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NEW QUESTION 54
An organization is training a machine learning model to predict extreme weather events in their country.
How should they collect data to maximize prediction accuracy?

  • A. Collect all weather data primarily from at-risk cities
  • B. Collect extreme weather data primarily from at-risk cities
  • C. Collect all weather data evenly across all cities
  • D. Collect extreme weather data evenly across all cities

Answer: C

Explanation:
Collect all weather data evenly across all cities. Mainly because it seems that the emphasis for data collection for ML is to make sure there are no holes in your data collection.

 

NEW QUESTION 55
Your company has made plans to roll out OpenShift, a Kubernetes platform solution offered by IBM Red Hat, across all its on-premises and public cloud environments. Given that you are the lead architect responsible for your company’s GCP deployments, what type of shared responsibility model will this deployment entail for you?

  • A. SaaS
  • B. PaaS
  • C. IaaS
  • D. On premises

Answer: C

Explanation:
The key to remember here is that for a service provided (GCP in this case) to take responsibility for its PaaS, it must offer the service as a managed service. GCP offers its own Kubernetes platform called GKE. But OpenShift is not a Google-offered PaaS solution. As such, Google will not take responsibility for the back-end operations and design of your OpenShift environments. You will need to manage all the VMs that OpenShift will provision as part of its GCP deployment. So this is an IaaS deployment from a shared responsibility model perspective.

 

NEW QUESTION 56
Which of the following statements is/are true about Google Cloud BigTable?

  • A. It is an enterprise-level Database that offers relational and non-relational features
  • B. It can not be used in Real-time Ad analytics and tracking thousands of IoT Devices Data.
  • C. It is not compatible with Hadoop.
  • D. It Scales from Giga Byte to Peta Byte with No Downtime.

Answer: D

Explanation:
Cloud Bigtable
A fully managed, scalable NoSQL database service for large analytical and operational workloads with up to 99.999% availability.
– Consistent sub-10ms latency-handle millions of requests per second
– Ideal for use cases such as personalization, ad tech, fintech, digital media, and IoT
– Seamlessly scale to match your storage needs; no downtime during reconfiguration
– Designed with a storage engine for machine learning applications leading to better predictions
– Easily connect to Google Cloud services such as BigQuery or the Apache ecosystem

 

NEW QUESTION 57
How is service availability measured in the context of cloud technology?

  • A. Number of downtime incidents
  • B. Percentage of uptime
  • C. Number of available regions
  • D. Speed of response time

Answer: B

 

NEW QUESTION 58
……

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