking Linux user accounts to Google identities and IAM. It eliminates the need to manually manage SSH keys in instance or project metadata.

Google Groups: Managing access through a Google Group minimizes overhead. By granting the necessary OS Login IAM roles (e.g., roles/compute.osLogin or roles/compute.osAdminLogin) to the Google Group at the project level, you only need to add or remove members from the Google Group when an engineer joins or leaves, and the access rights are automatically updated across all instances.

# The object's time spent set at the original storage class counts towards any minimum storage duration that applies for the new storage class.

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.

Let's analyze each option to find the one that meets the requirements of no startup time for new traffic, two idle instances, and minimal administrative overhead:

A. Unchecking "Serve this revision immediately" and using a traffic simulation tool: Unchecking "Serve this revision immediately" does prevent the new revision from receiving traffic immediately. However, manually using a traffic simulation tool adds administrative overhead. It also doesn't guarantee that two idle instances will be ready before traffic is shifted; you would need to monitor and adjust traffic manually based on the simulation.

B. Configuring service autoscaling and setting the minimum number of instances to 2: Service-level autoscaling applies to all revisions of the service. Setting the minimum instances at the service level would ensure at least two instances are running across all active revisions, not specifically for the new revision before traffic shift.

C. Configuring revision autoscaling for the new revision and setting the minimum number of instances to 2: This is the correct approach. By configuring revision autoscaling specifically for the new revision and setting the minimum number of instances to 2, Cloud Run will ensure that at least two instances of the new version are running and ready to serve traffic before you redirect any traffic to it. This eliminates startup latency when you do shift traffic. It also minimizes administrative overhead as Cloud Run manages the instance scaling based on this configuration.

D. Configuring revision autoscaling for the existing revision and setting the minimum number of instances to 2: This would ensure the existing version has at least two idle instances, which doesn't directly address the requirement of having idle instances ready for the new version before traffic redirection.

Google Cloud Documentation References:

Cloud Run Autoscaling: - This document explains how to configure minimum and maximum instances for Cloud Run services and revisions. It clarifies that you can set minimum instances at the revision level to ensure instances are always ready.

Cloud Run Traffic Management: - This describes how to deploy new revisions and gradually shift traffic between them. Combining minimum instances on the new revision with traffic splitting allows for zero-downtime deployments with pre-warmed instances.

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You write a lifecycle management rule in XML and push it to the bucket with gsutil lifecycle set config-xml-file. is not right.

gsutil lifecycle set enables you to set the lifecycle configuration on one or more buckets based on the configuration file provided. However, XML is not a valid supported type for the configuration file.

Write a script that runs gsutil ls -lr gs://myapp-gcp-ace-logs/ to find and remove items older than 90 days. Repeat this process every morning. is not right.

This manual approach is error-prone, time-consuming and expensive. GCP Cloud Storage provides lifecycle management rules that let you achieve this with minimal effort.

Write a script that runs gsutil ls -l gs://myapp-gcp-ace-logs/ to find and remove items older than 90 days. Schedule the script with cron. is not right.

This manual approach is error-prone, time-consuming and expensive. GCP Cloud Storage provides lifecycle management rules that let you achieve this with minimal effort.

Write a lifecycle management rule in JSON and push it to the bucket with gsutil lifecycle set config-json-file. is the right answer.

You can assign a lifecycle management configuration to a bucket. The configuration contains a set of rules which apply to current and future objects in the bucket. When an object meets the criteria of one of the rules, Cloud Storage automatically performs a specified action on the object. One of the supported actions is to Delete objects. You can set up a lifecycle management to delete objects older than 90 days. gsutil lifecycle set enables you to set the lifecycle configuration on the bucket based on the configuration file. JSON is the only supported type for the configuration file. The config-json-file specified on the command line should be a path to a local file containing the lifecycle configuration JSON document.

roles/monitoring.viewer provides read-only access to get and list information about all monitoring data and configurations. This role provides monitoring access and fits our requirements. roles/monitoring.viewer. is the right answer.

You can use the Google Cloud Pricing Calculator to total the estimated monthly costs for each GCP product. You dont incur any charges for doing so.

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.

