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Exam Number : Google-ACE
Exam Name : Google Associate Cloud Engineer - 2023
Vendor Name : Google
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Google-ACE exam Format | Google-ACE Course Contents | Google-ACE Course Outline | Google-ACE exam Syllabus | Google-ACE exam Objectives


Length: Two hours

Languages: English, Japanese, Spanish, Indonesian.

Exam format: Multiple choice and multiple select, taken in person at a test center. Locate a Test Center near you.

Prerequisites: None

Recommended experience: 6 months+ hands-on experience with Google Cloud



An Associate Cloud Engineer deploys applications, monitors operations, and manages enterprise solutions. This individual is able to use Google Cloud Console and the command-line interface to perform common platform-based tasks to maintain one or more deployed solutions that leverage Google-managed or self-managed services on Google Cloud.



The Associate Cloud Engineer exam assesses your ability to:



Set up a cloud solution environment

Plan and configure a cloud solution

Deploy and implement a cloud solution

Ensure successful operation of a cloud solution

Configure access and security



Setting up a cloud solution environment

Setting up cloud projects and accounts. Activities include:



Creating projects

Assigning users to predefined IAM roles within a project

Managing users in Cloud Identity (manually and automated)

Enabling APIs within projects

Provisioning one or more Stackdriver workspaces



Managing billing configuration. Activities include:



Creating one or more billing accounts

Linking projects to a billing account

Establishing billing budgets and alerts

Setting up billing exports to estimate daily/monthly charges



Installing and configuring the command line interface (CLI), specifically the Cloud SDK (e.g., setting the default project).



Planning and configuring a cloud solution

Planning and estimating GCP product use using the Pricing Calculator

Planning and configuring compute resources. Considerations include:



Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Google Kubernetes Engine, App Engine, Cloud Run, Cloud Functions)

Using preemptible VMs and custom machine types as appropriate



Planning and configuring data storage options. Considerations include:



Product choice (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Bigtable)

Choosing storage options (e.g., Standard, Nearline, Coldline, Archive)



Planning and configuring network resources. Tasks include:



Differentiating load balancing options

Identifying resource locations in a network for availability

Configuring Cloud DNS

Deploying and implementing a cloud solution

Deploying and implementing Compute Engine resources. Tasks include:



Launching a compute instance using Cloud Console and Cloud SDK (gcloud) (e.g., assign disks, availability policy, SSH keys)

Creating an autoscaled managed instance group using an instance template

Generating/uploading a custom SSH key for instances

Configuring a VM for Stackdriver monitoring and logging

Assessing compute quotas and requesting increases

Installing the Stackdriver Agent for monitoring and logging



Deploying and implementing Google Kubernetes Engine resources. Tasks include:



Deploying a Google Kubernetes Engine cluster

Deploying a container application to Google Kubernetes Engine using pods

Configuring Google Kubernetes Engine application monitoring and logging

Deploying and implementing App Engine, Cloud Run, and Cloud Functions resources. Tasks include, where applicable:



Deploying an application, updating scaling configuration, versions, and traffic splitting

Deploying an application that receives Google Cloud events (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)



Deploying and implementing data solutions. Tasks include:



Initializing data systems with products (e.g., Cloud SQL, Cloud Datastore, BigQuery, Cloud Spanner, Cloud Pub/Sub, Cloud Bigtable, Cloud Dataproc, Cloud Dataflow, Cloud Storage)

Loading data (e.g., command line upload, API transfer, import/export, load data from Cloud Storage, streaming data to Cloud Pub/Sub)



Deploying and implementing networking resources. Tasks include:



Creating a VPC with subnets (e.g., custom-mode VPC, shared VPC)

Launching a Compute Engine instance with custom network configuration (e.g., internal-only IP address, Google private access, static external and private IP address, network tags)

Creating ingress and egress firewall rules for a VPC (e.g., IP subnets, tags, service accounts)

Creating a VPN between a Google VPC and an external network using Cloud VPN

Creating a load balancer to distribute application network traffic to an application (e.g., Global HTTP(S) load balancer, Global SSL Proxy load balancer, Global TCP Proxy load balancer, regional network load balancer, regional internal load balancer)



