Q: 3

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options

Discussion

Sam L. Oct 4, 2026 7:15 pm

B . A is a trap since Kafka adds overhead, Pub/Sub is cheaper and survives unreliable leased lines.

Kevin A. Oct 11, 2026 3:55 am

B tbh, only edge case where A might win is if you needed local storage due to regulatory or privacy constraints. But question just wants cost-effective and resilient delivery, so managed Pub/Sub is perfect. Anyone disagree if on-prem cache wasn't a factor?

Jordan Oct 18, 2026 7:56 am

A isn’t it, B is the call here since Pub/Sub handles the unreliable connections and scales well for cost. This comes up in the official Google practice questions and the study guide covers managed service options pretty clearly.

Maya E. Oct 17, 2026 5:19 am

Jack U. Oct 16, 2026 8:51 pm

Don’t think A is right, seems like a trap since managing Kafka yourself adds overhead. I’m going with B this time, Pub/Sub just fits better for unreliable leased lines and it’s fully managed. Maybe some edge cases but I think B is safest.

Mason G. Oct 15, 2026 12:47 pm

A is wrong, B. Saw a similar question and Pub/Sub is just cheaper for unreliable lines.

IvyA Oct 3, 2026 12:54 pm

I don't think it's A. B fits better here since Cloud Pub/Sub is designed for unreliable or lossy networks and saves you from Kafka maintenance overhead. A is tempting but adds cost and complexity you don't need for the actual requirement. Pretty sure B's the expected answer on these types of Google questions.

Adam I. Oct 18, 2026 11:53 pm

B , Pub/Sub is built to handle unreliable and lossy connections well, plus it’s managed so you avoid all the Kafka ops headaches. If the question was about buffering on-site or local failover, I'd maybe consider A, but for pure cost-effectiveness with flaky links B is safer. Seen this logic in a few practice sets. Disagree?

Arjun I. Oct 19, 2026 12:10 pm

Is anyone using the official study guide or practice tests to prep for these scenario questions? I keep seeing similar cases where Pub/Sub is the recommended approach for flaky connections. Just want to confirm if the guide mentions this pattern clearly.

Emma E. Oct 16, 2026 12:06 pm

B , saw a similar scenario in practice sets and Pub/Sub is reliable for flaky networks plus it's cheaper compared to Interconnect.

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Correct Answer:

Explanation

The most likely cause for a successful load job in BigQuery resulting in a byte-for-byte mismatch with the source CSV is that some rows in the source file were invalid and subsequently skipped. BigQuery's load job configuration includes the maxBadRecords property. If this is set to a value greater than 0, the job is permitted to skip malformed rows (e.g., rows with an incorrect number of columns) and still complete successfully. The job is considered successful, but the resulting table will not contain the skipped rows, leading to a discrepancy with the source file.

Why Incorrect

A. The CSV data loaded in BigQuery is not flagged as CSV.

If the data format was not correctly specified as CSV, the load job would likely fail due to parsing errors or load the data incorrectly (e.g., each line as a single string), which is a more severe issue than a byte-for-byte mismatch.

C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

BigQuery expects UTF-8 encoding for CSV files. An encoding mismatch would typically cause parsing errors, leading to a failed job. For the job to succeed, it would have to be configured to skip these errored rows, which points back to the mechanism described in option B.

D. The CSV data has not gone through an ETL phase before loading into BigQuery.

The absence of a pre-load ETL (Extract, Transform, Load) process is a common pattern (known as ELT). It is a design choice and not a direct technical cause for data being skipped during a successful load operation.

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References

1. Google Cloud Documentation, "Loading CSV data from Cloud Storage": Under the bq load command options, the documentation for the --maxbadrecords flag states: "The maximum number of bad records to ignore. The default is 0, which causes the job to fail if any invalid rows are found." This directly supports the explanation that setting this value allows a job to succeed while skipping invalid rows.

Source: Google Cloud Documentation, BigQuery > Guides > Load data > Loading CSV data from Cloud Storage.

2. Google Cloud Documentation, "JobConfigurationLoad" API Reference: The description for the maxBadRecords field in the JobConfigurationLoad resource confirms its function: "The maximum number of bad records that BigQuery can ignore when running the job... If the number of bad records exceeds this value, an invalid error is returned in the job result. The default value is 0, which requires all records to be valid." This explains the mechanism for a successful job with missing data.

Source: Google Cloud Documentation, BigQuery > Reference > REST > v2 > jobs.

3. Google Cloud Documentation, "Troubleshooting data loading errors": This guide discusses common errors, including "CSV parsing error". It notes that such errors occur when the data doesn't conform to CSV formatting rules. The resolution often involves either fixing the source data or using load options like allowJaggedRows or maxBadRecords to ignore the problematic rows.

Source: Google Cloud Documentation, BigQuery > Support > Troubleshoot > Troubleshooting data loading errors.

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

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