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Google Exam Professional Data Engineer 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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Omer
19 days 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
24 days 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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Rikki
3 days ago
A) The CSV data loaded in BigQuery is not flagged as CSV.
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Dawne
1 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 days ago
Agreed. Skipping invalid rows during import could definitely lead to discrepancies in the data.
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Shelia
19 days 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
24 days 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 months ago
I agree with Ayesha, option B makes the most sense because invalid rows could cause the mismatch.
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Cordell
2 months ago
I disagree, I believe it could be option C.
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Ayesha
2 months ago
I think the most likely cause is option B.
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Sue
2 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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Stephen
14 days 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
19 days ago
I agree, option C seems like the most likely cause. Encoding issues can definitely cause data mismatches.
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Franchesca
20 days 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
24 days ago
I agree, option C seems like the most likely cause. Encoding issues can definitely cause data mismatches.
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Sue
2 months 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
2 months ago
I agree with Antonio, invalid rows could definitely cause the mismatch.
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Antonio
2 months ago
I think the most likely cause is option B.
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