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Your organization is currently updating an existing corporate application that is running in another public cloud to access managed database services in Google Cloud. The application will remain in the other public cloud while the database is migrated to Google Cloud. You want to follow Google-recommended practices for authentication. You need to minimize user disruption during the migration. What should you do?
You are configuring a brand new PostgreSQL database instance in Cloud SQL. Your application team wants to have an optimal and highly available environment with automatic failover to avoid any unplanned outage. What should you do?
You are designing a database architecture for a global application that stores information about public parks worldwide. The application uses the database for read-only purposes, and a centralized batch job updates the database nightly. You want to select an open source, SQL-compliant database. What should you do?
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?
You have a Cloud SQL instance (DB-1) with two cross-region read replicas (DB-2 and DB-3). During a business continuity test, the primary instance (DB-1) was taken offline and a replica (DB-2) was promoted. The test has concluded and you want to return to the pre-test configuration. What should you do?
Your organization has a ticketing system that needs an online marketing analytics and reporting application. You need to select a relational database that can manage hundreds of terabytes of data to support this new application. Which database should you use?
Your company is using Cloud SQL for MySQL with an internal (private) IP address and wants to replicate some tables into BigQuery in near-real time for analytics and machine learning. You need to ensure that replication is fast and reliable and uses Google-managed services. What should you do?
You use Python scripts to generate weekly SQL reports to assess the state of your databases and determine whether you need to reorganize tables or run statistics. You want to automate this report but need to minimize operational costs and overhead. What should you do?
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?
You are configuring a new application that has access to an existing Cloud Spanner database. The new application reads from this database to gather statistics for a dashboard. You want to follow Google-recommended practices when granting Identity and Access Management (IAM) permissions. What should you do?

Question 29 - Professional Cloud Database Engineer discussion

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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.
Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
Answers
A.
Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
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.
Answers
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.
Use Memorystore to handle your low-latency requirements and for real-time analytics.
Answers
C.
Use Memorystore to handle your low-latency requirements and for real-time analytics.
D.
Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.
Answers
D.
Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.
Suggested answer: A

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

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asked 18/09/2024
Jeff Benson
44 questions
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