You can deploy Cassandra as a Service, called Astra, on the Google Cloud Marketplace. Not only do you get a unified bill for all GCP services, your Developers can now create Cassandra clusters on Google Cloud in minutes and build applications with Cassandra as a database as a service without the operational overhead of managing Cassandra

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.

When you intially click on Monitoring(Stackdriver Monitoring) it creates a workspac(a stackdriver account) linked to the ACTIVE(CURRENT) Project from which it was clicked.

Now if you change the project and again click onto Monitoring it would create an another workspace(a stackdriver account) linked to the changed ACTIVE(CURRENT) Project, we don't want this as this would not consolidate our result into a single dashboard(workspace/stackdriver account).

If you have accidently created two diff workspaces merge them under Monitoring > Settings > Merge Workspaces > MERGE.

If we have only one workspace and two projects we can simply add other GCP Project under

Monitoring > Settings > GCP Projects > Add GCP Projects.

Nothing about

Google Cloud Coldline is a new cold-tier storage for archival data with access frequency of less than once per year. Unlike other cold storage options, Nearline has no delays prior to data access, so now it is the leading solution among competitors.

The Real description is about Coldline storage Class:

Coldline Storage

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is a better choice than Standard Storage or Nearline Storage in scenarios where slightly lower availability, a 90-day minimum storage duration, and higher costs for data access are acceptable trade-offs for lowered at-rest storage costs.

Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Note, however, that for data being kept entirely for backup or archiving purposes, Archive Storage is more cost-effective, as it offers the lowest storage costs.

This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost.

Example:

apiVersion: v1

kind: Pod

metadata:

name:my-gpu-pod

spec:

containers:

name:my-gpu-container

image: nvidia/cuda:10.0-runtime-ubuntu18.04

command: [/bin/bash]

resources:

limits:

nvidia.com/gpu: 2

nodeSelector:

cloud.google.com/gke-accelerator: nvidia-tesla-k80# or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia-tesla-v100 or nvidia-tesla-t4

For running an automated, scheduled, short-lived, and repeatable compute task (a batch workload) while minimizing cost and having automatic retries:

Batch Workload/Retries: A Cloud Run Job is specifically designed for running containers that execute a task and then exit. It natively supports automatic retries on failure, which is a key requirement.

Scheduling: Cloud Scheduler is the native, fully managed service for defining and triggering scheduled tasks, which satisfies the "overnight every day" requirement.

Cost Minimization: Cloud Run Jobs are serverless and only consume resources (and thus incur costs) while the batch task is actively running. This is significantly cheaper and lower overhead than running a persistent GKE cluster (Option A and B) or a continuous Cloud Run Service (Option D), which is designed for continuous request handling, not single batch execution.

Autopilot is more reliable and stable release gives more time to fix issues in new version of GKE

IAP controls access to your App Engine apps and Compute Engine VMs running on Google Cloud. It leverages user identity and the context of a request to determine if a user should be allowed access. IAP is a building block toward BeyondCorp, an enterprise security model that enables employees to work from untrusted networks without using a VPN.

By default, IAP uses Google identities and IAM. By leveraging Identity Platform instead, you can authenticate users with a wide range of external identity providers, such as:

Email/password

OAuth (Google, Facebook, Twitter, GitHub, Microsoft, etc.)

SAML

OIDC

Phone number

Custom

Anonymous

This is useful if your application is already using an external authentication system, and migrating your users to Google accounts is impractical.

Create an instance template for the instances so VMs have same specs. Set the "˜Automatic Restart' to on to VM automatically restarts upon crash. Set the "˜On-host maintenance' to Migrate VM instance. This will take care of VM during maintenance window. It will migrate VM instance making it highly available Add the instance template to an instance group so instances can be managed.

• onHostMaintenance: Determines the behavior when a maintenance event occurs that might cause your instance to reboot.

• [Default] MIGRATE, which causes Compute Engine to live migrate an instance when there is a maintenance event.

• TERMINATE, which stops an instance instead of migrating it.

• automaticRestart: Determines the behavior when an instance crashes or is stopped by the system.

• [Default] true, so Compute Engine restarts an instance if the instance crashes or is stopped.

• false, so Compute Engine does not restart an instance if the instance crashes or is stopped.

