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

Section	Weight	Objectives
Migrate data solutions	14%	- Implement migration and replication 1. Migration tool selection 2. Data replication methods 3. Schema conversion - Validate and cut over migration 1. Cutover procedures 2. Data integrity verification - Design migration strategies 1. Fall-back planning 2. Downtime requirements analysis
Manage a solution that spans multiple database technologies	25%	- Determine connectivity and access management 1. IAM policies and permissions 2. User authentication and authorization - Configure monitoring and troubleshooting 1. Troubleshoot common issues 2. Query optimization 3. Performance monitoring - Optimize cost and performance 1. Scaling strategies 2. Automate operational tasks 3. Cost optimization - Manage database operations and maintenance 1. Data lifecycle management 2. Index management 3. Updates and upgrades
Design innovative, scalable, and highly available cloud database solutions	32%	- Design for high availability and reliability 1. High availability configurations 2. Disaster recovery planning 3. Replication strategies - Design for security and compliance 1. Data encryption 2. Compliance requirements 3. Access control and IAM - Analyze variables for capacity and usage planning 1. Size compute and storage resources 2. Evaluate performance and cost tradeoffs 3. Solution sizing based on workload and requirements - Design for backup and recovery 1. Data import/export planning 2. RTO and RPO design 3. Backup strategies
Deploy scalable and highly available databases in Google Cloud	20%	- Implement high availability solutions 1. Provision HA configurations 2. Multi-region replication 3. Read replicas deployment - Configure monitoring and alerting 1. Health checks and alerts 2. Performance dashboards - Automate deployment and scaling 1. Auto-scaling setup 2. Infrastructure as Code

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Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q117-Q122):

NEW QUESTION # 117
You are running a Cloud SQL for PostgreSQL 13 Enterprise Edition instance. During an audit, you discovered that the write-ahead logs used for point-in-time recovery (PITR) are stored on disk. You need to store PITR logs in a Cloud Storage bucket going forward. How should you do this without compromising recoverability or losing the current PITR logs-

A. Change the transaction logs (WAL) retention period.
B. Disable PITR, then enable PITR.
C. Clone the instance. Create a new instance with PITR retention set to 30 days.
D. Upgrade to Enterprise Plus Edition.

Answer: D

Explanation:
You should upgrade your instance to Enterprise-Plus - the in-place upgrade will automatically switch your WAL archival from local disk to Cloud Storage (for any Enterprise-edition instance that had PITR enabled before Jan-9,-2023) without downtime or losing existing logs.

NEW QUESTION # 118
You work in the logistics department. Your data analysis team needs daily extracts from Cloud SQL for MySQL to train a machine learning model. The model will be used to optimize next-day routes. You need to export the data in CSV format. You want to follow Google-recommended practices. What should you do?

A. Use Cloud Scheduler to trigger a Cloud Function that will run a select * from table(s) query to call the cloudsql.instances.export API.
B. Use Cloud Scheduler to trigger a Cloud Function through Pub/Sub to call the cloudsql.instances.export API.
C. Use Cloud Composer to orchestrate an export by calling the cloudsql.instances.export API.
D. Use Cloud Composer to execute a select * from table(s) query and export results.

Answer: B

Explanation:
https://cloud.google.com/blog/topics/developers-practitioners/scheduling-cloud-sql-exports-using-cloud-functions-and-cloud-scheduler

NEW QUESTION # 119
You are managing two different applications: Order Management and Sales Reporting. Both applications interact with the same Cloud SQL for MySQL database. The Order Management application reads and writes to the database 24/7, but the Sales Reporting application is read-only. Both applications need the latest dat a. You need to ensure that the Performance of the Order Management application is not affected by the Sales Reporting application. What should you do?

A. Create two separate databases in the instance, and perform dual writes from the Order Management application.
B. Queue up all the requested reports in PubSub, and execute the reports at night.
C. Create a read replica for the Sales Reporting application.
D. Use a Cloud SQL federated query for the Sales Reporting application.

Answer: C

NEW QUESTION # 120
You work at a retail company using Cloud SQL for MySQL to store transactional data. You need to combine the transactional data with customer behavior information from BigQuery to make real-time sales predictions using machine learning. You want to accomplish this without incurring the overhead costs of moving the data out of their respective data stores.
What should you do?

A. Use Datastream to build ETL/ELT pipelines to move data from Cloud SQL to BigQuery. Use BigQuery ML for predictions.
B. Use BigQuery ML for predictions, and combine federated queries to Cloud SQL.
C. Extract data from BigQuery into Cloud SQL, and use Vertex AI to generate predictions.
D. Refactor the application to simultaneously write data to both MySQL and BigQuery. Use BigQuery to generate predictions.

Answer: B

Explanation:
Using BigQuery ML lets you train and run predictions where the customer behavior data already resides, and federated queries to Cloud SQL allow you to join transactional MySQL data with BigQuery data at query time without building ETL pipelines or duplicating data, minimizing overhead and data movement.

NEW QUESTION # 121
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. Add a new cluster to the existing instance, and enable multi-cluster routing in the app profile.
B. Add a new cluster to the existing instance, and isolate the workloads with two app profiles.
C. Scale up the Bigtable instance by enabling Autoscaling.
D. Scale up the Bigtable instance by manually adding more nodes.

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 # 122
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