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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ensuring Successful Operation	15-20%	- Managing Cloud Run resources 1. Configuring resource limits 2. Managing revisions and traffic splitting - Managing storage and data solutions 1. Configuring CORS and encryption 2. Managing Cloud Storage buckets and objects 3. Setting lifecycle policies - Managing Compute Engine resources 1. Attaching and detaching disks 2. Resizing instances and persistent disks 3. Managing instances, disks, and snapshots - Managing logging and monitoring 1. Using Cloud Monitoring and Logging 2. Viewing and analyzing logs 3. Creating metrics and alerts - Managing Kubernetes Engine resources 1. Scaling clusters and node pools 2. Upgrading clusters 3. Managing workloads (Deployments, Services, HPA)
Topic 2: Deploying and Implementing a Cloud Solution	20-25%	- Deploying and managing Kubernetes Engine resources 1. Configuring kubectl for cluster access 2. Deploying workloads with Deployments and Services 3. Creating and managing GKE clusters - Deploying applications with Cloud Deployment Manager 1. Creating deployment configurations 2. Using templates for resource provisioning - Deploying and managing Compute Engine resources 1. Creating and managing managed instance groups 2. Creating and managing custom machine types 3. Creating and managing instances with startup scripts - Deploying and managing Cloud Run resources 1. Deploying containerized applications to Cloud Run 2. Configuring memory limits and scaling settings - Deploying and managing Cloud Functions 1. Creating and triggering Cloud Functions 2. Configuring triggers and environment variables
Topic 3: Planning and Configuring a Cloud Solution	20-25%	- Planning and configuring compute resources 1. Configuring persistent disks and local SSDs 2. Selecting appropriate machine types and zones - Planning and configuring network resources 1. Designing VPC networks 2. Configuring load balancing and firewall rules - Planning and configuring data storage options 1. Selecting appropriate storage classes 2. Choosing between Cloud Storage, Filestore, Persistent Disk - Planning and estimating GCP product usage 1. Using GCP Pricing Calculator 2. Reading documentation for compute, storage, and network resources
Topic 4: Setting Up a Cloud Solution Environment	20-25%	- Setting up cloud projects and accounts 1. Creating and managing projects 2. Managing billing budgets and alerts 3. Assigning IAM roles to users and groups - Managing billing configuration 1. Setting up budget alerts and notifications 2. Linking billing to projects 3. Creating billing accounts
Topic 5: Configuring Access and Security	15-20%	- Managing IAM resources 1. Applying IAM policies to resources 2. Defining custom IAM roles 3. Creating and managing IAM roles - Managing service accounts 1. Assigning service accounts to resources 2. Creating and managing service accounts 3. Using service account keys - Viewing audit logs 1. Configuring audit log policies 2. Accessing and analyzing Cloud Audit Logs

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Google Associate Cloud Engineer Exam Sample Questions (Q80-Q85):

NEW QUESTION # 80
You are running an application on multiple virtual machines within a managed instance group and have autoscaling enabled. The autoscaling policy is configured so that additional instances are added to the group if the CPU utilization of instances goes above 80%. VMs are added until the instance group reaches its maximum limit of five VMs or until CPU utilization of instances lowers to 80%. The initial delay for HTTP health checks against the instances is set to 30 seconds. The virtual machine instances take around three minutes to become available for users. You observe that when the instance group autoscales, it adds more instances then necessary to support the levels of end-user traffic. You want to properly maintain instance group sizes when autoscaling. What should you do?

A. Increase the initial delay of the HTTP health check to 200 seconds.
B. Set the maximum number of instances to 1.
C. Decrease the maximum number of instances to 3.
D. Use a TCP health check instead of an HTTP health check.

Answer: A

Explanation:
Explanation
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

NEW QUESTION # 81
You are building a product on top of Google Kubernetes Engine (GKE). You have a single GKE cluster. For each of your customers, a Pod is running in that cluster, and your customers can run arbitrary code inside their Pod. You want to maximize the isolation between your customers' Pods. What should you do?

A. Use the Container Analysis API to detect vulnerabilities in the containers used by your customers' Pods.
B. Use the cos_containerd image for your GKE nodes. Add a nodeSelector with the value cloud.google.com/gke-os-distribution: cos_containerd to the specification of your customers' Pods.
C. Create a GKE node pool with a sandbox type configured to gvisor. Add the parameter runtimeClassName: gvisor to the specification of your customers' Pods.
D. Use Binary Authorization and whitelist only the container images used by your customers' Pods.

Answer: C

NEW QUESTION # 82
Your team is building a website that handles votes from a large user population. The incoming votes will arrive at various rates. You want to optimize the storage and processing of the votes. What should you do?

A. Save the incoming votes to Firestore. Use Cloud Scheduler to trigger a Cloud Functions instance to periodically process the votes.
B. Save the incoming votes to Pub/Sub. Use the Pub/Sub topic to trigger a Cloud Functions instance to process the votes.
C. Save the incoming votes to a JSON file on Cloud Storage. Process the votes in a batch at the end of the day.
D. Use a dedicated instance to process the incoming votes. Send the votes directly to this instance.

Answer: B

Explanation:
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

NEW QUESTION # 83
You have a Compute Engine instance hosting a production application. You want to receive an email if the instance consumes more than 90% of its CPU resources for more than 15 minutes. You want to use Google services. What should you do?

A. 1. Create a Stackdriver Workspace, and associate your GCP project with it.
2.Write a script that monitors the CPU usage and sends it as a custom metric to Stackdriver.
3.Create an uptime check for the instance in Stackdriver.
B. 1. In Stackdriver Logging, create a logs-based metric to extract the CPU usage by using this regular expression: CPU Usage: ([0-9] {1,3}) %
2.In Stackdriver Monitoring, create an Alerting Policy based on this metric.
3.Configure your email address in the notification channel.
C. 1. Create a consumer Gmail account.
2.Write a script that monitors the CPU usage.
3.When the CPU usage exceeds the threshold, have that script send an email using the Gmail account and smtp.gmail.com on port 25 as SMTP server.
D. 1. Create a Stackdriver Workspace, and associate your Google Cloud Platform (GCP) project with it.
2.Create an Alerting Policy in Stackdriver that uses the threshold as a trigger condition.
3.Configure your email address in the notification channel.

Answer: D

Explanation:
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-ui https://cloud.google.com/monitoring/support
/notification-options

NEW QUESTION # 84
You are configuring service accounts for an application that spans multiple projects. Virtual machines (VMs) running in the web-applications project need access to BigQuery datasets in crm-databases-proj. You want to follow Google-recommended practices to give access to the service account in the web-applications project. What should you do?

A. Give "project owner" role to crm-databases-proj and the web-applications project.
B. Give "project owner" for web-applications appropriate roles to crm-databases- proj
C. Give "project owner" role to crm-databases-proj and bigquery.dataViewer role to web-applications.
D. Give bigquery.dataViewer role to crm-databases-proj and appropriate roles to web-applications.

Answer: D

Explanation:
Reference:
bigquery.dataViewer role provides permissions to read the datasets metadata and list tables in the dataset as well as Read data and metadata from the datasets tables. This is exactly what we need to fulfil this requirement and follows the least privilege principle.
Ref: https://cloud.google.com/iam/docs/understanding-roles#bigquery-roles

NEW QUESTION # 85
......

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