Exam Professional Cloud Architect topic 1 question 2 discussion

This question could go either way for A or B. But Big Query was designed with this in mind, according to numerous Google presentation and videos. Cloud Datastore is a NoSQL database (https://cloud.google.com/datastore/docs/concepts/overview) Cloud Storage does not have an SQL interface. The previous two sentences eliminate options C and D. So I'd pick "A".

I'll go with A because BQ (and BT) are usually meant for analytics. B isn't correct because Cloud SQL does not scale to that volume. C isn't correct because Cloud Storage does not provide a standard SQL mechanism. D could be right but it sounds off because of the analytics requirement.

GBQ supports multi PB of records, CloudSQL won't.

A is ok

Cloud SQL supports TB not PB of data

optimize the ease of Analysis ... Any data that can be analyzed is using Big Query So Option A will suit the requirement

CloudSQL cannot handle petabytes but tens of terabytes only.

Cloud SQL is limited to 30TB of data. A is correct.

A or B but as it said it is for an analyst, A is better

A is the answer.

I'll go A as BQ has compatible usage of SQL

Answer: A. Load data into Google BigQuery This is the correct choice because it's the only option that meets all requirements: Can handle multi-petabyte scale Provides the required SQL interface for analysts Ensures 24/7 availability Optimized for analytical queries Serverless and automatically scales

The best answer here is A. Load data into Google BigQuery. Here's why: 1. Designed for large datasets: BigQuery is a serverless, highly scalable, and cost-effective multicloud data warehouse designed specifically for analyzing massive datasets. Petabyte-scale data is exactly what it excels at. 2. SQL interface: Your analysts are already familiar with SQL, and BigQuery uses standard SQL, making the transition easy and minimizing the learning curve. 3. High availability: BigQuery offers high availability with built-in redundancy and replication. 4. Performance: BigQuery is optimized for analytical queries and can handle complex queries across massive datasets very efficiently.

A, BigQuery is more suitable for analysis. While Cloud SQL can work, it's more generic

Let's go with option elimination A. Load data into Google BigQuery >>Big Query = Analytic + SQL (Ease of using SQL) Storage hence the solution B. Insert data into Google Cloud SQL >> Yes you can SQL query with your own applicaiton console compared to BigQuery SQL console, and 24 hrs avalablity but you won't have 1-2 sec response on petabytes of data, as you can do in GCP BigQuery partitioned and clustered tables. C. Put flat files into Google Cloud Storage >>The requirement is for analytics and SQL querying of data. You can store it in the flat file but will need to use GCP BigQuery to do that D. Stream data into Google Cloud Datastore >> Only dealing with storage problems does not address analytics and SQL querying

A. Load data into Google BigQuery BigQuery is a fully managed, cloud-native data warehousing solution that makes it easy to analyze large and complex datasets. It is optimized for analyzing large amounts of data quickly, and can handle petabyte-scale datasets with ease. It also has a SQL-like interface that is familiar to business analysts, making it easy for them to query and analyze the data. Additionally, BigQuery is highly scalable and can handle high query concurrency, making it a good choice for storing data that must be available 24/7. Option B, inserting data into Google Cloud SQL, is not a good choice for a multi-petabyte dataset because Cloud SQL is not designed to handle such large volumes of data. Option C, putting flat files into Cloud Storage, is also not a good choice because it is not optimized for querying and analyzing data. Option D, streaming data into Cloud Datastore, is not a good choice because Cloud Datastore is a NoSQL database and does not have a SQL-like interface.

A. Load data into Google BigQuery To optimize the storage of the multi-petabyte data set for ease of analysis by business analysts who have experience only with using a SQL interface, you should load the data into Google BigQuery. BigQuery is a fully-managed, cloud-native data warehouse that allows you to perform fast SQL queries on large amounts of data. By loading the data into BigQuery, you can provide your business analysts with a familiar SQL interface for querying the data, making it easier for them to analyze the data set. Other options, such as inserting data into Google Cloud SQL, putting flat files into Google Cloud Storage, or streaming data into Google Cloud Datastore, may not provide the necessary SQL interface or query performance for efficient analysis of the data set.