Enabling automatic restart ensures that compute engine instances are automatically restarted when they crash. And Enabling Migrate VM Instance enables live migrates i.e. compute instances are migrated during system maintenance and remain running during the migration.

Automatic Restart  If your instance is set to terminate when there is a maintenance event, or if your instance crashes because of an underlying hardware issue, you can set up Compute Engine to automatically restart the instance by setting the automaticRestart field to true. This setting does not apply if the instance is taken offline through a user action, such as calling sudo shutdown, or during a zone outage.

Enabling the Migrate VM Instance option migrates your instance away from an infrastructure maintenance event, and your instance remains running during the migration.Your instance might experience a short period of decreased performance, although generally, most instances should not notice any difference. This is ideal for instances that require constant uptime and can tolerate a short period of decreased performance.

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: - 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: - 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: - This emphasizes BigQuery 's analytical capabilities rather than low-latency operational workloads.

Cloud Storage 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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"The Cloud Spanner to Cloud Storage Text template is a batch pipeline that reads in data from a Cloud Spanner table, optionally transforms the data via a JavaScript User Defined Function (UDF) that you provide, and writes it to Cloud Storage as CSV text files."

"The Dataflow connector for Cloud Spanner lets you read data from and write data to Cloud Spanner in a Dataflow pipeline"

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

Comprehensive and Detailed In Depth Explanation:

The goal is to proactively identify stuck Dataflow jobs with minimal management effort. Let's analyze each option:

A. Error Reporting for slowdowns: Error Reporting primarily focuses on capturing and aggregating exceptions and errors (stack traces). While a stuck job might eventually throw an error, it might also just become unresponsive without generating explicit errors. Relying solely on Error Reporting might not provide timely detection of stuck jobs. Identifying stack traces that indicate slowdowns can also be complex and require significant manual configuration and analysis.

B. Personalized Service Health dashboard: The Personalized Service Health dashboard provides information about Google Cloud service incidents that might be affecting your resources. While it can alert you to broader Dataflow service outages, it won't specifically identify individual stuck jobs due to application-level errors or logic within your Dataflow pipeline.

C. Pub/Sub messages for delays, backup job, and Cloud Tasks alerts: This approach involves significant custom implementation and management. You would need to instrument your Dataflow jobs to detect delays, send messages to Pub/Sub, manage a backup job, and configure Cloud Tasks for alerting. This adds considerable operational overhead and complexity.

D. Cloud Monitoring alerts on data freshness metric: Dataflow provides built-in metrics, including "data freshness" (or similar metrics like "system lag" or "processing time"), which indicate how far behind the pipeline is in processing data. If a job gets stuck, the data freshness will deteriorate beyond an acceptable threshold. Cloud Monitoring allows you to easily set up alerts based on these built-in metrics. This requires minimal custom coding and leverages the platform's existing monitoring capabilities, aligning with the "minimal management effort" requirement.

Therefore, setting up Cloud Monitoring alerts on relevant Dataflow metrics like data freshness is the most efficient and recommended way to detect stuck Dataflow jobs with minimal management overhead.

Google Cloud Documentation References:

Monitoring Dataflow Pipelines: - This document details the various metrics available for monitoring Dataflow jobs in Cloud Monitoring, including metrics related to processing time, system lag, and data freshness.

Creating Alerts in Cloud Monitoring: - Explains how to set up alerts based on metrics collected by Cloud Monitoring.

Dataflow Metrics: - Provides a comprehensive list of Dataflow metrics that can be used for monitoring and alerting.

1. In Stackdriver Logging, create a filter to view only Compute Engine logs. 2. Click Create Export. 3. Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.

Keywords: - continually -> Standard - mission-critical analytics -> dual-regional

This command correctly specifies the duration that the signed url should be valid for by using the -d flag. The default is 1 hour so omitting the -d flag would have also resulted in the same outcome. Times may be specified with no suffix (default hours), or with s = seconds, m = minutes, h = hours, d = days. The max duration allowed is 7d.

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: - This document highlights Spanner 's global scalability, strong consistency, and multi-region capabilities.

Cloud Bigtable Overview: - While scalable and low-latency, it 's a NoSQL database, which might require significant application changes.

BigQuery Overview: - Focuses on analytics, not low-latency transactional workloads.

