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To prepare for the Google Professional-Cloud-Architect exam, candidates should have experience in designing and implementing cloud solutions using GCP. They should also have a good understanding of cloud computing concepts, such as virtualization, networking, and storage. Google recommends that candidates complete the Architecting with Google Cloud Platform specialization on Coursera before taking the exam.
There are no official requirements that the candidates for the Google Professional Cloud Architect certification need to satisfy. However, it is recommended that the applicants possess 3 or more years of industry experience, including at least 1 year of experience with designing and managing solutions using Google Cloud Platform. Their solid knowledge of the topics covered in the qualifying exam is also required.
Google Professional-Cloud-Architect Certification is designed for individuals who are seeking to demonstrate their expertise in designing, developing, and managing robust, secure, scalable, and highly available cloud solutions using Google Cloud Platform (GCP). Google Certified Professional - Cloud Architect (GCP) certification exam is ideal for cloud architects, solution architects, and IT professionals who work with GCP on a regular basis.
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NEW QUESTION # 144
For this question, refer to the Mountkirk Games case study. Which managed storage option meets Mountkirk's technical requirement for storing game activity in a time series database service?
Answer: B
Explanation:
Reference:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using-gcp
NEW QUESTION # 145
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?
Answer: D
Explanation:
https://cloud.google.com/compute/docs/instance-templates
NEW QUESTION # 146
The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most
15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss.
Which process should you implement?
Answer: D
NEW QUESTION # 147
For this question, refer to the Dress4Win case study.
As part of Dress4Win's plans to migrate to the cloud, they want to be able to set up a managed logging and monitoring system so they can handle spikes in their traffic load.
They want to ensure that:
* The infrastructure can be notified when it needs to scale up and down to handle the ebb and flow of usage throughout the day
* Their administrators are notified automatically when their application reports errors.
* They can filter their aggregated logs down in order to debug one piece of the application across many hosts Which Google StackDriver features should they use?
Answer: A
NEW QUESTION # 148
Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily.
You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user's perspective. What should you do?
Answer: A
Explanation:
Topic 8, Helicopter Racing League Case
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
* Existing content is stored in an object storage service on their existing public cloud provider.
* Video encoding and transcoding is performed on VMs created for each job.
* Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
* Support ability to expose the predictive models to partners.
* Increase predictive capabilities during and before races:
Race results
Mechanical failures
Crowd sentiment
* Increase telemetry and create additional insights.
* Measure fan engagement with new predictions.
* Enhance global availability and quality of the broadcasts.
* Increase the number of concurrent viewers.
* Minimize operational complexity.
* Ensure compliance with regulations.
* Create a merchandising revenue stream.
Technical requirements
* Maintain or increase prediction throughput and accuracy.
* Reduce viewer latency.
* Increase transcoding performance.
* Create real-time analytics of viewer consumption patterns and engagement.
* Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S.
Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
NEW QUESTION # 149
......
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