Free Practice Questions for Google Cloud-Digital-Leader Exam

The correct answer is D. Create a model in BigQuery to predict future maintenance schedules. Here's why:

Context of the Question: The organization wants to use Google Cloud services to efficiently upgrade and maintain their network to reduce waste and achieve sustainability goals.

Google Cloud Product Relevance:

BigQuery is a fully managed data warehouse that supports SQL queries for analyzing large datasets. It also has built-in machine learning capabilities (BigQuery ML) that allow users to create and train models directly within the database using SQL.

By creating a predictive model in BigQuery, the organization can analyze historical data to forecast future maintenance needs, optimize upgrade schedules, and minimize waste, aligning with their sustainability goals.

Why Not Other Options:

A. Secure data access by using Identity and Access Management: While IAM is important for securing access, it does not directly help with maintenance prediction or sustainability goals.

B. Move data from BigQuery to Cloud Storage to reduce storage costs: This option focuses on cost savings, not on improving operational efficiency or achieving sustainability goals.

C. Provide access to third-party developers by creating an API using Apigee: This is not directly related to the goal of optimizing maintenance and reducing waste.

Google Cloud Digital Leader References:

Refer to BigQuery ML documentation to learn how to create and use machine learning models within BigQuery.

An Application Programming Interface (API) is a set of protocols, routines, and tools that allow different software applications to communicate with each other. APIs provide the necessary instructions for one program to interact with another, enabling the integration of different systems and the development of new functionalities.

Option D: To provide a set of instructions that allow computer programs to communicate with each other is correct because it accurately describes the fundamental purpose of an API.

The correct answer is B. Machine learning. Here’s why:

Context of the Question: The question describes an application that can diagnose medical conditions based on images submitted by users. This is a task typically handled by machine learning (ML) models, specifically those focused on image recognition and classification.

Google Cloud Product Relevance:

Machine learning (ML) is the correct service because it refers to the use of Google Cloud's AI/ML capabilities, which include services such as AutoML Vision and Vertex AI. These tools can build and deploy machine learning models specifically for image recognition tasks, such as identifying medical conditions from images.

AutoML Vision is designed to create custom ML models with minimal ML expertise, making it suitable for diagnosing medical conditions from images.

Vertex AI allows for training, evaluating, and deploying ML models, including complex models for medical image diagnostics.

Why Not Other Options:

A. App Engine: App Engine is a platform-as-a-service (PaaS) used for building and deploying web applications, not specifically for ML or image recognition tasks.

C. Cloud Logging: Cloud Logging is used to store, search, analyze, and monitor log data and events. It does not have any capabilities related to diagnosing medical conditions or image recognition.

D. Cloud Profiler: Cloud Profiler is a performance analysis tool for applications running in production. It helps identify bottlenecks and optimize resource usage but has no relevance to image diagnosis or machine learning.

Google Cloud Digital Leader References:

For more information on Machine Learning with Google Cloud, refer to the Google Cloud documentation on AutoML Vision and Vertex AI.

Understanding the basic principles and applications of Google Cloud’s ML services is crucial for the Google Cloud Digital Leader exam, which emphasizes knowing how different services solve business challenges.

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.

The correct answer is D. Service-level objective. Here's why:

Context of the Question: The organization wants to understand the term used in Site Reliability Engineering (SRE) for defining the desired level of reliability and performance.

Google Cloud Product Relevance:

A Service-Level Objective (SLO) is a key term in SRE, representing the target level of reliability or performance for a specific service. SLOs define acceptable levels of service in terms of availability, latency, or other performance metrics. They are used to set clear expectations and help measure whether services meet the desired reliability levels.

SLOs are closely tied to Service-Level Agreements (SLAs) and Service-Level Indicators (SLIs), where SLIs measure specific aspects of service performance, and SLOs set the target levels based on those indicators.

Why Not Other Options:

A. Enhanced support: This is a support option for Google Cloud customers and is not related to SRE concepts.

B. Scalable infrastructure: This refers to cloud infrastructure's ability to scale resources up or down but is not specific to reliability and performance metrics.

C. Service-level indicator: An SLI measures a specific aspect of the service’s performance but does not define the target level of performance.

Google Cloud Digital Leader References:

Refer to the Site Reliability Engineering (SRE) documentation to learn more about SLOs, SLIs, and SLAs.

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