Free Google Professional-Cloud-Database-Engineer Exam Dumps Questions & Answers

Your application uses Cloud SQL for MySQL. Your users run reports on data that relies on near- real time; however, the additional analytics caused excessive load on the primary database. You created a read replica for the analytics workloads, but now your users are complaining about the lag in data changes and that their reports are still slow. You need to improve the report performance and shorten the lag in data replication without making changes to the current reports. Which two approaches should you implement? (Choose two.)

• A.  Disable replication on the primary instance, and set the flag for parallel replication on the primary instance. Re-enable replication and optimize performance by setting flags on the read replica.
• B.  Disable replication on the read replica, and set the flag for parallel replication on the read replica.
  Re-enable replication and optimize performance by setting flags on the primary instance.
• C.  Create secondary indexes on the replica.
• D.  Create additional read replicas, and partition your analytics users to use different read replicas.
• E.  Move your analytics workloads to BigQuery, and set up a streaming pipeline to move data and update BigQuery.

You recently launched a new product to the US market. You currently have two Bigtable clusters in one US region to serve all the traffic. Your marketing team is planning an immediate expansion to APAC. You need to roll out the regional expansion while implementing high availability according to Google-recommended practices. What should you do?

• A.  Maintain a target of 35% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone australia-southeast1-a
  cluster-c in zone europe-west1-d
  cluster-d in zone asia-east1-b
• B.  Maintain a target of 35% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone us-central2-a
  cluster-c in zone asia-northeast1-b
  cluster-d in zone asia-east1-b
• C.  Maintain a target of 23% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone europe-west1-d
  cluster-c in zone asia-east1-b
• D.  Maintain a target of 23% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone us-central1-b
  cluster-c in zone us-east1-a

Your company uses Cloud Spanner for a mission-critical inventory management system that is globally available. You recently loaded stock keeping unit (SKU) and product catalog data from a company acquisition and observed hotspots in the Cloud Spanner database. You want to follow Google-recommended schema design practices to avoid performance degradation. What should you do? (Choose two.)

• A.  Normalize the data model.
• B.  Promote low-cardinality attributes in multi-attribute primary keys.
• C.  Use an auto-incrementing value as the primary key.
• D.  Use bit-reverse sequential value as the primary key.
• E.  Promote high-cardinality attributes in multi-attribute primary keys.

You need to redesign the architecture of an application that currently uses Cloud SQL for PostgreSQL. The users of the application complain about slow query response times. You want to enhance your application architecture to offer sub-millisecond query latency. What should you do?

• A.  Configure Cloud SQL for PostgreSQL read replicas to offload queries.
• B.  Configure Bigtable, and modify your application to offload queries.
• C.  Configure Firestore, and modify your application to offload queries.
• D.  Configure Memorystore, and modify your application to offload queries.

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-

• A.  Upgrade the Cloud SQL for PostgreSQL instance to db-n1-highmem-32and implement connection pooling with PgBouncerto handle increased load.
• B.  Upgrade to Cloud SAL for PostgreSQL Enterprise Edition and route transaction and analytical queries to the primary instance.
• C.  Migrate to AlloyDB for PostgreSQL with a 16 vCPU primary instance and enable columnar engine for analytical workloads, using read pools for query isolation.
• D.  Migrate to AlloyDB for PostgreSQL and upgrade the machine type of the primary instance to 32 vCPU. Create materialized views for common analytical queries.