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Google Associate-Cloud-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Deploying and Implementing a Cloud Solution20-25%- Deploying and managing Cloud Run resources
  • 1. Deploying containerized applications to Cloud Run
  • 2. Configuring memory limits and scaling settings
- Deploying and managing Compute Engine resources
  • 1. Creating and managing managed instance groups
  • 2. Creating and managing instances with startup scripts
  • 3. Creating and managing custom machine types
- Deploying and managing Cloud Functions
  • 1. Creating and triggering Cloud Functions
  • 2. Configuring triggers and environment variables
- Deploying applications with Cloud Deployment Manager
  • 1. Using templates for resource provisioning
  • 2. Creating deployment configurations
- Deploying and managing Kubernetes Engine resources
  • 1. Creating and managing GKE clusters
  • 2. Configuring kubectl for cluster access
  • 3. Deploying workloads with Deployments and Services
Configuring Access and Security15-20%- Managing service accounts
  • 1. Creating and managing service accounts
  • 2. Assigning service accounts to resources
  • 3. Using service account keys
- Managing IAM resources
  • 1. Applying IAM policies to resources
  • 2. Creating and managing IAM roles
  • 3. Defining custom IAM roles
- Viewing audit logs
  • 1. Accessing and analyzing Cloud Audit Logs
  • 2. Configuring audit log policies
Ensuring Successful Operation15-20%- Managing Cloud Run resources
  • 1. Managing revisions and traffic splitting
  • 2. Configuring resource limits
- Managing storage and data solutions
  • 1. Setting lifecycle policies
  • 2. Managing Cloud Storage buckets and objects
  • 3. Configuring CORS and encryption
- Managing Kubernetes Engine resources
  • 1. Managing workloads (Deployments, Services, HPA)
  • 2. Upgrading clusters
  • 3. Scaling clusters and node pools
- Managing logging and monitoring
  • 1. Viewing and analyzing logs
  • 2. Creating metrics and alerts
  • 3. Using Cloud Monitoring and Logging
- Managing Compute Engine resources
  • 1. Attaching and detaching disks
  • 2. Resizing instances and persistent disks
  • 3. Managing instances, disks, and snapshots
Planning and Configuring a Cloud Solution20-25%- Planning and estimating GCP product usage
  • 1. Reading documentation for compute, storage, and network resources
  • 2. Using GCP Pricing Calculator
- Planning and configuring data storage options
  • 1. Choosing between Cloud Storage, Filestore, Persistent Disk
  • 2. Selecting appropriate storage classes
- 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
Setting Up a Cloud Solution Environment20-25%- Managing billing configuration
  • 1. Creating billing accounts
  • 2. Setting up budget alerts and notifications
  • 3. Linking billing to projects
- Setting up cloud projects and accounts
  • 1. Assigning IAM roles to users and groups
  • 2. Creating and managing projects
  • 3. Managing billing budgets and alerts

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Google Associate Cloud Engineer Exam Sample Questions (Q367-Q372):

NEW QUESTION # 367
You are planning to migrate your containerized workloads to Google Kubernetes Engine (GKE). You need to determine which GKE option to use. Your solution must have high availability, minimal downtime, and the ability to promptly apply security updates to your nodes. You also want to pay only for the compute resources that your workloads use without managing nodes. You want to follow Google-recommended practices and minimize operational costs. What should you do?

Answer: D


NEW QUESTION # 368
You are deploying an application to App Engine. You want the number of instances to scale based on request rate. You need at least 3 unoccupied instances at all times. Which scaling type should you use?

Answer: B

Explanation:
Reference: https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/go/config/appref
"App Engine calculates the number of instances necessary to serve your current application traffic based on scaling settings such as target_cpu_utilization and target_throughput_utilization. Setting min_idle_instances specifies the number of instances to run in addition to this calculated number. For example, if App Engine calculates that 5 instances are necessary to serve traffic, and min_idle_instances is set to 2, App Engine will run 7 instances (5, calculated based on traffic, plus 2 additional per min_idle_instances)." Automatic scaling creates dynamic instances based on request rate, response latencies, and other application metrics. However, if you specify the number of minimum idle instances, that specified number of instances run as resident instances while any additional instances are dynamic.
Ref: https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed


NEW QUESTION # 369
You deployed an App Engine application using gcloud app deploy, but it did not deploy to the intended project. You want to find out why this happened and where the application deployed. What should you do?

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/endpoints/docs/openapi/troubleshoot-aeflex-deployment


NEW QUESTION # 370
A team of data scientists infrequently needs to use a Google Kubernetes Engine (GKE) cluster that you manage. They require GPUs for some long-running, non-restartable jobs. You want to minimize cost. What should you do?

Answer: B


NEW QUESTION # 371
You've created the code for a Cloud Function that will respond to HTTP triggers and return some data in JSON format. You have the code locally, it's tested and working. Which command can you use to create the function inside Google Cloud?

Answer: A,C


NEW QUESTION # 372
......

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