Cloud SQL for PostgreSQL Overview: - While it offers scaling and regional HA, its multi-region write capabilities are not as robust as Spanner 's.

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As to mexblood1's point, CPU utilization is a recommended proxy for traffic when it comes to Cloud Spanner. See: Alerts for high CPU utilization The following table specifies our recommendations for maximum CPU usage for both single-region and multi-region instances. These numbers are to ensure that your instance has enough compute capacity to continue to serve your traffic in the event of the loss of an entire zone (for single-region instances) or an entire region (for multi-region instances).

To enforce a mandatory restriction across all resources deployed in the organization and its nested folders/projects, the policy should be set at the highest possible level, which is the Organization level.

The constraint constraints/gcp.resourceLocations (or similar resource location constraints) works by defining a list of allowed regions or zones. The most secure and explicit way to enforce that resources must be deployed in the EEA is to define the allowed EEA locations and apply the policy at the Organization level to ensure it covers all current and future projects and folders.

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.

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.

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

Creating or upgrading a cluster by specifying the version as latest does not provide automatic upgrades. Enable node auto-upgrades to ensure that the nodes in your cluster are up-to-date with the latest stable version.

Node auto-upgrades help you keep the nodes in your cluster up to date with the cluster master version when your master is updated on your behalf. When you create a new cluster or node pool with Google Cloud Console or the gcloud command, node auto-upgrade is enabled by default.

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.

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.

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.

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.

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.

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.

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.

Cloud VPN is another alternative. Because Cloud VPN establishes reachability through managed IPsec tunnels, it doesn 't have the aggregate limits of VPC Network Peering. Cloud VPN uses a VPN Gateway for connectivity and doesn't consider the aggregate resource use of the IPsec peer. The drawbacks of Cloud VPN include increased costs (VPN tunnels and traffic egress), management overhead required to maintain tunnels, and the performance overhead of IPsec.

Creating scheduled snapshots for persistent disk This document describes how to create a snapshot schedule to regularly and automatically back up your zonal and regional persistent disks. Use snapshot schedules as a best practice to back up your Compute Engine workloads. After creating a snapshot schedule, you can apply it to one or more persistent

Because if you directly add users to the IAM roles, then if any users left the organization then you have to remove the users from multiple places and need to revoke his/her access from multiple places. But, if you put a user into a group then its very easy to manage these type of situations. Now, if any user left then you just need to remove the user from the group and all the access got revoked

The organization creates a Google group for these external auditors and adds the current auditor to the group. This group is monitored and is typically granted access to the dashboard application. During normal access, the auditors' Google group is only granted access to view the historic logs stored in BigQuery. If any anomalies are discovered, the group is granted permission to view the actual Cloud Logging Admin Activity logs via the dashboard's elevated access mode. At the end of each audit period, the group's access is then revoked. Data is redacted using Cloud DLP before being made accessible for viewing via the dashboard application. The table below explains IAM logging roles that an Organization Administrator can grant to the service account used by the dashboard, as well as the resource level at which the role is granted.

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

OS Login is a Google-recommended practice for managing access to Linux VMs in Compute Engine. It centralizes user account management by linking the Linux user accounts on the VMs to Google Cloud identities. You then use IAM roles to grant users the necessary permissions to access the VMs (e.g., roles/compute.osLogin or roles/compute.osAdminLogin). This simplifies management as you control access through IAM policies rather than managing individual SSH keys on each VM, thus minimizing operational costs.

Option B: While enabling OS Login is a good first step, writing custom startup scripts to manage user permissions adds complexity and operational overhead, contradicting the goal of simplification and minimizing costs.

Option C: Requiring developers to manage their own SSH keys and making the owner root is a significant security risk and not a recommended practice. It also doesn't centralize management.

Option D: This approach also involves managing individual SSH keys and custom scripts, which increases operational overhead and doesn't leverage the centralized management benefits of OS Login.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

OS Login and its benefits for simplified and secure Linux VM access management are detailed in the Compute Engine documentation, which is a key area for the Associate Cloud Engineer certification. The integration with IAM for permission control is a central aspect of this service.

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.

