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NEW QUESTION 24
You are tasked with building an online analytical processing (OLAP) marketing analytics and reporting tool. This requires a relational database that can operate on hundreds of terabytes of data. What is the Google- recommended tool for such applications?
- A. BigQuery, because it is designed for large-scale processing of tabular data
- B. Cloud Firestore, because it offers real-time synchronization across devices
- C. Cloud SQL, because it is a fully managed relational database
- D. Cloud Spanner, because it is globally distributed
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/files/BigQueryTechnicalWP.pdf
NEW QUESTION 25
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?
- A. Create Server Uptime and Error Rate as service level indicators.
- B. Create CPU Utilization and Request Latency as service level indicators.
- C. Create GKE CPU Utilization and Memory Utilization as service level indicators.
- D. Create Request Latency and Error Rate as service level indicators.
Answer: D
Explanation:
Topic 10, Helicopter Racing League
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 26
You need to develop procedures to test a disaster plan for a mission-critical application. You want to use Google-recommended practices and native capabilities within GCP.
What should you do?
- A. Use Deployment Manager to automate service provisioning. Use Stackdriver to monitor and debug your tests.
- B. Use gcloud scripts to automate service provisioning. Use Activity Logs monitor and debug your tests.
- C. Use gcloud scripts to automate service provisioning. Use Stackdriver to monitor and debug your tests.
- D. Use Deployment Manager to automate service provisioning. Use Activity Logs to monitor and debug your tests.
Answer: A
NEW QUESTION 27
Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a through testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?
- A. Create a set of static environments in GCP to test different levels of load - for example, high, medium, and low
- B. Create a scalable environment in GCP for simulating production load
- C. Use the existing infrastructure to test the GCP-based backend at scale
- D. Build stress tests into each component of your application using resources internal to GCP to simulate load
Answer: B
Explanation:
From scenario: 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
NEW QUESTION 28
Your company is designing its application landscape on Compute Engine. Whenever a zonal outage occurs, the application should be restored in another zone as quickly as possible with the latest application dat a. You need to design the solution to meet this requirement. What should you do?
- A. Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another zone in the same region. Use the regional persistent disk for the application data.
- B. Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in another zone within the same region.
- C. Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another region. Use the regional persistent disk for the application data,
- D. Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in the same zone.
Answer: A
Explanation:
Regional persistent disk is a storage option that provides synchronous replication of data between two zones in a region. Regional persistent disks can be a good building block to use when you implement HA services in Compute Engine. https://cloud.google.com/compute/docs/disks/high-availability-regional-persistent-disk
NEW QUESTION 29
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