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Google Professional Data Engineer Exam - Topic 5 Question 75 Discussion

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?
D) The CSV data has not gone through an ETL phase before loading into BigQuery.
A) The CSV data loaded in BigQuery is not flagged as CSV.
B) The CSV data has invalid rows that were skipped on import.
C) The CSV data loaded in BigQuery is not using BigQuery's default encoding.

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?

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Suggested Answer: D

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

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Luisa
9 months ago
A is unlikely, it should recognize CSV format.
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Jamey
10 months ago
I disagree, ETL phase isn't always necessary.
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Santos
10 months ago
Wait, how can it not match byte-to-byte? That's weird!
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Juan
10 months ago
I think it's C, encoding issues are common.
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Lillian
10 months ago
Probably B, invalid rows can mess things up.
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Vilma
10 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
11 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
11 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
11 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
11 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
11 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
11 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
11 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
11 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
11 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
1 year 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
1 year 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
1 year ago
C) The CSV data loaded in BigQuery is not using BigQuery's default encoding.
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Ryan
1 year ago
B) The CSV data has invalid rows that were skipped on import.
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Rikki
1 year ago
A) The CSV data loaded in BigQuery is not flagged as CSV.
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Dawne
1 year 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
1 year ago
Agreed. Skipping invalid rows during import could definitely lead to discrepancies in the data.
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Shelia
1 year 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
1 year 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
1 year ago
I agree with Ayesha, option B makes the most sense because invalid rows could cause the mismatch.
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Cordell
1 year ago
I disagree, I believe it could be option C.
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Ayesha
1 year ago
I think the most likely cause is option B.
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Sue
1 year 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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Stephen
1 year ago
It could also be option B - invalid rows being skipped during import. That could lead to discrepancies in the data.
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Layla
1 year ago
I agree, option C seems like the most likely cause. Encoding issues can definitely cause data mismatches.
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Franchesca
1 year ago
I think it could also be option B - invalid rows being skipped during import could lead to data discrepancies.
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Teri
1 year ago
I agree, option C seems like the most likely cause. Encoding issues can definitely cause data mismatches.
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Sue
1 year ago
I believe option D could also be a potential cause, if the data wasn't properly transformed before loading.
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Dannette
1 year ago
I agree with Antonio, invalid rows could definitely cause the mismatch.
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Antonio
1 year ago
I think the most likely cause is option B.
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