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Google Professional-Cloud-Database-Engineer Exam Syllabus Topics:

SectionObjectives
Manage a database solution spanning multiple systems- Operational database management
  • 1. Backup and restore strategies
    • 2. Capacity planning and scaling
      • 3. Monitoring and performance tuning
        Design scalable and highly available database solutions- Select appropriate database services (relational, NoSQL, analytical)
        • 1. Bigtable use cases and schema design
          • 2. Cloud SQL vs AlloyDB vs Spanner selection criteria
            • 3. Firestore and document database patterns
              - Design for high availability and disaster recovery
              • 1. Replication strategies and failover design
                • 2. Regional vs multi-regional architectures
                  Migrate data solutions to Google Cloud- Database migration strategies
                  • 1. Lift-and-shift vs re-platforming
                    • 2. Online vs offline migration tools (e.g., Database Migration Service)
                      Ensure reliability, performance, and optimization- Security and compliance
                      • 1. Encryption at rest and in transit
                        • 2. IAM roles and access control
                          - Performance optimization
                          • 1. Indexing, query optimization, and caching strategies
                            • 2. Workload tuning across database services

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                              Google Cloud Certified - Professional Cloud Database Engineer 認定 Professional-Cloud-Database-Engineer 試験問題 (Q12-Q17):

                              質問 # 12
                              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-

                              正解:A

                              解説:
                              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.


                              質問 # 13
                              You need to migrate existing databases from Microsoft SQL Server 2016 Standard Edition on a single Windows Server 2019 Datacenter Edition to a single Cloud SQL for SQL Server instance.
                              During the discovery phase of your project, you notice that your on-premises server peaks at around 25,000 read IOPS. You need to ensure that your Cloud SQL instance is sized appropriately to maximize read performance. What should you do?

                              正解:D

                              解説:
                              https://cloud.google.com/compute/docs/disks/performance


                              質問 # 14
                              You need to issue a new server certificate because your old one is expiring. You need to avoid a restart of your Cloud SQL for MySQL instance. What should you do in your Cloud SQL instance?

                              正解:A

                              解説:
                              https://cloud.google.com/sql/docs/mysql/manage-ssl-instance#rotate


                              質問 # 15
                              You are designing a database strategy for a new web application. You plan to start with a small pilot in one country and eventually expand to millions of users in a global audience. You need to ensure that the application can run 24/7 with minimal downtime for maintenance. What should you do?

                              正解:B

                              解説:
                              https://docs.google.com/forms/d/e/1FAIpQLSfZ77ZnuUL0NpU-bOtO5QUkC0cnRCe5YKMiubLXwfV3abBqkg/viewform


                              質問 # 16
                              You want to migrate an existing on-premises application to Google Cloud. Your application supports semi-structured data ingested from 100,000 sensors, and each sensor sends 10 readings per second from manufacturing plants. You need to make this data available for real-time monitoring and analysis. What should you do?

                              正解:B

                              解説:
                              Bigtable is a scalable, fully managed, and high-performance NoSQL database service that can handle semi-structured data and support real-time monitoring and analysis. Cloud SQL is a relational database service that does not support semi-structured dat a. BigQuery is a data warehouse service that is optimized for batch processing and analytics, not real-time monitoring. Cloud Spanner is a relational database service that supports semi-structured data with JSON data type, but it is more expensive and complex than Bigtable for this use case.


                              質問 # 17
                              ......

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