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Free Google Cloud Architect Professional Exam Braindumps (page: 29)

Page 28 of 68
PDF with 278 Questions

You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading.
Where should you store the data?

  1. Google BigQuery
  2. Google Cloud SQL
  3. Google Cloud Bigtable
  4. Google Cloud Storage

Answer(s): C

Explanation:

It is time-series data, So Big Table.
https://cloud.google.com/bigtable/docs/schema-design-time-series

Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads.

Good for:
Low-latency read/write access
High-throughput analytics
Native time series support
Common workloads:
IoT, finance, adtech
Personalization, recommendations
Monitoring
Geospatial datasets
Graphs


Reference:

https://cloud.google.com/storage-options/



The database administration team has asked you to help them improve the performance of their new database server running on Google Compute Engine. The database is for importing and normalizing their performance statistics and is built with MySQL running on Debian Linux. They have an n1-standard-8 virtual machine with 80 GB of SSD persistent disk.
What should they change to get better performance from this system?

  1. Increase the virtual machine's memory to 64 GB.
  2. Create a new virtual machine running PostgreSQL.
  3. Dynamically resize the SSD persistent disk to 500 GB.
  4. Migrate their performance metrics warehouse to BigQuery.
  5. Modify all of their batch jobs to use bulk inserts into the database.

Answer(s): C



Your application needs to process credit card transactions. You want the smallest scope of Payment

Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

  1. Create a tokenizer service and store only tokenized data.
  2. Create separate projects that only process credit card data.
  3. Create separate subnetworks and isolate the components that process credit card data.
  4. Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.
  5. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.

Answer(s): A

Explanation:

https://cloud.google.com/solutions/pci-dss-compliance-in-gcp



You have been asked to select the storage system for the click-data of your company's large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute, with bursts of up to 8,500 clicks per second. It must been stored for future analysis by your data science and user experience teams.
Which storage infrastructure should you choose?

  1. Google Cloud SQL
  2. Google Cloud Bigtable
  3. Google Cloud Storage
  4. Google cloud Datastore

Answer(s): C

Explanation:

https://cloud.google.com/bigquery/docs/loading-data-cloud-storage






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