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Google Cloud Digital Leader Certification Exam is ideal for professionals who are interested in advancing their careers in digital transformation and cloud computing. It is also suitable for organizations that are looking to enhance their digital transformation capabilities by training and certifying their employees on Google Cloud technologies. Google Cloud Digital Leader certification exam is recognized globally and demonstrates a high level of expertise in digital transformation using Google Cloud technology.

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Google Cloud Digital Leader Sample Questions (Q152-Q157):

NEW QUESTION # 152
An organization is developing applications by using Kubernetes. They want their teams to spend more time developing rather than managing clusters. Which Google Cloud service should the organization choose?

A. Compute Engine
B. Cloud Run
C. GKE Autopilot
D. GKE Standard

Answer: C

Explanation:
The correct answer is C. GKE Autopilot. Here's why:
* Context of the Question: The organization is using Kubernetes and wants to minimize cluster management so their teams can focus more on developing applications.
* Google Cloud Product Relevance:
* GKE Autopilot is a fully managed mode of operation for Google Kubernetes Engine (GKE) that abstracts much of the cluster management, such as provisioning and managing the control plane, nodes, and infrastructure. It automates operational tasks and optimizes resource management, enabling developers to focus on writing and deploying code rather than managing Kubernetes clusters.
* GKE Autopilot is ideal for organizations that want the benefits of Kubernetes without the overhead of managing cluster infrastructure.
* Why Not Other Options:
* A. Cloud Run: While Cloud Run is a fully managed service for running containers, it is not specifically a Kubernetes service and does not offer the same Kubernetes-native experience as GKE Autopilot.
* B. Compute Engine: Compute Engine requires manual management of VMs and is not specific to containerized or Kubernetes applications.
* D. GKE Standard: GKE Standard provides more control over cluster configuration but requires more management effort compared to GKE Autopilot.
Google Cloud Digital Leader References:
* For more details on GKE Autopilot, refer to the GKE Autopilot documentation.

NEW QUESTION # 153
Your organization is running all its workloads in a private cloud on top of a hypervisor. Your organization has decided it wants to move to Google Cloud as quickly as possible. Your organization wants minimal changes to the current environment, while using the maximim amount of managed services Google offers.
What should your organization do?

A. Migrate the workloads to Compute Engine
B. Migrate the workloads to Google Kubernetes Engine
C. Migrate the workloads to Bare Metal Solution
D. Migrate the workloads to Google Cloud VMware Engine

Answer: A

Explanation:
Migrate for Compute Engine enables you to lift and shift workloads at scale to Google Cloud Compute Engine with minimal changes and risk.

NEW QUESTION # 154
Your organization needs to process large amounts of data from an online application that operates continuously. You do not want to be required to provision infrastructure or create server clusters. What should your organization choose?

A. Dataflow
B. Dataproc
C. Google Kubernetes Engine with Cloud Bigtable
D. Compute Engine with BigQuery

Answer: A

Explanation:
Explanation
You do not want to be required to provision infrastructure or create server clusters. Because Unified stream and batch data processing that's serverless, fast, and cost-effective.
Reference link- https://cloud.google.com/dataflow

NEW QUESTION # 155
A Multiple projects within your organization have long-term VM usage. Based on current demand, they are able to project into the future and estimate how many VM hours they will use every year. Going in for a committed use contract seems sensible. How can you configure committed use easily across all the projects?

A. Take a report of each project's use in the last year. Enable committed use on a per-project basis based on the previous year's numbers.
B. Review the usage of resources by each project on a daily basis. Enable committed use for the following day based on that number, so that it gives maximum granularity without wastage.
C. Share a Google Sheet and request each project team to send in their estimate. Enable committed use accordingly on a per-project basis.
D. Enable committed use with discount sharing for the projects

Answer: D

Explanation:
Explanation
Enable committed use discounts are applied to the project from which you purchased it. To share the discount across multiple projects linked to your Cloud Billing account, enable committed use discount sharing from the console. When you enable committed use discount sharing, all of your current active committed use dis-counts in all the projects under the same Cloud Billing account, including those you previously purchased and new ones you purchase in the future are shared across your Cloud Billing account. Your sustained use discounts are also pooled and shared across all projects within your Cloud Billing account.

NEW QUESTION # 156
A retail organization is training a model to recommend products to customers for an ecommerce website. The model was trained on previous purchases, but did not include demographic information on each buyer. What dimension of the data is responsible for the model's poor performance?

A. Validity
B. Completeness
C. Timeliness
D. Accuracy

Answer: B

Explanation:
Completeness refers to the degree to which all necessary data is present and accounted for. In the context of machine learning, if a model is trained without including all relevant dimensions (such as demographic information in this case), it might lead to poor performance because it lacks important data needed to make accurate predictions.
Option D: Completeness is correct because the lack of demographic information represents incomplete data. The absence of this data dimension could prevent the model from learning patterns that are influenced by demographic factors, thereby impacting its overall effectiveness.
Reference:
Google Cloud Documentation: Data Quality in Machine Learning
Google Cloud AI Platform: Best Practices for Training Data Preparation

NEW QUESTION # 157
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