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| Section | Weight | Objectives |
|---|---|---|
| Deploy scalable and highly available databases in Google Cloud | 20% | - Configure monitoring and alerting
|
| Migrate data solutions | 14% | - Design migration strategies
|
| Manage a solution that spans multiple database technologies | 25% | - Determine connectivity and access management
|
| Design innovative, scalable, and highly available cloud database solutions | 32% | - Design for backup and recovery
|
>> Professional-Cloud-Database-Engineer Actual Questions <<
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NEW QUESTION # 24
You are using Compute Engine on Google Cloud and your data center to manage a set of MySQL databases in a hybrid configuration. You need to create replicas to scale reads and to offload part of the management operation. What should you do?
Answer: C
Explanation:
https://cloud.google.com/sql/docs/mysql/replication/external-server
NEW QUESTION # 25
You are deploying a Cloud SQL for MySQL database to serve a non-critical application. The database size is 10 GB and will be updated every night with data stored in a Cloud Storage bucket. The database serves read-only traffic from the application during the day. The data locality requirement of this application mandates that data must reside in a single region. You want to minimize the cost of running this database while maintaining an RTO of 1 day. What should you do-
Answer: A
Explanation:
Chosen option is the best approach:
* Single-region data locality: By pointing your automated backups at a region-scoped Cloud- Storage bucket, you ensure all data (including backups) stays in-region.
* RTO ≤-1-day: Automated daily backups let you restore from the most recent snapshot within your 24-hour RTO window.
* Cost minimized: Disabling HA and not spinning up any replicas avoids the extra instance costs.
You pay only for one primary instance plus your small storage charges for ~10-GB of backups.
* Simplicity: Cloud-SQL's built-in automated backups require zero operational overhead beyond the one-time bucket configuration.
NEW QUESTION # 26
Your e-learning platform runs on a Cloud SQL for PostgreSQL instance (16 VCPUs, 60 GB memory and 1TB SSD) serving users in North America. Your analytics team runs complex reporting queries that often consume 80% of CPU resources, causing slow response times for student transactions during peak hours. Current workload includes 8,000 transactions per second with 60% reads and 40% writes. The reporting queries involve JOIN operations across multiple large tables with millions of rows requiring highly efficient analytical processing. The platform also experiences sudden spikes in analytical reporting demand, requiring an elastic scaling of read capacity. You need to improve the query performance for your analytics team to run their reports efficiently without impacting transactional users. You also need to plan for future traffic growth.
What should you do-
Answer: A
Explanation:
Migrating to AlloyDB for PostgreSQL with the columnar engine is the best option for this mixed workload. The columnar engine accelerates analytical queries such as multi-table JOINs on large datasets, while the primary instance continues handling transactional traffic. Using read pools isolates analytical reporting from transactional operations and provides elastic scaling to handle sudden spikes in reporting demand. This ensures efficient reporting without degrading transactional performance and supports future growth.
NEW QUESTION # 27
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?
Answer: B
Explanation:
This scenario has BigTable written all over it - large amounts of data from many devices to be analysed in realtime. I would even argue it could qualify as a multicloud solution, given the links to HBASE. BUT it does not support SQL queries and is not therefore compatible (on its own) with Looker. Firestore + Looker has the same problem. Spanner + Data Studio is at least a compatible pairing, but I agree with others that it doesn't fit this use-case - not least because it's Google-native. By contrast, MongoDB Atlas is a managed solution (just not by Google) which is compatible with the proposed reporting tool (Mongo's own Charts), it's specifically designed for this type of solution and of course it can run on any cloud.
NEW QUESTION # 28
You are creating new Cloud SQL for PostgreSQL instances in us-east1and us-east4. You are assigning private IP addresses to the Cloud SQL instances starting with us-east1so you can reach the instance without going through the internet or using external IP addresses.
However, you keep getting a "Network association failed" error message. You are using a shared VPC network. What should you do to resolve the issue-
Answer: A
Explanation:
To use private IP addresses with Cloud SQL, you must first configure Private Services Access between your VPC network and Google-managed services. This step creates the necessary allocated IP range for Google services and establishes the required peering connection. Without it, assigning a private IP will fail with a "Network association failed" error.
NEW QUESTION # 29
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