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Google Associate Cloud Engineer Exam - Topic 2 Question 111 Discussion

You have a large 5-TB AVRO file stored in a Cloud Storage bucket. Your analysts are proficient only in SQL and need access to the data stored in this file. You want to find a cost-effective way to complete their request as soon as possible. What should you do?
C) Create external tables in BigQuery that point to Cloud Storage buckets and run a SQL query on these external tables to complete your request.
A) Load data in Cloud Datastore and run a SQL query against it.
B) Create a BigQuery table and load data in BigQuery. Run a SQL query on this table and drop this table after you complete your request.
D) Create a Hadoop cluster and copy the AVRO file to NDFS by compressing it. Load the file in a hive table and provide access to your analysts so that they can run SQL queries.

Google Associate Cloud Engineer Exam - Topic 2 Question 111 Discussion

Actual exam question for Google's Associate Cloud Engineer exam
Question #: 111
Topic #: 2
[All Associate Cloud Engineer Questions]

You have a large 5-TB AVRO file stored in a Cloud Storage bucket. Your analysts are proficient only in SQL and need access to the data stored in this file. You want to find a cost-effective way to complete their request as soon as possible. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: C

https://cloud.google.com/bigquery/external-data-sources

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


by Ashleigh at Aug 18, 2025, 04:36 AM

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Madelyn
7 months ago
Wait, why not just use external tables? C seems easier.
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Herman
7 months ago
Definitely B! Quick and cost-effective.
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Carmela
7 months ago
Surprised there's no mention of using Dataflow here!
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Elvera
7 months ago
I thought Hadoop was outdated for this kind of task?
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Francine
8 months ago
BigQuery is super efficient for large datasets!
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Barney
8 months ago
I'm leaning towards option B, but I worry about the costs of loading such a large file into BigQuery just to drop it afterward.
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Yolande
8 months ago
I feel like we practiced a similar question where using external tables in BigQuery was the right choice, but I can't recall the exact details.
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Freeman
8 months ago
I think option C sounds familiar because it allows querying directly from Cloud Storage without needing to load the data, which seems efficient.
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Lavonna
9 months ago
I remember we discussed using BigQuery for large datasets, but I'm not sure if creating a table and dropping it later is the best approach for cost-effectiveness.
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Jennifer
9 months ago
Option D with Hadoop seems like overkill for this use case. The data is already in a Cloud Storage bucket, so I think the best approach is to use BigQuery, either through external tables or by loading the data directly.
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Howard
9 months ago
I'm leaning towards option B. Creating a temporary BigQuery table and loading the data there might be a bit more work, but it could provide better performance for the analysts' SQL queries.
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Reta
9 months ago
Option C seems like the most straightforward solution. Creating external tables in BigQuery that point to the Cloud Storage bucket should allow the analysts to run SQL queries without having to load the entire 5-TB file.
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Paulene
9 months ago
This looks like a tricky one. I'm not sure if I should go with option B or C. I'll need to think through the pros and cons of each approach.
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Lashaun
10 months ago
I disagree, I believe option C is better. It allows direct querying of data in Cloud Storage.
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Katina
11 months ago
I think option B is the best choice. It's cost-effective and efficient.
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