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Quiz 2025 Google Perfect Cloud-Digital-Leader: Google Cloud Digital Leader Exam Consultant
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Google Cloud Digital Leader Certification Exam is a professional accreditation offered by Google Cloud to individuals seeking to validate their expertise and skillset in digital transformation and cloud technologies. Cloud-Digital-Leader exam is designed to test the candidate's knowledge in areas such as cloud computing, data analytics, machine learning, and digital transformation, among others. Google Cloud Digital Leader certification is globally recognized and demonstrates proficiency in digital leadership and cloud technologies.
Google Cloud Digital Leader Certification Exam is a globally recognized certification designed for professionals who are interested in advancing their knowledge and skills in digital transformation using Google Cloud technology. Cloud-Digital-Leader Exam is meant to validate the candidate's understanding of the core principles and best practices of digital transformation, as well as their ability to lead and implement digital transformation initiatives using Google Cloud technologies.
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Google Cloud Digital Leader Sample Questions (Q69-Q74):
NEW QUESTION # 69
A manufacturing organization has a large collection of images labeled as intact or defective parts. They want to use this data to build a simple solution to detect faulty parts on their production line. They have no data science expertise. Which solution should they use?
- A. Discovery Al for Retail
- B. Document Al
- C. AutoML
- D. Pre-trainedAPIs
Answer: C
Explanation:
The correct answer is D. AutoML. Here's why:
Context of the Questio n : 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 Reference:
For more information on AutoML, refer to the AutoML Vision documentation.
NEW QUESTION # 70
Your organization consists of many teams. Each team has many Google Cloud projects. Your organization wants to simplify the management of identity and access policies for these projects.
How can you group these projects to meet this goal?
- A. Use folders to group each team's projects
- B. Group each team's projects into a separate organization node
- C. Assign labels based on the virtual machines that are part of each team's projects
- D. Group each team's projects into a separate domain
Answer: A
NEW QUESTION # 71
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 Bare Metal Solution
- B. Migrate the workloads to Google Kubernetes Engine
- C. Migrate the workloads to Google Cloud VMware Engine
- D. Migrate the workloads to Compute Engine
Answer: D
Explanation:
Explanation
Migrate for Compute Engine enables you to lift and shift workloads at scale to Google Cloud Compute Engine with minimal changes and risk.
Reference: https://dataintegration.info/simplify-vm-migrations-with-migrate-for-compute-engine-as-a-service
NEW QUESTION # 72
What is a benefit of Google's purpose-built servers compared to standard servers?
- A. They are optimized for specific tasks making them more efficient.
- B. They are cheaper to build than standard servers.
- C. They are backward compatible with legacy disk drives.
- D. They run software that cannot be deployed on standard servers.
Answer: A
Explanation:
Google's purpose-built servers are specifically designed and optimized for Google's workloads, making them more efficient than standard off-the-shelf servers. These servers are tailored for specific tasks, such as handling large-scale data processing or machine learning workloads, providing better performance and energy efficiency.
Option C: They are optimized for specific tasks making them more efficient is correct because it describes the primary benefit of Google's custom server hardware.
Reference:
Google Cloud Data Center Innovation: Purpose-built Servers
Google Cloud Infrastructure: Hardware and Software Optimization
NEW QUESTION # 73
An organization has an ecommerce application that requires a cost-effective, transactional database. The application will only serve customers in a single region. Which service should they use?
- A. BigQuery
- B. Cloud Spanner
- C. Cloud SQL
- D. Cloud Bigtable
Answer: C
Explanation:
The correct answer is C. Cloud SQL. Here's why:
Context of the Questio n : The organization needs a cost-effective, transactional database for an e-commerce application serving customers in a single region. A transactional database is needed to handle online transactions with ACID (Atomicity, Consistency, Isolation, Durability) properties.
Google Cloud Product Relevance:
Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server. It is cost-effective, supports transactional workloads, and is suitable for applications with regional data residency requirements. This makes it ideal for an e-commerce application that only serves customers in a single region.
Why Not Other Options:
A . Cloud Spanner: Cloud Spanner is a globally distributed, strongly consistent database designed for large-scale, mission-critical applications. It is more expensive and better suited for applications requiring global distribution and high availability across multiple regions.
B . Cloud Bigtable: Cloud Bigtable is a NoSQL wide-column database designed for low-latency, high-throughput workloads, such as analytics. It does not support ACID transactions, making it unsuitable for transactional workloads like an e-commerce application.
D . BigQuery: BigQuery is a fully managed data warehouse designed for large-scale analytics. It is not suitable for transactional database needs.
Google Cloud Digital Leader Reference:
For more details on Cloud SQL, refer to the Cloud SQL documentation in the Google Cloud platform.
NEW QUESTION # 74
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