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Google Exam Associate Data Practitioner Topic 3 Question 9 Discussion

Actual exam question for Google's Associate Data Practitioner exam
Question #: 9
Topic #: 3
[All Associate Data Practitioner Questions]

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

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Suggested Answer: C

Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.


by Lavonne at Mar 19, 2025, 09:39 AM

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Rory
1 months ago
Option B seems like the way to go. Cloud Data Fusion makes it easy to connect multiple data sources and perform complex analyses.
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Melissia
10 days ago
I agree, using plugins to connect to the data sources and performing SQL join operations seems efficient.
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Laurel
11 days ago
I think option B is the best choice. Cloud Data Fusion can easily connect to both BigQuery and Cloud Storage.
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Monroe
2 months ago
Ha! Looks like we've got some 'Big Data' on our hands. I'd go with option D - keep it simple, stupid!
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Theresia
2 months ago
PySpark is overkill for a one-time analysis. Option C looks like the most straightforward approach here.
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Suzan
18 days ago
Yeah, using external tables to perform SQL joins seems like the simplest solution.
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Maybelle
1 months ago
I agree, Option C seems like the most efficient choice for this scenario.
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Ardella
2 months ago
I'm not a fan of external tables - they can be a bit of a pain to manage. I'd go with option D and just load the Parquet files directly into BigQuery.
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Irma
2 months ago
I think option D could work too, loading the files into BigQuery.
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Filiberto
2 months ago
I prefer option C, creating external tables over the files in Cloud Storage.
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Fairy
2 months ago
I agree, using Dataproc cluster with PySpark seems efficient.
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Daniel
2 months ago
Cloud Data Fusion seems like the easiest way to get this done. No need to write any code!
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Joni
1 months ago
C) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
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Peggy
1 months ago
B) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
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Sarah
2 months ago
A) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
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Sommer
2 months ago
I think option A sounds like a good idea.
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