Deploying a solution using Cloud Marketplace. Tasks include:



Browsing Cloud Marketplace catalog and viewing solution details

Deploying a Cloud Marketplace solution

Deploying application infrastructure using Cloud Deployment Manager. Tasks include:



Developing Deployment Manager templates

Launching a Deployment Manager template



Ensuring successful operation of a cloud solution

Managing Compute Engine resources. Tasks include:



Managing a single VM instance (e.g., start, stop, edit configuration, or delete an instance)

SSH/RDP to the instance

Attaching a GPU to a new instance and installing CUDA libraries

Viewing current running VM inventory (instance IDs, details)

Working with snapshots (e.g., create a snapshot from a VM, view snapshots, delete a snapshot)

Working with images (e.g., create an image from a VM or a snapshot, view images, delete an image)

Working with instance groups (e.g., set autoscaling parameters, assign instance template, create an instance template, remove instance group)

Working with management interfaces (e.g., Cloud Console, Cloud Shell, GCloud SDK)



Managing Google Kubernetes Engine resources. Tasks include:



Viewing current running cluster inventory (nodes, pods, services)

Browsing the container image repository and viewing container image details

Working with node pools (e.g., add, edit, or remove a node pool)

Working with pods (e.g., add, edit, or remove pods)

Working with services (e.g., add, edit, or remove a service)

Working with stateful applications (e.g. persistent volumes, stateful sets)

Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)



Managing App Engine and Cloud Run resources. Tasks include:



Adjusting application traffic splitting parameters

Setting scaling parameters for autoscaling instances

Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)



Managing storage and database solutions. Tasks include:



Moving objects between Cloud Storage buckets

Converting Cloud Storage buckets between storage classes

Setting object life cycle management policies for Cloud Storage buckets

Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Datastore, Cloud Bigtable)

Estimating costs of a BigQuery query

Backing up and restoring data instances (e.g., Cloud SQL, Cloud Datastore)

Reviewing job status in Cloud Dataproc, Cloud Dataflow, or BigQuery

Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)



Managing networking resources. Tasks include:



Adding a subnet to an existing VPC

Expanding a subnet to have more IP addresses

Reserving static external or internal IP addresses

Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)



Monitoring and logging. Tasks include:



Creating Stackdriver alerts based on resource metrics

Creating Stackdriver custom metrics

Configuring log sinks to export logs to external systems (e.g., on-premises or BigQuery)

Viewing and filtering logs in Stackdriver

Viewing specific log message details in Stackdriver

Using cloud diagnostics to research an application issue (e.g., viewing Cloud Trace data, using Cloud Debug to view an application point-in-time)

Viewing Google Cloud Platform status

Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)



Configuring access and security

Managing identity and access management (IAM). Tasks include:



Viewing IAM role assignments

Assigning IAM roles to accounts or Google Groups

Defining custom IAM roles



Managing service accounts. Tasks include:



Managing service accounts with limited privileges

Assigning a service account to VM instances

Granting access to a service account in another project



Viewing audit logs for project and managed services.



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Google VideoPoet AI video generator: what is it and how does it work? | Killexams.com Resources

google videopoet demo

Calvin Wankhede / Android Authority

When Google announced PaLM 2 and Gemini language fashions in mid-2023, the quest significant emphasised that its AI become multimodal. This supposed it may generate text, pictures, audio, and even video. traditionally, language fashions like ChatGPT’s GPT-four have best excelled at reproducing text. Google’s latest VideoPoet model challenges that proposal, besides the fact that children, because it can convert text-based prompts into AI-generated movies.

With VideoPoet, Google has develop into the first tech significant to announce an AI in a position to producing videos. and in contrast to prior makes an attempt, Google says it will possibly also generate scenes with lots of movement instead of just refined movements. So what’s the magic behind VideoPoet and what can it do? here’s every little thing you deserve to understand.

what's Google VideoPoet?

google videopoet block diagram

Google

Google VideoPoet is an experimental significant language model that may generate movies from a text-primarily based instantaneous. that you can describe a fictional scene, even one as ridiculous as “A robot cat consuming spaghetti,” and have a video able to watch within seconds. if you’ve ever used an AI image generator like Midjourney or DALL-E three, you already know what to expect from VideoPoet.

