Google Professional Data Engineer Exam - Topic 5 Question 75 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 75
Topic #: 5

[All Professional Data Engineer Questions]

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?

AThe CSV data loaded in BigQuery is not flagged as CSV.

BThe CSV data has invalid rows that were skipped on import.

CThe CSV data loaded in BigQuery is not using BigQuery's default encoding.

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

Show Suggested Answer

Suggested Answer: D

by Karl at Dec 21, 2023, 08:28 PM

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Luisa

4 months ago

A is unlikely, it should recognize CSV format.

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Jamey

4 months ago

I disagree, ETL phase isn't always necessary.

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Santos

4 months ago

Wait, how can it not match byte-to-byte? That's weird!

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Juan

4 months ago

I think it's C, encoding issues are common.

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Lillian

5 months ago

Probably B, invalid rows can mess things up.

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Vilma

5 months ago

I feel like the CSV flagging might not be the issue, but I can't recall if it could lead to mismatches. I guess A is less likely?

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Ariel

5 months ago

I’m a bit confused about the ETL phase. I thought it was optional for loading into BigQuery, but could it really affect the byte-to-byte match?

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Kallie

5 months ago

I think invalid rows could definitely cause problems during import, so maybe B is the right choice. I’ve seen similar questions before.

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Becky

5 months ago

I remember something about encoding issues, so maybe it's option C? But I'm not entirely sure if that's the only reason for mismatches.

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Annice

5 months ago

I'm feeling pretty confident about this one. I think the most likely issue is that the CSV data isn't using BigQuery's default encoding, so the imported data won't match the source.

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Reuben

5 months ago

Okay, I've got a strategy here. I'll methodically go through each of the options and consider the potential issues that could lead to a byte-for-byte mismatch.

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Linn

5 months ago

Hmm, I'm a bit confused on this one. I'll need to review the details on how BigQuery handles CSV data imports to figure out the most likely cause.

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Lucia

5 months ago

This seems like a tricky one. I'll need to think carefully about the different ways the data could be mismatched during the import process.

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Deandrea

6 months ago

This question seems straightforward. I'll focus on the test conditions and the critical risk item to determine the correct test case.

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Shawnee

6 months ago

This looks like a straightforward question about starting an ODI agent on Linux. I think I've seen this command before, so I'll go with option A.

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Omer

10 months ago

I'm going with option C as well. BigQuery is pretty picky about the encoding, and if it's not the default, you can end up with mismatched data. Gotta love those character encoding problems!

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Cheryl

10 months ago

Ha! The question says the data is 'fully imported successfully', so option D about an ETL phase is clearly not the issue. These exam questions can be tricky sometimes.

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Deonna

9 months ago

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

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Ryan

9 months ago

B) The CSV data has invalid rows that were skipped on import.

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Rikki

9 months ago

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

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Dawne

11 months ago

Option B seems plausible - the CSV data could have invalid rows that were skipped on import. That would lead to the data not matching byte-to-byte. I'll keep that in mind.

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Theodora

10 months ago

Agreed. Skipping invalid rows during import could definitely lead to discrepancies in the data.

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Shelia

10 months ago

Yes, that makes sense. It's important to ensure the CSV data is clean before loading it into BigQuery.

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Hester

10 months ago

I think option B is the most likely cause. Invalid rows could definitely cause the data not to match byte-to-byte.

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Vincent

11 months ago

I agree with Ayesha, option B makes the most sense because invalid rows could cause the mismatch.

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Cordell

11 months ago

I disagree, I believe it could be option C.

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Ayesha

11 months ago

I think the most likely cause is option B.

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Sue

11 months ago

I think the most likely cause is option C - the CSV data loaded in BigQuery is not using BigQuery's default encoding. I've seen this issue before when the source file uses a different encoding than what BigQuery expects.

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