Google Cloud-Digital-Leader Online Access

The correct answer is B. Platform as a service. Here's why:

Context of the Question: The organization wants to run a custom application in the cloud in a flexible and scalable way without managing any infrastructure.

Google Cloud Product Relevance:

Platform as a Service (PaaS) provides a platform that allows customers to develop, run, and manage applications without dealing with the complexity of building and maintaining the underlying infrastructure.

Google Cloud's App Engine is an example of a PaaS offering. It enables organizations to deploy custom applications in a fully managed environment where scaling, load balancing, and infrastructure management are handled automatically.

Why Not Other Options:

A. Infrastructure as a service: IaaS requires the organization to manage the underlying infrastructure (like virtual machines, networks, and storage), which is not suitable if the organization does not want to manage any infrastructure.

C. Network as a service: NaaS is focused on network connectivity and does not provide the capabilities needed to run custom applications.

D. Software as a service: SaaS provides pre-built applications managed by a provider; it is not suitable for running custom applications.

Google Cloud Digital Leader References:

For more on PaaS, refer to the Google App Engine documentation.

When data is uploaded to Google Cloud, the customer retains full ownership of their data. Google Cloud acts as the data processor, providing the infrastructure and services for storage, but the rights to the data, including access, control, and management, remain with the customer.

Option B: The customer is correct because, according to Google Cloud's data privacy policy, the customer owns their data.

References:

Google Cloud Privacy Policy: Data Ownership and Responsibility

Google Cloud Trust Principles: Data Protection and Privacy

The correct answer is D. AutoML. Here's why:

Context of the Question: The organization wants to build a solution to detect faulty parts on a production line using a large collection of labeled images (intact or defective parts). They do not have data science expertise, so they need a tool that simplifies the machine learning process.

Google Cloud Product Relevance:

AutoML is a suite of machine learning products that allows users to build custom models easily without needing deep expertise in machine learning or data science. It is designed to simplify the process of training, evaluating, and deploying models, especially in cases like image recognition where labeled datasets are already available.

AutoML Vision, a part of AutoML, would be ideal for this use case as it specifically handles image classification and can easily differentiate between intact and defective parts.

Why Not Other Options:

A. Discovery AI for Retail: This is a solution tailored for retail use cases like product discovery and search optimization, not for manufacturing defect detection.

B. Pre-trained APIs: While pre-trained APIs (like Vision API) can recognize general image patterns, they may not be specific enough for a custom use case like detecting defective parts.

C. Document AI: This service is designed for understanding and processing documents, not for analyzing images.

Google Cloud Digital Leader References:

For more information on AutoML, refer to the AutoML Vision documentation.