Google Associate Data Practitioner Exam - Topic 2 Question 19 Discussion

Actual exam question for Google's Associate Data Practitioner exam

Question #: 19
Topic #: 2

[All Associate Data Practitioner Questions]

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

AUse Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

BUse BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

CCreate a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

DUse Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

Show Suggested Answer

Suggested Answer: B

Comprehensive and Detailed In-Depth

For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.

Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.

Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.

Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.

Option D: Dataflow is powerful for large-scale or streaming ETL but introduces unnecessary complexity and cost for a small, simple batch job. Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.' Reference: Google Cloud Documentation - 'BigQuery Data Loading' (https://cloud.google.com/bigquery/docs/loading-data).

Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.'

by Corinne at Nov 21, 2025, 05:29 PM

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Shayne

9 hours ago

A sounds too complicated for just filtering and aggregating.

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Julianna

6 days ago

I think D could be overkill for simple tasks.

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Joanne

11 days ago

B is the simplest and most cost-effective option!

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Leonard

16 days ago

Option B - the Swiss Army Knife of data transformation! Why use a bazooka when a pocket knife will do?

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Franklyn

21 days ago

Cloud Data Fusion looks interesting, but Option B is the most straightforward and cost-effective solution.

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Elke

26 days ago

I agree with Option B. Why complicate things when BigQuery can handle the transformation easily?

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Dorthy

1 month ago

Option B is the way to go. BigQuery's SQL capabilities are perfect for this simple transformation.

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Zack

1 month ago

I recall that Cloud Data Fusion is great for visual ETL, but I wonder if it's necessary for simple tasks like filtering. I might lean towards option B for simplicity.

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Lorrine

1 month ago

I practiced a similar question where we had to choose between Dataproc and Dataflow. I think both are powerful, but for this case, I feel like B or C might be more straightforward.

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Louisa

2 months ago

I'm not entirely sure, but I think using Dataflow could be overkill for just filtering and aggregating a small dataset. Maybe it's not the most cost-effective option?

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Fernanda

2 months ago

I think I'll go with option B. BigQuery's SQL capabilities should be sufficient for the filtering and aggregation needed here, and it's likely the most straightforward and cost-effective solution for a small dataset.

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Marge

2 months ago

Dataflow (option D) seems like a good choice if I want to leverage Apache Beam for the transformation logic. But I'm not sure if that's necessary for such a simple use case. I'll have to weigh the pros and cons of each approach.

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Avery

2 months ago

Option C with Cloud Data Fusion looks interesting, but I'm not too familiar with that service. I'll need to research how it compares to the other choices in terms of complexity and performance for this type of task.

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James

2 months ago

I agree, B is cost-effective and straightforward. No extra services needed.

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Mickie

2 months ago

I remember we discussed how using BigQuery's SQL capabilities can be really efficient for simple transformations. It seems like option B might be the best choice.

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Albina

3 months ago

I think option B is the best choice. Simple SQL in BigQuery is efficient.

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Glory

3 months ago

Dataproc and Spark seem like overkill for such a small dataset. Option B is the most efficient choice.

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Chantell

3 months ago

I'm a bit unsure about this one. The question mentions efficiency and cost-effectiveness, so I'm not sure if Dataproc and Spark (option A) might be overkill. Maybe I should look into the other options a bit more.

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Glory

3 months ago

Hmm, this seems like a straightforward data transformation task. I think I'll go with option B and use BigQuery's SQL capabilities - it's the most direct approach and should be cost-effective for a small dataset.

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