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Google Professional-Cloud-Database-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified - Professional Cloud Database Engineer
Exam Number:	Professional-Cloud-Database-Engineer
Exam Price:	$200 USD
Related Certifications:	Google Cloud Certified - Professional Data Engineer Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Associate Cloud Engineer
Available Languages:	Japanese, English
Exam Duration:	120 minutes
Passing Score:	Not officially disclosed
Certificate Validity Period:	2 years
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	50-60
Sample Questions:	Google Professional-Cloud-Database-Engineer Sample Questions
Exam Way:	Online-proctored or onsite-proctored at a testing center
Pre Condition:	No formal prerequisites. Google recommends hands-on experience with Google Cloud database services and cloud architecture.
Official Syllabus URL:	https://cloud.google.com/learn/certification/cloud-database-engineer

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Google Professional-Cloud-Database-Engineer exam is a certification exam designed to test the skills and knowledge of database engineers who work with Google Cloud Platform (GCP). Professional-Cloud-Database-Engineer Exam is part of Google's Cloud Certified program, which offers certification for various roles in cloud computing.

Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q95-Q100):

NEW QUESTION # 95
Your project is using Bigtable to store data that should not be accessed from the public internet under any circumstances, even if the requestor has a valid service account key. You need to secure access to this dat a. What should you do?

A. Use Identity and Access Management (IAM) for Bigtable access control.
B. Use Google Cloud Armor to add IP addresses to an allowlist.
C. Use customer-managed encryption keys (CMEK).
D. Use VPC Service Controls to create a trusted network for the Bigtable service.

Answer: D

NEW QUESTION # 96
Your team uses thousands of connected IoT devices to collect device maintenance data for your oil and gas customers in real time. You want to design inspection routines, device repair, and replacement schedules based on insights gathered from the data produced by these devices.
You need a managed solution that is highly scalable, supports a multi-cloud strategy, and offers low latency for these IoT devices. What should you do?

A. Use Bigtable with Looker.
B. Use Firestore with Looker.
C. Use Cloud Spanner with Data Studio.
D. Use MongoD8 Atlas with Charts.

Answer: D

NEW QUESTION # 97
You work for a financial services company that wants to use fully managed database services. Traffic volume for your consumer services products has increased annually at a constant rate with occasional spikes around holidays. You frequently need to upgrade the capacity of your database. You want to use Cloud Spanner and include an automated method to increase your hardware capacity to support a higher level of concurrency. What should you do?

A. Set up alerts that are triggered when Cloud Spanner utilization metrics breach the threshold, and then schedule an upgrade during the scheduled maintenance window.
B. Use linear scaling to implement the Autoscaler-based architecture
C. Upgrade the Cloud Spanner instance on a periodic basis during the scheduled maintenance window.
D. Use direct scaling to implement the Autoscaler-based architecture.

Answer: C

NEW QUESTION # 98
Which Google Cloud pairing offers a fully managed MongoDB service with live migration that supports continuous schema changes, data validation, rapid rollback, and cutover in under 20 minutes?

A. Database Migration Service and AlloyDB for PostgreSQL
B. Cloud Dataflow with MongoDB connector and Firestore in Native mode
C. MongoDB Atlas Live Migration and MongoDB Atlas on Google Cloud

Answer: C

Explanation:
MongoDB Atlas provides a fully managed MongoDB service on Google Cloud with automated operations, scaling, and high availability. Atlas Live Migration performs an initial sync then continuously replicates changes from the source, which allows you to keep applications online while you validate data and adjust schemas. It supports data validation and enables a quick cutover with the ability to roll back by directing traffic back to the source if needed.

NEW QUESTION # 99
You have an application that performs numerous reads and writes all day running in a single Bigtable cluster. A new batch analytics job needs to read the same data and you want to make sure that the new job does not affect the existing workload. What should you do-

A. Scale up the Bigtable instance by manually adding more nodes.
B. Add a new cluster to the existing instance, and isolate the workloads with two app profiles.
C. Add a new cluster to the existing instance, and enable multi-cluster routing in the app profile.
D. Scale up the Bigtable instance by enabling Autoscaling.

Answer: B

Explanation:
This is the only way to isolate your analytical reads from the production workload-by adding a second cluster in the same Bigtable instance and creating two app profiles (one bound to the primary cluster for OLTP, and one bound to the new cluster for analytics). This keeps the batch job's heavy scans off your production nodes, while still reading the same underlying data.

NEW QUESTION # 100
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