Exam Associate Cloud Engineer topic 1 question 146 discussion

I think it should be D. https://cloud.google.com/bigquery/external-data-sources

Correct Answer is (D): Introduction to external data sources This page provides an overview of querying data stored outside of BigQuery. https://cloud.google.com/bigquery/external-data-sources

Interesting how most people are choosing D, yet that answer makes no reference to Cloud Spanner. I'm going to have to go with B since it specifically mentions Cloud Spanner and Cloud Bigtable.

The Q says that an analyst wants to analyze data about a user from 2 different sources, which Dataflow will give you, plus as Google states, it allows you more time analyzing stuff and less time fiddling with setting things up, which option D is talking about, which is wrong per the asked Q.

D is apt and possible.

BigQuery is powerful. If you have data in one of the popular sources like Cloud Storage or Bigtable, it is much more efficient - both for cost and computation - to create an external table on those data sources, than to copy their data around. Besides that, also keep in mind that table clones and snapshots are much more efficient than full table copy etc.

I go for option B. As in option D, the data is backed up data and not the most recent data.

B. Create a dataflow job that copies data from Cloud Bigtable and Cloud Spanner for specific users. To join data from Cloud Spanner and Cloud Bigtable for specific users, creating a dataflow job that copies data from both sources is the most efficient option. This approach allows you to process the data in parallel, and you can take advantage of Cloud Dataflow's autoscaling feature to handle large volumes of data. You can use Cloud Dataflow to read data from Cloud Bigtable and Cloud Spanner, join the data based on the user fields, and write the output to a new location or send it to the analyst. Option A (copying data from Cloud Storage) does not provide data from Cloud Spanner, and option C (running a Spark job on a Dataproc cluster) involves higher overhead costs. Option D (using BigQuery external tables) is not efficient for ad hoc requests, as data is exported from Spanner to Cloud Storage during backups, so there may be a delay in data availability.

I thinks is D, but not 100% sure, because D does not have any infomation about the specific user like others options.

D is the correct answer, An external data source is a data source that you can query directly from BigQuery, even though the data is not stored in BigQuery storage. BigQuery supports the following external data sources: Amazon S3 Azure Storage Cloud Bigtable Cloud Spanner Cloud SQL Cloud Storage Drive

D makes sense as the BigQuery external tables are made for such use cases. and "efficient" keyword makes sense to use this way as resources used are less.

First of all, using Dataflow can perhaps be effective, but NOT efficient, specially because of costs. Second: “To query Cloud Bigtable data using a permanent external table, you: Create a table definition file (for the API or bq command-line tool); Create a table in BigQuery linked to the external data source; Query the data using the permanent table.” Source: https://cloud.google.com/bigquery/docs/external-data-bigtable#:~:text=To%20query%20Cloud%20Bigtable%20data,data%20using%20the%20permanent%20table Third: “To query a Cloud Storage external data source, provide the Cloud Storage URI path to your data and create a table that references the data source.” Source: https://cloud.google.com/bigquery/docs/external-data-cloud-storage Correct answer: D.

"efficiently as possible" -> use the least amount of resources and achieve the same result... so I think it's D

Most "cloud" solution is D

option d

Option is D

I think it is B. The data in Cloud storage is not up to date as backup window is daily. SO, there are chances is missing one day worth of data. As it is mentioned as "efficiently" instead of quickly, I would choose "B".