"large quantity" : Cloud Storage or BigQuery "files" a file is nothing but an Object

Shared VPC allows an organization to connect resources from multiple projects to a common Virtual Private Cloud (VPC) network, so that they can communicate with each other securely and efficiently using internal IPs from that network. When you use Shared VPC, you designate a project as a host project and attach one or more other service projects to it. The VPC networks in the host project are called Shared VPC networks. Eligible resources from service projects can use subnets in the Shared VPC network

"For example, an existing instance in a service project cannot be reconfigured to use a Shared VPC network, but a new instance can be created to use available subnets in a Shared VPC network."

1. Create an ingress firewall rule with the following settings: "¢ Targets: all instances with tier #2 service account "¢ Source filter: all instances with tier #1 service account "¢ Protocols: allow TCP:8080 2. Create an ingress firewall rule with the following settings: "¢ Targets: all instances with tier #3 service account "¢ Source filter: all instances with tier #2 service account "¢ Protocols: allow TCP: 8080

Set budgets and budget alerts Overview Avoid surprises on your bill by creating Cloud Billing budgets to monitor all of your Google Cloud charges in one place. A budget enables you to track your actual Google Cloud spend against your planned spend. After you've set a budget amount, you set budget alert threshold rules that are used to trigger email notifications. Budget alert emails help you stay informed about how your spend is tracking against your budget. 2. Set budget scope Set the budget Scope and then click Next. In the Projects field, select one or more projects that you want to apply the budget alert to. To apply the budget alert to all the projects in the Cloud Billing account, choose Select

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."

To minimize latency for global users and support growth across multiple geographical regions, you must deploy your resources in those regions and use a global load balancing service.

Action B (Multi-Region Deployment): Deploying VMs in multiple regions closest to your users is necessary to reduce latency (by shortening the physical distance data must travel) and support regional growth.

Action C (Global External Application Load Balancer): The external Application Load Balancer (Global) is the Google-recommended Layer 7 solution for applications that require global reach. It provides a single anycast IP address and directs users to the nearest healthy backend, thus minimizing latency globally.

A managed instance group (MIG) is a group of identical virtual machines (VMs) that you can manage as a single entity. You can use a MIG to deploy and maintain a stateless application that runs directly on VMs. A MIG can automatically scale the number of VMs based on the load or a schedule. A MIG can also automatically heal the VMs if they become unhealthy or unavailable. A MIG is suitable for applications that need to run on VMs rather than containers or serverless platforms.

B is incorrect because Kubernetes Engine is a managed service for running containerized applications on a cluster of nodes. It is not necessary to use Kubernetes Engine if the application does not use containers and can run directly on VMs.

C is incorrect because Cloud Functions is a serverless platform for running event-driven code in response to triggers. It is not suitable for applications that need to run continuously and handle HTTP requests.

D is incorrect because Cloud Run is a serverless platform for running stateless containerized applications. It is not suitable for applications that do not use containers and can run directly on VMs.

Managed instance groups documentation

Choosing a compute option for Google Cloud

Storage Solution: Cloud Storage is the Google-managed, highly durable, and scalable object storage solution ideal for storing unstructured documents like invoices.

Tiering/Cost Optimization:

For frequent access (past 6 months), the initial storage class should be Standard (lowest latency, higher storage cost).

For archived/audit purposes only (after 6 months), the access frequency is extremely low. Coldline Storage is the lowest-cost storage class designed for data accessed infrequently (once a quarter or less), minimizing overall cost.

Automation: Object Lifecycle Management is the native Cloud Storage feature used to automatically transition objects between storage classes (e.g., Standard $\to$ Coldline) based on time (e.g., after 6 months), fulfilling the requirements for automation and cost minimization.

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.

You create the service account in proj-sa and take note of the service account email, then you go to proj-vm in IAM > ADD and add the service account's email as new member and give it the Compute Storage Admin role.

Explanation

Run gcloud builds submit tag gcr.io/img-278322/acme_track_n_trace:v1. is the right answer.

This command correctly tags the image as acme_track_n_trace:v1 and uploads the image to the img-278322 project.

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.

