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[General] Cloud-Digital-Leader Exam Success & Valid Cloud-Digital-Leader Exam Guide

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【General】 Cloud-Digital-Leader Exam Success & Valid Cloud-Digital-Leader Exam Guide

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Google Cloud Digital Leader Sample Questions (Q158-Q163):NEW QUESTION # 158
Your team is working on building a machine learning model. There are a bunch of terminologies that are being used. What is an "instance" or an "example"?
  • A. The "knobs" that you tweak during successive runs of training a model. E.g. learning rate
  • B. One row of a dataset containing one or more input columns and possibly a prediction result.
  • C. An answer for a prediction task, either the answer produced by a machine learning system or the right answer supplied in training data. E.g. image contains a "cat".
  • D. An input variable is used in making predictions. E.g. number of rooms in a house price prediction model.
Answer: B
Explanation:
One row of a dataset containing one or more input columns and possibly a prediction result.
A picture containing timeline Description automatically generated

https://developers.google.com/ma ... s-of-ml#terminology

NEW QUESTION # 159
You are consulting for a client who is migrating to Google Cloud. They presently have a matrix or-ganization.
Their IT environments were managed around projects. Each team had multiple projects. All the projects had a flat structure under the company. What would you advise them when plan-ning for the move?
  • A. Since a Project could spawn other sub-Projects, on Google Cloud it is better to as-sign a folder for each Project.
  • B. The flat structure is what is currently used in IT organizations, and this can be used as-is which will provide the best results.
  • C. On Google Cloud, create a folder corresponding to each team. Under that, there could be projects or further sub folders as the team decides.
  • D. In terms of not disturbing the project developers and testers, advise them that the strategic decision is to retain the structure on Google Cloud also.
Answer: C
Explanation:
Folders for a related group of projects are the recommended approach.
-> A flat structure under the organization node is possible on Google Cloud, but it is not recommended. It becomes tougher to manage.
-> Projects cannot have sub-projects; there can only be resources within Projects.
Reference link- https://cloud.google.com/resourc ... -resource-hierarchy

NEW QUESTION # 160
Which scenario uses machine learning to unlock business value from unstructured data?
  • A. Recommending new products based on previous purchases
  • B. Determining customer sentiments from call center voice recordings
  • C. Analyzing tabular records of product defects to predict future maintenance cycles
  • D. Monitoring financial transactions to identify potential fraud and risk
Answer: B

NEW QUESTION # 161
Your company is running the majority of its workloads in a co-located data center. The workloads are running on virtual machines (VMs) on top of a hypervisor and use either Linux or Windows server editions. As part of your company's transformation strategy, you need to modernize workloads as much as possible by adopting cloud-native technologies. You need to migrate the workloads into Google Cloud.
What should you do?
  • A. Export the VMs into VMDK format, and import them into Google Cloud VMware Engine
  • B. Migrate the workloads using Migrate for Compute Engine
  • C. Export the VMs into VMDK format, and import them into Compute Engine
  • D. Migrate the workloads using Migrate for Anthos
Answer: D
Explanation:
Anthos: Anthos lets you build, deploy, and manage applications anywhere in a secure, consistent manner. You can modernize existing applications running on virtual machines while deploying cloud-native apps on containers in an increasingly hybrid and multi-cloud world.

NEW QUESTION # 162
An organization is building advanced machine learning models in Google Cloud by using TensorFlow. They want to develop their models faster with purpose-built hardware. Which solution should the organization use?
  • A. CPUs
  • B. GPUs
  • C. DPUs
  • D. TPUs
Answer: D
Explanation:
Tensor Processing Units (TPUs) are a type of purpose-built hardware designed specifically to accelerate machine learning workloads, particularly those involving TensorFlow. TPUs provide significant speed and performance improvements over general-purpose CPUs and even GPUs when running complex ML models.
Option C: TPUs is correct because TPUs (Tensor Processing Units) are optimized to accelerate the training of machine learning models developed with TensorFlow, providing faster performance compared to other hardware.
References:
* Google Cloud: TPUs Overview
* Google Cloud AI and Machine Learning Products: Accelerators for TensorFlow

NEW QUESTION # 163
......
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