Associate-Cloud-Engineer인증덤프샘플다운 - Associate-Cloud-Engineer유효한최신버전덤프

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많은 분들이 고난의도인 Google관련인증시험을 응시하고 싶어 하는데 이런 시험은 많은 전문적인 관련지식이 필요합니다. 시험은 당연히 완전히 전문적인 Associate-Cloud-Engineer관련지식을 터득하자만이 패스할 가능성이 높습니다. 하지만 지금은 많은 방법들로 여러분의 부족한 면을 보충해드릴 수 있으며 또 힘든 Google시험도 패스하실 수 있습니다. 혹은 여러분은 전문적인 Google Associate Cloud Engineer Exam관련지식을 터득하자들보다 더 간단히 더 빨리 시험을 패스하실 수 있습니다.

Google Associate-Cloud-Engineer Exam Syllabus Topics:

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

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최신 Google Cloud Certified Associate-Cloud-Engineer 무료샘플문제 (Q358-Q363):

질문 # 358
You need to manage multiple Google Cloud projects in the fewest steps possible. You want to configure the Google Cloud SDK command line interface (CLI) so that you can easily manage multiple projects. What should you do?

A. 1. Use the default configuration for one project you need to manage.
2. Use gcloud initto update the configuration values when you need to work with a non-default project.
B. 1. Create a configuration for each project you need to manage.
2. Activate the appropriate configuration when you work with each of your assigned Google Cloud projects.
C. 1. Create a configuration for each project you need to manage.
2. Use gcloud initto update the configuration values when you need to work with a non-default project
D. 1. Use the default configuration for one project you need to manage.
2. Activate the appropriate configuration when you work with each of your assigned Google Cloud projects.

정답：A

질문 # 359
You are creating an application that will run on Google Kubernetes Engine. You have identified MongoDB as the most suitable database system for your application and want to deploy a managed MongoDB environment that provides a support SLA. What should you do?

A. Create a Cloud Bigtable cluster and use the HBase API
B. Download a MongoDB installation package and run it on Compute Engine instances
C. Deploy MongoDB Alias from the Google Cloud Marketplace
D. Download a MongoDB installation package, and run it on a Managed Instance Group

정답：C

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

질문 # 360
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?

A. Basic Scaling with min_instances set to 3.
B. Basic Scaling with max_instances set to 3.
C. Manual Scaling with 3 instances.
D. Automatic Scaling with min_idle_instances set to 3.

정답：D

설명：
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).
https://cloud.google.com/appengine/docs/standard/go/config/appref

질문 # 361
You are managing several Google Cloud Platform (GCP) projects and need access to all logs for the past 60 days. You want to be able to explore and quickly analyze the log contents. You want to follow Google- recommended practices to obtain the combined logs for all projects. What should you do?

A. Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days.
B. Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days.
C. Navigate to Stackdriver Logging and select resource.labels.project_id="*"
D. Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.

정답：D

설명：
* 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

질문 # 362
You've uploaded some PDFs to a public bucket. When users browse to the documents, they're downloaded rather than viewed in the browser. How can we ensure that the PDFs are viewed in the browser?

A. Set the Content-Type metadata for the object to "application/pdf".
B. Set the Content metadata for the object to "application/pdf".
C. This is a browser setting and not something that can be changed.
D. Use the gsutil set file-type pdfcommand.

정답：A

질문 # 363
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