Exam Professional Cloud Architect topic 1 question 35 discussion

Answer is A as they want to load logs for analytics and E for storing data in buckets for long term.

AE To archive approximately 100 TB of log data to the cloud and test the analytics features available while also retaining the data as a long-term disaster recovery backup, you can take the following steps: E: Upload log files into Google Cloud Storage: Google Cloud Storage is a scalable, durable, and fully-managed cloud storage service that can be used to store large amounts of data. You can upload your log files to Cloud Storage to archive them in the cloud. A: Load logs into Google BigQuery: Google BigQuery is a fully-managed, cloud-native data warehouse that can be used to analyze large amounts of data quickly and efficiently. You can load your log data into BigQuery to perform analytics on it and test the available analytics features. Other options, such as loading logs into Google Cloud SQL, importing logs into Google Stackdriver, or inserting logs into Google Cloud Bigtable, may not provide the necessary functionality for archiving and analyzing the log data.

I will go for AE.

A. Load logs into Google BigQuery: BigQuery is Google Cloud's serverless, highly scalable, and cost-effective multicloud data warehouse designed for data analytics. It's ideal for storing and analyzing large volumes of log data (100 TB in this case). You can use BigQuery's powerful SQL capabilities to run queries, generate reports, and gain insights from your logs. E. Upload log files into Google Cloud Storage: Cloud Storage provides durable, scalable, and secure object storage. It's perfect for storing your log data as a long-term disaster recovery backup. Cloud Storage offers different storage classes to optimize costs based on your data access frequency and retention needs.

This looks right.

If you want to analize those logs its recommended Big Query. For storing and backup Cloud Storage is your option, so AE

A and E can do the required task

AE are the only options for analysis and archiving

A load in Big query for analytics and E for cloud storage

The key word is 'Analytics' here the main reason for moving logs to GCP is to perform Analytics on the data. BigQuery is the best suite for it. For long term storage it wiould be GC

Answer is C and E Key features Real-time log management and analysis Cloud Logging is a fully managed service that performs at scale and can ingest application and platform log data, as well as custom log data from GKE environments, VMs, and other services inside and outside of Google Cloud. Get advanced performance, troubleshooting, security, and business insights with Log Analytics, integrating the power of BigQuery into Cloud Logging. - https://cloud.google.com/products/operations

Big Query for analytics, Cloud Storage for long term archive

For Storage GCS is the best option and for analyzing the data BIg Query makes sense

A E are ok

AE are the correct answers

Answers: A is correct because BigQuery is the fully managed cloud data warehouse for analytics and supports the analytics requirement. E is correct because Cloud Storage provides the Coldline storage class to support long-term storage with infrequent access, which would support the long-term disaster recovery backup requirement. https://cloud.google.com/bigquery/ https://cloud.google.com/stackdriver/ https://cloud.google.com/storage/docs/storage-classes#coldline https://cloud.google.com/sql/ https://cloud.google.com/bigtable/

Go for A,E