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Google Cloud-Digital-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Digital Transformation with Google Cloud	17%	- Impact of cloud infrastructure on business flexibility, scalability, and agility - Cloud service models: IaaS, PaaS, SaaS - Cost management and pricing models in Google Cloud - Google Cloud global infrastructure and resource management
Trust and Security with Google Cloud	17%	- Risk management and incident response - Security principles and shared responsibility model - Identity and access management - Data security, encryption, and compliance
Innovating with Google Cloud Artificial Intelligence	16%	- Use cases for AI in business operations and customer experience - Responsible AI principles and practices - AI and ML fundamentals and business application - Google Cloud AI/ML products and tools
Scaling with Google Cloud Operations	16%	- Operational excellence and reliability - Business continuity and disaster recovery - Automation and infrastructure as code - Monitoring, logging, and observability tools
Modernize Infrastructure and Applications with Google Cloud	17%	- Application modernization: containers, Kubernetes, serverless - Compute, storage, and networking solutions - Hybrid and multi-cloud environments - Migration and modernization strategies
Exploring Data Transformation with Google Cloud	17%	- Modern data architectures and solutions - Google Cloud data storage and analytics services - Data-driven decision making and business value - Data lifecycle management and governance

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Google Cloud Digital Leader Sample Questions (Q208-Q213):

NEW QUESTION # 208
An organization recently launched a virtual customer support agent, generating vast amounts of text and speech data.
Why should they use a cloud data warehouse to interpret this data?

A. To natively visualize both types of data using a dashboard in real time
B. To secure data transmission between cloud and on-premises environments
C. To transform data from structured to unstructured
D. To ingest and analyze structured and unstructured data at scale, in real time

Answer: D

Explanation:
Explanation
Real-time data ingestion and updates. A simple and universal solution for continually ingesting your enterprise data into popular cloud-based data warehouses in real time.
https://www.qlik.com/us/cloud-data-migration/cloud-data-warehouse

NEW QUESTION # 209
An organization needs a flexible and scalable NoSQL database with strong web and mobile application support. Which Google Cloud product or service should the organization use?

A. restore
B. Cloud Storage
C. BigQuery
D. Cloud Spanner

Answer: B

NEW QUESTION # 210
A customer is migrating there on-promises data analytics solution to Google Cloud. The current solution has a lot of data being read form and written to disk. The performance of this approach has occasionally been a bottleneck for a scale of operations that your cus-tomer has. The application is fault tolerant and can with stand machine going down fre-quently. In moving to Google Cloud they are asking your advice on any way to improve performance?

A. Use Cloud Storage which can be central, scalable storage
B. Use Persistent Disk with the VMs
C. Use Big Query Which has very fast data access and analysis
D. Use local SSDs with the VMs

Answer: D

Explanation:
Local SSDs are attached to the VM and have very high throughput. However, when the VM shuts down, The local SSD is also shut down, Since our Workload here is foult tolerant, than is not an issue.

NEW QUESTION # 211
Your company security team manages access control to production systems using an LDAP directory group.
How is this access control managed in the Google Cloud production project?

A. Assign the proper role to the Google Group in the project's 1AM Policy.
B. Grant each user the roles/iam.serviceAccountUser role on a service account that exists in the Google Group.
C. Assign the proper role to the Service Account in the project's 1AM Policy
D. Create the project in a folder with the same name as the LDAP directory group.

Answer: A

NEW QUESTION # 212
Which of the following is true while creating a boot persistent disk from a snapshot.

A. You cannot apply a snapshot to an existing persistent disk, or apply a snapshot to persistent disks that belong to a different project than that snapshot.
B. It is only possible to apply data from a snapshot when you first create a persistent disk.
C. All of the above.
D. After you create a snapshot of a boot persistent disk, you can apply data from that snapshot to new persistent disks.

Answer: C

Explanation:
When you create a virtual machine (VM) instance, you must also create a boot disk for the VM. You can use a public image, a custom image, or a snapshot that was taken from another boot disk. When you create a boot disk, limit the disk size to 2 TB to account for the limitations of MBR partitioning.
Compute Engine automatically creates a boot persistent disk when you create an instance. If you require additional data storage space for your instances, add one or more secondary instance storage options.
You might need to create a standalone boot persistent disk and attach it to an instance later, or resize a boot persistent disk to improve performance and add more space for additional applications or operating system files. That process is described in Add or resize a persistent disk.
As a best practice, do not use regional persistent disks for boot disks. In a failover situation, they do not force-attach to a VM.
After you create a snapshot of a boot persistent disk, you can apply data from that snapshot to new persistent disks. It is only possible to apply data from a snapshot when you first create a persistent disk. You cannot apply a snapshot to an existing persistent disk, or apply a snapshot to persistent disks that belong to a different project than that snapshot.

NEW QUESTION # 213
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