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NEW QUESTION 27
JencoMart wants to move their User Profiles database to Google Cloud Platform.
Which Google Database should they use?
- A. Google Cloud SQL
- B. Cloud Spanner
- C. Google BigQuery
- D. Google Cloud Datastore
Common workloads for Google Cloud Datastore:
* User profiles
* Product catalogs
* Game state
Mountkirk Games, A
Mountkirk Games makes online, session-based, multiplayer games for the most popular mobile platforms.
They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
* Increase to a global footprint
* Improve uptime - downtime is loss of players
* Increase efficiency of the clous resources we use
* Reduce lateny to all customers
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity
2. Connect to a managed NoSQL database service
3. Run customize Linux distro
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity
2. Process incoming data on the fly directly from the game servers
3. Process data that arrives late because of slow mobile networks
4. Allow SQL queries to access at least 10 TB of historical data
5. Process files that are regularly uploaded by users' mobile devices
6. Use only fully managed services
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users.
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
We are not capturing enough user demographic data, usage metrics, and other KPIs. As a result, we do not engage the right users, we are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.
NEW QUESTION 28
Your company runs several databases on a single MySQL instance. They need to take backups of a specific database at regular intervals. The backup activity needs to complete as quickly as possible and cannot be allowed to impact disk performance.
How should you configure the storage?
- A. Mount a Local SSD volume as the backup location. After the backup is complete, use gsutil to move the backup to Google Cloud Storage.
- B. Use gcsfise to mount a Google Cloud Storage bucket as a volume directly on the instance and write backups to the mounted location using mysqldump.
- C. Configure a cron job to use the gcloud tool to take regular backups using persistent disk snapshots.
- D. Mount additional persistent disk volumes onto each virtual machine (VM) instance in a RAID10 array and use LVM to create snapshots to send to Cloud Storage
NEW QUESTION 29
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combination of Google technologies will meet all of their requirements?
- A. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
- B. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
- C. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
- D. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
- E. Container Engine, Cloud Pub/Sub, and Cloud SQL
A real time requires Stream / Messaging so Pub/Sub, Analytics by Big Query.
Ingest millions of streaming events per second from anywhere in the world with Cloud Pub/Sub, powered by Google's unique, high-speed private network. Process the streams with Cloud Dataflow to ensure reliable, exactly-once, low-latency data transformation. Stream the transformed data into BigQuery, the cloud-native data warehousing service, for immediate analysis via SQL or popular visualization tools.
From scenario: They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics.
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow SQL queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Use only fully managed services
NEW QUESTION 30
Your development team has created a mobile game app. You want to test the new mobile app on Android and iOS devices with a variety of configurations. You need to ensure that testing is efficient and cost-effective. What should you do?
- A. Create Android and iOS VMs on Google Cloud, install the mobile app on the VMs, and test the mobile app.
- B. Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the containers, and test the mobile app.
- C. Upload your mobile app with different configurations to Firebase Hosting and test each configuration.
- D. Upload your mobile app to the Firebase Test Lab, and test the mobile app on Android and iOS devices.
Topic 8, Helicopter Racing League Case
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.
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.
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.
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.
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 31