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Google Associate Data Practitioner Exam - Topic 1 Question 27 Discussion

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?
B) Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
A) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
C) Create 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.
D) Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

Google Associate Data Practitioner Exam - Topic 1 Question 27 Discussion

Actual exam question for Google's Associate Data Practitioner exam
Question #: 27
Topic #: 1
[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?

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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.'

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).


by Ranee at Jun 20, 2026, 07:30 PM

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