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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 42 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 42
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook.
What should you do?

  • A. Use AI Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.
  • B. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.
  • C. Download your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
  • D. From a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️
by zosoabi at June 10, 2021, 3:31 p.m.

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zosoabi
Highly Voted 3 years, 4 months ago
A: no "CSV" found in provided link https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas
upvoted 27 times
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Sum_Sum
Highly Voted 11 months, 1 week ago
Selected Answer: A
A is the google recommended answer. And what you should use C is what the intern does ...
upvoted 6 times
sharth
9 months, 2 weeks ago
Dude, I laughed so hard
upvoted 3 times
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PhilipKoku
Most Recent 4 months, 1 week ago
Selected Answer: A
A) Magic command
upvoted 1 times
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M25
1 year, 5 months ago
Selected Answer: A
Went with A
upvoted 2 times
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SergioRubiano
1 year, 6 months ago
Selected Answer: A
A, Using the command %%bigquery df
upvoted 1 times
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Dunnoth
1 year, 8 months ago
Why not D? using BQ notebook magic would be ok for a single time use. but usually a DS would reload the data multiple time, and every time you need to stream 500mb data to the notebook instance from BQ. Isn't it cheaper to store the data as a csv in a bucket?
upvoted 2 times
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John_Pongthorn
1 year, 9 months ago
Selected Answer: A
%%bigquery df SELECT name, SUM(number) as count FROM `bigquery-public-data.usa_names.usa_1910_current` GROUP BY name ORDER BY count DESC LIMIT 3 print(df.head())
upvoted 4 times
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hiromi
1 year, 10 months ago
Selected Answer: A
A https://cloud.google.com/bigquery/docs/visualize-jupyter
upvoted 2 times
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Sachin2360
2 years, 3 months ago
Answer : A . Refer to this link for details: https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas First 2 points talks about querying the data. Download query results to a pandas DataFrame by using the BigQuery Storage API from the IPython magics for BigQuery in a Jupyter notebook. Download query results to a pandas DataFrame by using the BigQuery client library for Python. Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python. Download BigQuery table data to a pandas DataFrame by using the BigQuery Storage API client library for Python.
upvoted 2 times
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Mohamed_Mossad
2 years, 4 months ago
Selected Answer: A
https://googleapis.dev/python/bigquery/latest/magics.html#ipython-magics-for-bigquery
upvoted 2 times
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NickNtaken
2 years, 5 months ago
Selected Answer: A
this is the simplest and most straightforward way read BQ data into Pandas dataframe.
upvoted 3 times
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mmona19
2 years, 6 months ago
Selected Answer: C
both A and C is technically correct. C has more manual step and A has less. The question does not ask which requires least effort. so C is clear answer
upvoted 1 times
wish0035
1 year, 10 months ago
"A and C are valid, but C is more difficult than A. they don't ask to be easier so I will go with the more difficult". WHAAAT? Google best practices are always: easier > harder. Even they encourage you to skip ML if you don't need ML.
upvoted 2 times
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SlipperySlope
2 years, 8 months ago
Selected Answer: C
C is the correct answer due to the size of the data. It wouldn't be possible to download it all into an in memory data frame.
upvoted 1 times
u_phoria
2 years, 3 months ago
500mb of data into a pandas dataframe generally isn't a problem, far from it.
upvoted 2 times
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ggorzki
2 years, 9 months ago
Selected Answer: A
IPython magics for BigQuery https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas
upvoted 1 times
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NamitSehgal
2 years, 9 months ago
I agree with A
upvoted 1 times
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Y2Data
3 years, 1 month ago
Just load it https://googleapis.dev/python/bigquery/latest/magics.html
upvoted 2 times
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