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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Professional Cloud Database Engineer Exam |
| Exam Number: | Professional-Cloud-Database-Engineer |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer Google Cloud Certified - Professional Cloud Architect |
| Exam Price: | $200 USD (plus tax where applicable) |
| Exam Format: | Multiple select, Multiple choice |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Japanese |
| Passing Score: | Not publicly disclosed |
| Real Exam Qty: | 50-60 |
| Recommended Training: | Google Cloud Professional Cloud Database Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Cloud-Database-Engineer Sample Questions |
| Exam Way: | Online proctored (remote) or onsite proctored (testing center) |
| Pre Condition: | No mandatory prerequisites; recommended: 5+ years of overall database and IT experience, including 2 years of hands-on experience with Google Cloud database solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/cloud-database-engineer |
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Google Cloud Certified - Professional Cloud Database Engineer certification exam is a credential offered by Google to validate the skills and expertise of professionals who design and manage databases on Google Cloud. Google Cloud Certified - Professional Cloud Database Engineer certification exam is designed for individuals who have experience in designing, implementing, and managing databases on Google Cloud Platform. Professional-Cloud-Database-Engineer Exam Tests the candidate's knowledge of database concepts, cloud-native database services, database design and implementation, automation, and security.
NEW QUESTION # 31
You have a Cloud SQL for MySQL instance with a table of product information including a column of product descriptions. Your application development team is building a customer facing chatbot and would like to find the product that most closely matches a freeform text description provided by the customer. How should you enable this functionality-
Answer: B
Explanation:
By precomputing embeddings for each product description and storing them in your table, you can turn a freeform customer query into an embedding and then run an approximate nearest- neighbor search to find the semantically closest products. This approach delivers much more relevant matches than regex, LIKE, or SOUNDEX without overloading the database with expensive text-processing on every query.
NEW QUESTION # 32
You are migrating an on-premises database to Spanner. There are a few tables, each with a few hundred records that do not have a primary key on the source database. You need to migrate all of the tables over to the news database while avoiding hot-spotting issues. What should you do-
Answer: C
Explanation:
Using GENERATE_UUID() creates random, uniformly distributed primary keys, preventing hot- spotting in Spanner during migration.
NEW QUESTION # 33
Your application stores session documents in Firestore and queries them by steps, sessionDuration, and energyBurned. After a 30 day test period, latency is acceptable but costs are high. Which Firestore change would reduce cost while preserving performance and availability?
Answer: A
Explanation:
Firestore automatically creates single field indexes for most fields which means each document write updates multiple index entries and drives up write and storage costs. By trimming indexes to only the ones required by your queries on steps, sessionDuration and energyBurned you reduce index write amplification and index storage while preserving the performance of the queries you care about. This is a configuration change that does not reduce availability because Firestore remains fully managed and you keep the essential indexes for your filters and sort orders.
You can delete unused composite indexes and add single field index exemptions for attributes that are not part of your supported query patterns. Monitor index usage and keep only the minimal set that powers production queries so you sustain acceptable latency while lowering cost.
NEW QUESTION # 34
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: C
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 # 35
Your organization works with sensitive data that requires you to manage your own encryption keys. You are working on a project that stores that data in a Cloud SQL database. You need to ensure that stored data is encrypted with your keys. What should you do?
Answer: B
NEW QUESTION # 36
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