Practice Test 1 | Google Cloud Certified Professional Cloud Architect | Dumps | Mock Test

Correct Answer C

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A – Google cloud Datastore. Doesn’t meet this requirement “It must be stored for future analysis by your data science and user experience teams.” Google Cloud Datastore is a NoSQL document database built for automatic scaling, high performance, and ease of application development and integrating well with App Engine.

Datastore: A scalable, fully-managed NoSQL document database for your web and mobile applications.

Good for:

Semi-structured application data

Hierarchical data

Durable key-value data

Workload:

User profiles

Product catalogs

Game state

B – Google Cloud SQL. Cloud SQL is mainly for OLTP (Transactional, CRUD) not for taking and storing streaming data. It does not have the scalability and elasticity to absorb this amount of data in real time.

C (Correct Answer) – Google Cloud Bigtable. The reason is that data is in IoT nature and it will be used for analytics.

Bigtable: A scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads. Bigtable is ideal for very large NoSQL datasets and is useful for high-speed transactions and analysis. It integrates well with ML. Dataproc, and analytics

Good for

Low-latency read/write access

High-throughput analytics

Native time series support

Work load

IoT, finance, adtech

Personalization, recommendations

Monitoring

Geospatial datasets

Graphs

Although both Datastore and Bigtable are NoSQL databases, Bigtable is able to support over a petabyte of data and is useful for high speed analytics as well, whereas Datastore is not.

D – Google Cloud Storage. GCS is ideally for Object storage purpose although it has pretty good scalability. It’s not suitable for IoT kind of spiky streaming data. Its buckets initially support roughly 1000 writes per second and then scale as needed. As the request rate for a given bucket grows, Cloud Storage automatically increases the IO capacity for that bucket by distributing the request load across multiple servers. Especially considering the click stream rate of 6,000 clicks per minute, with bursts of up to 8,500 clicks per second, the way GCS handle and absorb this kind high and low data stream by scale up and down make it not suitable for this task.