Free Professional-Cloud-Database-Engineer Exam Dumps

Question 6

Your team recently released a new version of a highly consumed application to accommodate additional user traffic. Shortly after the release, you received an alert from your production monitoring team that there is consistently high replication lag between your primary instance and the read replicas of your Cloud SQL for MySQL instances. You need to resolve the replication lag. What should you do?

Correct Answer:A
https://cloud.google.com/sql/docs/mysql/replication/replication-lag#optimize_queries_and_schema

Question 7

Your company is developing a new global transactional application that must be ACID- compliant and have 99.999% availability. You are responsible for selecting the appropriate Google Cloud database to serve as a datastore for this new application. What should you do?

Correct Answer:B

Question 8

You are designing for a write-heavy application. During testing, you discover that the write workloads are performant in a regional Cloud Spanner instance but slow down by an order of magnitude in a multi-regional instance. You want to make the write workloads faster in a multi-regional instance. What should you do?

Correct Answer:A
https://cloud.google.com/spanner/docs/instance-configurations#multi-region- best-practices Best practices For optimal performance, follow these best practices: Design a schema that prevents hotspots and other performance issues. For optimal write latency, place compute resources for write-heavy workloads within or close to the default leader region. For optimal read performance outside of the default leader region, use staleness of at least 15 seconds. To avoid single-region dependency for your workloads, place critical compute resources in at least two regions. A good option is to place them next to the two different read-write regions so that any single region outage will not impact all of your application. Provision enough compute capacity to keep high priority total CPU utilization under 45% in each region.

Question 9

You want to migrate an on-premises mission-critical PostgreSQL database to Cloud SQL.
The database must be able to withstand a zonal failure with less than five minutes of downtime and still not lose any transactions. You want to follow Google-recommended practices for the migration. What should you do?

Correct Answer:D

Question 10

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 hot-spots in the Cloud Spanner database. You want to follow Google-recommended schema design practices to avoid performance degradation. What should you do? (Choose two.)

Correct Answer:DE
https://cloud.google.com/spanner/docs/schema-design D because high cardinality means you have more unique values in the collumn. That's a good thing for a hot-spotting issue. E because Spanner specifically has this feature to reduce hot spotting. Basically, it generates unique values https://cloud.google.com/spanner/docs/schema- design#bit_reverse_primary_key
* D. Promote high-cardinality attributes in multi-attribute primary keys.
This is a correct answer because promoting high-cardinality attributes in multi-attribute primary keys can help avoid hotspots in Cloud Spanner. High-cardinality attributes are those that have many distinct values, such as UUIDs, email addresses, or timestamps1. By placing high-cardinality attributes first in the primary key, you can ensure that the rows are distributed more evenly across the key space, and avoid having too many requests sent to the same server2.
* E. Use bit-reverse sequential value as the primary key.
This is a correct answer because using bit-reverse sequential value as the primary key can help avoid hotspots in Cloud Spanner. Bit-reverse sequential value is a technique that reverses the bits of a monotonically increasing value, such as a timestamp or an auto- incrementing ID1. By reversing the bits, you can create a pseudo-random value that spreads the writes across the key space, and avoid having all the inserts occurring at the end of the table2.