Like AI photograph mills, VideoPoet can also operate edits in existing video content. for instance, you could crop out a component of the video body and ask the AI to fill in the gap with anything from your imagination as a substitute.

Google has invested in startups like Runway working on AI video technology, however VideoPoet comes courtesy of the enterprise’s inner efforts. The VideoPoet technical paper enlists as many as 31 researchers from Google research.

How does Google VideoPoet work?

google how does videopoet work

Google

within the aforementioned paper, Google’s researchers defined that VideoPoet differs from popular textual content-to-picture and text-to-video mills. unlike Midjourney, for instance, VideoPoet doesn't use a spread model to generate images from random noise. That strategy works neatly for particular person photographs however falls flat for video clips where the model should account for movement and consistency over time.

At its core, Google’s VideoPoet is a large language model. This capability that it’s according to the equal expertise powering ChatGPT and Google Bard that may predict how words fit together to form sentences. VideoPoet takes that thought a step additional because it’s also in a position to predicting video and audio chunks, and never simply text.

VideoPoet is a big language model that generates movies as an alternative of text.

VideoPoet required a really expert pre-practicing manner which concerned translating photos, video frames, and audio clips into a standard language, referred to as tokens. Put without problems, the model discovered the way to interpret distinct modalities from the practising records. Google says that it used 1000000000 image-text pairs and 270 million public video samples to teach VideoPoet. finally, VideoPoet has turn into able to predicting video tokens similar to a normal LLM mannequin would predict textual content tokens.

VideoPoet has a robust foundation because of its practicing that allows it to function projects past text-to-video technology as well. for example, it could apply styles to current videos, perform edits like adding background consequences, change the seem of an latest video with filters, and change the action of a moving object in an present video. Google proven the latter with a raccoon dancing in quite a few styles.

VideoPoet vs. rival AI video generators: What’s the difference?

Meta logo on smartphone stock photo (5)

Edgar Cervantes / Android Authority

Google’s VideoPoet differs from most of its competitors that depend on diffusion models to show text into movies. although, it’s no longer exactly the first – a smaller variety of Google mind researchers presented Phenaki closing year. Likewise, Meta’s Make-A-Video assignment made waves in the AI group for generating diverse movies devoid of practising on video-text pairs formerly. although, neither fashions have been publicly launched.

So considering they don’t have access to any video-generating fashions, they are able to most effective count on the advice Google has supplied about VideoPoet. With that in mind, the paper’s authors assert that “in lots of circumstances, even the latest main models both generate small action or, when producing larger motions, display substantive artifacts.” VideoPoet, on the other hand, can handle more action.

VideoPoet can generate longer video clips and address motion extra gracefully than the competitors.

Google additionally says that VideoPoet can generate longer video clips than the competition. while it’s constrained to an initial burst of two-2d video clips, it will possibly keep context throughout eight to ten seconds of video. That might also not sound like a lot nonetheless it’s superb given how a lot a scene may exchange in that point length. however, Google’s instance movies most effective consist of a couple of dozen frames, far from the 24 or 30 frames per second benchmark used for expert video or filmmaking.

Google VideoPoet availability: Is it free?

google videopoet samples

Google

while Google has posted dozens of example movies to reveal the strengths of VideoPoet, it stopped in need of announcing a public rollout. In other phrases, they don’t comprehend after we’ll be capable of use VideoPoet, if at all.

Google hasn't announced a product or release date for VideoPoet yet.

As for pricing, they might also have to take the hint from AI image generators like Midjourney that are most effective available by the use of a subscription. indeed, AI-generated photos and videos are computationally costly so opening up entry to everyone may additionally now not be possible, even for Google. We’ll need to look ahead to a disruptive liberate like OpenAI’s ChatGPT to force the search large’s hand. unless then, we’ll quite simply should wait and watch from the sidelines.

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