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| Section | Weight | Objectives |
|---|---|---|
| Trust and Security with Google Cloud | 17% | - Data security, encryption, and compliance - Identity and access management - Risk management and incident response - Security principles and shared responsibility model |
| Scaling with Google Cloud Operations | 16% | - Business continuity and disaster recovery - Automation and infrastructure as code - Operational excellence and reliability - Monitoring, logging, and observability tools |
| Exploring Data Transformation with Google Cloud | 17% | - Google Cloud data storage and analytics services - Modern data architectures and solutions - Data-driven decision making and business value - Data lifecycle management and governance |
| Innovating with Google Cloud Artificial Intelligence | 16% | - Responsible AI principles and practices - Google Cloud AI/ML products and tools - AI and ML fundamentals and business application - Use cases for AI in business operations and customer experience |
| Digital Transformation with Google Cloud | 17% | - Cost management and pricing models in Google Cloud - Impact of cloud infrastructure on business flexibility, scalability, and agility - Cloud service models: IaaS, PaaS, SaaS - Google Cloud global infrastructure and resource management |
| Modernize Infrastructure and Applications with Google Cloud | 17% | - Application modernization: containers, Kubernetes, serverless - Migration and modernization strategies - Compute, storage, and networking solutions - Hybrid and multi-cloud environments |
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NEW QUESTION # 396
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"?
Answer: C
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/machine-learning/guides/rules-of-ml#terminology
NEW QUESTION # 397
An organization has hired a team of data scientists and developers They want to create unique value in their business by coding an advanced machine learning model in Vertex Al Workbench Which service should the organization use to train the model?
Answer: B
NEW QUESTION # 398
An organization wants to refactor their application by using a microservices architecture when migrating to the cloud. Which benefit would this action provide?
Answer: C
Explanation:
The correct answer is A. The refactored application is more efficient and scalable. Here's why:
* Context of the Question: The organization wants to refactor its application into a microservices architecture when migrating to the cloud.
* Google Cloud Product Relevance:
* Microservices architecture breaks down applications into smaller, independent services that can be developed, deployed, and scaled independently. This increases efficiency and scalability, as each microservice can be scaled according to its needs without affecting the entire application.
* Google Cloud provides tools such as Kubernetes Engine (GKE) for container orchestration, Anthos for managing microservices across hybrid and multi-cloud environments, and Cloud Run for deploying containerized applications efficiently.
* Why Not Other Options:
* B. No code changes will be required: Refactoring to microservices often requires significant code changes to decouple services.
* C. This migration pattern provides the fastest path to the cloud: Refactoring to microservices is a complex process that typically takes more time compared to a "lift-and-shift" migration.
* D. The application will become PCI-DSS compliant by default: Compliance with PCI-DSS (Payment Card Industry Data Security Standard) depends on specific security controls, not on using microservices architecture.
Google Cloud Digital Leader References:
* For more information on microservices and cloud-native architectures, refer to the Google Cloud Microservices documentation.
NEW QUESTION # 399
A partner of yours used to have their own private data center. Your company was already on Google Cloud and now they have also moved to Google Cloud. You are investigating whether there are ways to collaborate better or shared services. What would be one good option to consider?
Answer: A
Explanation:
Explanation
VPC Network Peering allows internal IP address connectivity across two Virtual Private Cloud (VPC) networks regardless of whether they belong to the same project or the same organization.
-> Shared VPC is only within an organization - it allows an organization to connect resources from multiple projects to a common Virtual Private Cloud (VPC) network, so that they can communicate with each other securely and efficiently using internal IPs from that network.
-> Private Google Access is only to access Google APIs and services
References:
-> https://cloud.google.com/vpc/docs/vpc-peering
-> https://cloud.google.com/vpc/docs/private-google-access
-> https://cloud.google.com/vpc/docs/shared-vpc
NEW QUESTION # 400
Which of the following statements is/are true about Google Cloud BigTable?
Answer: B
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 # 401
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