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The Google Cloud Certified - Professional Cloud Database Engineer certification exam consists of multiple-choice questions that test the candidate's knowledge in various areas such as database design, data modeling, database management, data security, and data migration. Professional-Cloud-Database-Engineer Exam also covers the use of Google Cloud Platform products such as Cloud SQL, Cloud Spanner, and Cloud Datastore.

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In order to earn the certification, candidates must pass a rigorous exam that consists of multiple-choice and scenario-based questions. They must demonstrate their ability to design, develop, and manage databases on the Google Cloud Platform, as well as their proficiency in using various Google Cloud database services. Once they have earned the certification, they must maintain it through ongoing professional development and continuing education.

Google Professional-Cloud-Database-Engineer Certification Exam is a valuable credential for professionals who want to demonstrate their expertise in database development and management on the Google Cloud Platform. Google Cloud Certified - Professional Cloud Database Engineer certification provides a competitive edge to candidates in the job market and helps them stand out among other candidates.

Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q218-Q223):

NEW QUESTION # 218
Your organization stores marketing data such as customer preferences and purchase history on Bigtable. The consumers of this database are predominantly data analysts and operations users. You receive a service ticket from the database operations department citing poor database performance between 9 AM-10 AM every day.
Theapplication team has confirmed no latency from their logs. A new cohort of pilot users that is testing a dataset loaded from a third-party data provider is experiencing poor database performance. Other users are not affected. You need to troubleshoot the issue. What should you do?

A. Check the Cloud Monitoring table/bytes_used metric from Bigtable.
B. Isolate the data analysts and operations user groups to use different Bigtable instances.
C. Add more nodes to the Bigtable cluster.
D. Use Key Visualizer for Bigtable.

Answer: D

Explanation:
https://cloud.google.com/bigtable/docs/performance#troubleshooting

NEW QUESTION # 219
Your organization has a security policy to ensure that all Cloud SQL for PostgreSQL databases are secure.
You want to protect sensitive data by using a key that meets specific locality or residency requirements. Your organization needs to control the key's lifecycle activities. You need to ensure that data is encrypted at rest and in transit. What should you do?

A. Create the database persistent disk with customer-managed encryption keys.
B. Create the database with customer-managed encryption keys.
C. Create the database with Google-managed encryption keys.
D. Create the database persistent disk with Google-managed encryption keys.

Answer: B

Explanation:
https://cloud.google.com/sql/docs/postgres/configure-cmek#createcmekinstance

NEW QUESTION # 220
Your ecommerce website captures user clickstream data to analyze customer traffic patterns in real time and support personalization features on your website. You plan to analyze this data using big data tools. You need a low-latency solution that can store 8 TB of data and can scale to millions of read and write requests per second. What should you do?

A. Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.
B. Deploy a Cloud SQL environment with read replicas for improved performance. Use Datastream to export data to Cloud Storage and analyze with Dataproc and the Cloud Storage connector.
C. Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
D. Use Memorystore to handle your low-latency requirements and for real-time analytics.

Answer: C

Explanation:
Start with the lowest tier and smallest size and then grow your instance as needed. Memorystore provides automated scaling using APIs, and optimized node placement across zones for redundancy. Memorystore for Memcached can support clusters as large as 5 TB, enabling millions of QPS at very low latency

NEW QUESTION # 221
Your organization has a production Cloud SQL for MySQL instance. Your instance is configured with 16 vCPUs and 104 GB of RAM that is running between 90% and 100% CPU utilization for most of the day. You need to scale up the database and add vCPUs with minimal interruption and effort. What should you do?

A. Update a MySQL database flag to increase the number of vCPUs.
B. Back up the database, create an instance with additional vCPUs, and restore the database.
C. Issue a gcloud sql instances patch command to increase the number of vCPUs.
D. Issue a gcloud compute instances update command to increase the number of vCPUs.

Answer: C

Explanation:
https://cloud.google.com/sdk/gcloud/reference/sql/instances/patch

NEW QUESTION # 222
You work for a financial services company that wants to use fully managed database services.
Traffic volume for your consumer services products has increased annually at a constant rate with occasional spikes around holidays. You frequently need to upgrade the capacity of your database. You want to use Cloud Spanner and include an automated method to increase your hardware capacity to support a higher level of concurrency. What should you do?

A. Use direct scaling to implement the Autoscaler-based architecture.
B. Upgrade the Cloud Spanner instance on a periodic basis during the scheduled maintenance window.
C. Use linear scaling to implement the Autoscaler-based architecture
D. Set up alerts that are triggered when Cloud Spanner utilization metrics breach the threshold, and then schedule an upgrade during the scheduled maintenance window.

Answer: C

Explanation:
https://cloud.google.com/architecture/autoscaling-cloud-spanner#linear

NEW QUESTION # 223
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