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Google Cloud Certified - Professional Cloud Database Engineer certification is an excellent way for IT professionals to demonstrate their expertise in cloud-based database management. It is a valuable credential for individuals seeking career advancement opportunities in the field of cloud computing. With this certification, individuals can demonstrate their knowledge and skills to potential employers and clients, making them stand out in a competitive job market.

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It is similar to the Professional-Cloud-Database-Engineer desktop-based software, with all the elements of the desktop practice exam. This Professional-Cloud-Database-Engineer exam can be accessed from any browser and does not require installation. The Professional-Cloud-Database-Engineer questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based Professional-Cloud-Database-Engineer Practice Test immediately through any operating system and browsers.

Google Professional-Cloud-Database-Engineer is a certification exam designed for professionals who want to showcase their expertise in designing, building, and managing databases on Google Cloud Platform. Professional-Cloud-Database-Engineer exam is intended for individuals who have a deep understanding of database management, data migration, backup and recovery, and security on Google Cloud Platform. Successful completion of Professional-Cloud-Database-Engineer Exam will earn candidates the Google Cloud Certified - Professional Cloud Database Engineer certification.

Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q43-Q48):

NEW QUESTION # 43
You manage a production MySQL database running on Cloud SQL at a retail company. You perform routine maintenance on Sunday at midnight when traffic is slow, but you want to skip routine maintenance during the year-end holiday shopping season. You need to ensure that your production system is available 24/7 during the holidays. What should you do?

A. Define a maintenance window on Sundays between 12 AM and 5 AM, and deny maintenance periods between November 1 and February 15.
B. Create a Cloud Scheduler job to start maintenance at 12 AM on Sundays. Pause the Cloud Scheduler job between November 1 and January 15.
C. Build a Cloud Composer job to start a maintenance window on Sundays between 12 AM and 1AM, and deny maintenance periods between November 1 and January 15.
D. Define a maintenance window on Sundays between 12 AM and 1 AM, and deny maintenance periods between November 1 and January 15.

Answer: D

Explanation:
"Deny maintenance period. A block of days in which Cloud SQL does not schedule maintenance. Deny maintenance periods can be up to 90 days long. " https://cloud.google.com/sql/docs/mysql/maintenance

NEW QUESTION # 44
You run a toy sales ecommerce application on Spanner. The application contains details about the various product listings such as name, category, description, and review(s). Your leadership wants to enable customers to ask free-form questions about the product catalog. You want to implement this functionality in the easiest, quickest, and least expensive way possible.
What should you do?

A. Register an existing Generative AI Vertex model in Spanner. Use the ML.PREDICTfunction to supply the model with a user query along with the product information in a SQL query. Return the response back to the application.
B. Generate vector embeddings for product information in Spanner. Find the vector with the smallest distance between the users' query and existing product information or review by using the EUCLIDEAN_DISTANCE()function in Spanner. Return the closest product information or review.
C. Create, train and deploy a custom ML model(s). Fetch toy details from Spanner and invoke ML predictions from the application layer to return the response.
D. Create a Spanner full-text search index. Find the product information or review against the user's question in a SQL query. Return the response back to the application.

Answer: A

Explanation:
Registering an existing Vertex AI generative model in Spanner and calling it with ML.PREDICT lets you implement free-form Q&A directly from SQL without building custom ML pipelines, generating/maintaining embeddings, or deploying your own model - making it the quickest and lowest-effort approach while keeping the solution cost-effective by reusing a managed model.

NEW QUESTION # 45
Your company uses the Cloud SQL out-of-disk recommender to analyze the storage utilization trends of production databases over the last 30 days. Your database operations team uses these recommendations to proactively monitor storage utilization and implement corrective actions. You receive a recommendation that the instance is likely to run out of disk space. What should you do to address this storage alert?

A. Compress the data using a different compression algorithm.
B. Manually or automatically increase the storage capacity.
C. Create another schema to load older data.
D. Normalize the database to the third normal form.

Answer: B

Explanation:
https://cloud.google.com/sql/docs/mysql/instance-settings#storage-capacity-2ndgen

NEW QUESTION # 46
You released a popular mobile game and are using a 50 TB Cloud Spanner instance to store game data in a PITR-enabled production environment. When you analyzed the game statistics, you realized that some players are exploiting a loophole to gather more points to get on the leaderboard. Another DBA accidentally ran an emergency bugfix script that corrupted some of the data in the production environment. You need to determine the extent of the data corruption and restore the production environment. What should you do?
(Choose two.)

A. If the corruption is significant, use import and export.
B. If the corruption is significant, perform a stale read and specify a recovery timestamp. Write the results back.
C. If the corruption is significant, use backup and restore, and specify a recovery timestamp.
D. If the corruption is insignificant, perform a stale read and specify a recovery timestamp. Write the results back.
E. If the corruption is insignificant, use backup and restore, and specify a recovery timestamp.

Answer: C,D

Explanation:
https://cloud.google.com/spanner/docs/pitr#ways-to-recover
To recover the entire database, backup or export the database specifying a timestamp in the past and then restore or import it to a new database. This is typically used to recover from data corruption issues when you have to revert the entire database to a point-in-time before the corruption occurred.
This part describes significant corruption - A
To recover a portion of the database, perform a stale read specifying a query-condition and timestamp in the past, and then write the results back into the live database. This is typically used for surgical operations on a live database. For example, if you accidentally delete a particular row or incorrectly update a subset of data, you can recover it with this method.
This describes insignificant corruption case - E
https://cloud.google.com/spanner/docs/pitrhttps://cloud.google.com/spanner/docs/backup/restore-backup

NEW QUESTION # 47
You are running a large, highly transactional application on Oracle Real Application Cluster (RAC) that is multi-tenant and uses shared storage. You need a solution that ensures high- performance throughput and a low-latency connection between applications and databases. The solution must also support existing Oracle features and provide ease of migration to Google Cloud. What should you do?

A. Migrate to Google Cloud VMware Engine
B. Migrate to Compute Engine.
C. Migrate to Google Kubernetes Engine (GKE)
D. Migrate to Bare Metal Solution for Oracle.

Answer: D

NEW QUESTION # 48
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