Practice Test 2 | Google Cloud Certified Professional Cloud Architect | Dumps | Mock Test
Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should they take? Choose 2 answers
A. Load logs into Google BigQuery.
B. Import logs into Google Operations Suite (formerly Stackdriver)
C. Insert logs into Google Cloud Bigtable.
D. Load logs into Google Cloud SQL.
E. Upload log files into Google Cloud Storage.
Correct Answer A and E
The order should be Upload log files into Google Cloud Storage and then Load logs into Google BigQuery.
E (Correct answer) – Upload log files into Google Cloud Storage Cloud Storage is best solution for Long-term disaster recovery backup. You can do SQL query direct against data in Cloud Storage. It also meets the low risk requirement to prevent potential accidental data loss and modification.
A (Correct answer) – Load logs into Google BigQuery – BigQuery is most suitable solution for doing analytics against large amount of data; You can do SQL query direct against data in Cloud Storage.
B – Import logs into Google Operations Suite (formerly Stackdriver) – Operations Suite (formerly Stackdriver) is not a suitable solution for Long-term disaster recovery backup
C – Insert logs into Google Cloud Bigtable: BigTable is not a suitable solution for Long-term disaster recovery backup
D – Load logs into Google Cloud SQL – Cloud SQL is relation database designed for transactional CRUD OLTP processing suitable for data less than 10 TB.
Note:
Our requirement is “analytics feature”
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BigQuery is Google’s Cloud-based data warehousing solution. It targets data in big picture and can query huge volume of data in a short time. As the data is stored in columnar data format,
it is much faster in scanning large amounts of data compared with BigTable.
BigQuery allows to scale to petabyte and is great enterprise data warehouse for analytics. BigQuery is serverless
BigTable is designed in NoSQL architecture, but can still use row-based data format. With data read/write under 10 milliseconds, it is good for applications that have frequent data ingestion. It can be scaleable to hundreds of petabytes and handle millions of operations per second.
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