This PDE practice question tests your understanding of building and operationalizing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated daily and each file is about 10 GB. The data is not time-sensitive and can be processed within a 24-hour window. Which service is most cost-effective for this use case?
Dataproc Serverless with PySpark
Dataproc Serverless is cost-effective and suitable for batch processing of large CSVs.
Dataflow with batch mode
Why wrong: Dataflow is more expensive for batch than Dataproc Serverless.
Cloud Data Fusion
Why wrong: Data Fusion is a full ETL tool with higher costs and complexity.
BigQuery Data Transfer Service
Why wrong: Data Transfer Service is for scheduled transfers, not processing.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Dataproc Serverless with PySpark
Dataproc Serverless with PySpark is the most cost-effective choice because it eliminates cluster management overhead and automatically scales resources based on workload, charging only for the processing time used. For 10 GB CSV files processed daily within a 24-hour window, the serverless model avoids the fixed costs of a persistent cluster, making it ideal for batch, non-time-sensitive jobs. PySpark's native support for CSV parsing and BigQuery integration via the Spark BigQuery connector ensures efficient data loading without additional services.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Dataproc Serverless with PySpark
Why this is correct
Dataproc Serverless is cost-effective and suitable for batch processing of large CSVs.
Related concept
Read the scenario before looking for a memorised answer.
Dataflow with batch mode
Why it's wrong here
Dataflow is more expensive for batch than Dataproc Serverless.
Cloud Data Fusion
Why it's wrong here
Data Fusion is a full ETL tool with higher costs and complexity.
BigQuery Data Transfer Service
Why it's wrong here
Data Transfer Service is for scheduled transfers, not processing.
Common exam traps
The trap here is that candidates often choose Dataflow (Option B) because it is a popular batch processing service, but they overlook that Dataproc Serverless is more cost-effective for non-time-sensitive, large CSV batch jobs due to its serverless pricing model and native Spark support for CSV processing.
Detailed technical explanation
Dataproc Serverless uses an ephemeral, auto-scaling Spark cluster that provisions resources based on the job's DAG and data size, leveraging the Spark BigQuery connector (com.google.cloud.spark.bigquery) to write directly to BigQuery tables with optimized write semantics like micro-batching. Under the hood, it uses the Cloud Storage connector to read CSV files in parallel, applying schema inference and optional transformations via PySpark DataFrames, and then loads data into BigQuery using the Storage Write API for high-throughput ingestion. In real-world scenarios, this approach can reduce costs by up to 50% compared to a fixed Dataproc cluster for daily 10 GB jobs, as it avoids idle compute time and scales down to zero after completion.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Dataproc Serverless with PySpark — Dataproc Serverless with PySpark is the most cost-effective choice because it eliminates cluster management overhead and automatically scales resources based on workload, charging only for the processing time used. For 10 GB CSV files processed daily within a 24-hour window, the serverless model avoids the fixed costs of a persistent cluster, making it ideal for batch, non-time-sensitive jobs. PySpark's native support for CSV parsing and BigQuery integration via the Spark BigQuery connector ensures efficient data loading without additional services.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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