"Google Cloud VPC Network Peering allows internal IP address connectivity across two Virtual Private Cloud (VPC) networks regardless of whether they belong to the same project or the same organization."

while

"Cloud Interconnect provides low latency, high availability connections that enable you to reliably transfer data between your on-premises and Google Cloud Virtual Private Cloud (VPC) networks."

and

"HA VPN is a high-availability (HA) Cloud VPN solution that lets you securely connect your on-premises network to your VPC network through an IPsec VPN connection in a single region."

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.

gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetwork gcloud compute networks subnets expand-ip-range NAME --prefix-length=PREFIX_LENGTH [--region=REGION] [GCLOUD_WIDE_FLAG …]

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 costs, 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

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.

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

Pub/Sub is a scalable and reliable messaging service that can handle large volumes of data from different sources at different rates. It allows you to decouple the producers and consumers of the data, and provides a durable and persistent storage for the messages until they are delivered. Cloud Functions is a serverless platform that can execute code in response to events, such as messages published to a Pub/Sub topic. It can scale automatically based on the load, and you only pay for the resources you use. By using Pub/Sub and Cloud Functions, you can optimize the storage and processing of the votes, as you can handle the variable rates of incoming votes, process them in real time or near real time, and avoid managing servers or VMs. References:

Pub/Sub documentation

Cloud Functions documentation

Choosing a messaging service for Google Cloud

As part of your workload, there might be certain VM instances that are critical to running your application or services, such as an instance running a SQL server, a server used as a license manager, and so on. These VM instances might need to stay running indefinitely so you need a way to protect these VMs from being deleted. By setting the deletionProtection flag, a VM instance can be protected from accidental deletion. If a user attempts to delete a VM instance for which you have set the deletionProtection flag, the request fails. Only a user that has been granted a role with compute.instances.create permission can reset the flag to allow the resource to be deleted.

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

What is Google Cloud Memorystore?

Overview. Cloud Memorystore for Redis is a fully managed Redis service for Google Cloud Platform. Applications running on Google Cloud Platform can achieve extreme performance by leveraging the highly scalable, highly available, and secure Redis service without the burden of managing complex Redis deployments.

Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app

"Billing Account Viewer access would usually be granted to finance teams, it provides access to spend information, but does not confer the right to link or unlink projects or otherwise manage the properties of the billing account."

C:\GCP\appeng>gcloud config list

[core]

account = xxx@gmail.com

disable_usage_reporting = False

project = my-first-demo-xxxx

"Run gcloud configurations list to start the Compute Engine instances". How the heck are you expecting to "start" GCE instances doing "configuration list".

Each gcloud configuration has a 1 to 1 relationship with the region (if a region is defined). Since we have two different regions, we would need to create two separate configurations using gcloud config configurations create

Secondly, you can activate each configuration independently by running gcloud config configurations activate [NAME]

Finally, while each configuration is active, you can run the gcloud compute instances start [NAME] command to start the instance in the configurations region.

they require cloud storage for archival and the want to automate the process to upload new medical image to cloud storage, hence we go for gsutil to copy on-prem images to cloud storage and automate the process via cron job. whereas Pub/Sub listens to the changes in the Cloud Storage bucket and triggers the pub/sub topic, which is not required.

The Vertical Pod Autoscaler (VPA) in Kubernetes automatically adjusts the CPU and memory requests and limits of the containers within a pod based on historical and real-time resource usage. In this scenario, where a single-replica stateful application needs more CPU during peak times, VPA can dynamically increase the CPU allocated to the pod when needed and potentially decrease it during off-peak periods to optimize resource utilization and cost efficiency.

Option A: Cluster autoscaling adds or removes nodes in your GKE cluster based on the resource requests of your pods. While it can help with overall cluster capacity, it oesn't directly address the need for more CPU for a specific pod.

Option C: Horizontal Pod Autoscaler (HPA) scales the number of pod replicas based on observed CPU utilization or other select metrics. Since the application can only have one replica, HPA is not suitable.

Option D: Node auto-provisioning is similar to cluster autoscaling, automatically creating and deleting node pools based on workload demands. It doesn't directly manage the resources of individual pods.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The functionality and use cases of the Vertical Pod Autoscaler (VPA) are detailed in the Google Kubernetes Engine documentation, specifically within the resource management and autoscaling sections. Understanding how VPA can dynamically adjust pod resources is relevant to the Associate Cloud Engineer certification.