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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 4 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 4
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

  • A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
  • B. Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
  • C. Ingest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
  • D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by ralf_cc at July 8, 2021, 12:35 p.m.

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nunzio144
Highly Voted 7 months, 1 week ago
It should be D .... Data Fusion is not SQL syntax ....
upvoted 22 times
q4exam
3 years, 7 months ago
Agree, BQ is the only serverless that support SQL
upvoted 4 times
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A4M
3 years, 3 months ago
Needs to be D as the most suitable answer given the req's in question Datafusion is more of a no code Data transformation tool
upvoted 1 times
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Celia20210714
Highly Voted 3 years, 9 months ago
ANS: A https://cloud.google.com/data-fusion#section-1 - Data Fusion is a serverless approach leveraging the scalability and reliability of Google services like Dataproc means Data Fusion offers the best of data integration capabilities with a lower total cost of ownership. - BigQuery is serverless and supports SQL. - Dataproc is not serverless, you have to manage clusters. - Cloud SQL is not serverless, you have to manage instances.
upvoted 12 times
TornikePirveli
8 months, 2 weeks ago
By your logic it should be D, because BQ is fully serverless and supports SQL
upvoted 1 times
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q4exam
3 years, 7 months ago
Data Fusion is not serverless, it create dataproc to execute the job .... I think the answer is C
upvoted 1 times
mousseUwU
3 years, 6 months ago
Data Fusion is serverless: https://cloud.google.com/data-fusion#all-features
upvoted 3 times
tavva_prudhvi
2 years, 1 month ago
I think you're only viewing the sentence "A serverless approach leveraging the scalability and reliability of Google services like Dataproc means Data Fusion offers the best of data integration capabilities with a lower total cost of ownership", The sentence implies that Data Fusion leverages a serverless approach, but it does not explicitly state that Data Fusion itself is serverless. It states that Data Fusion offers the best of data integration capabilities by using a serverless approach that leverages the scalability and reliability of Google services like Dataproc. So, while Data Fusion may not be fully serverless, it is designed to take advantage of serverless capabilities through its integration with Google services.
upvoted 2 times
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mousseUwU
3 years, 6 months ago
Agree, A is correct
upvoted 2 times
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manualrg
Most Recent 3 months, 4 weeks ago
Selected Answer: B
Both B and D are vaild IMHO (SQL + Severless https://cloud.google.com/dataproc-serverless/docs/overview), but the book Official Google Cloud Certified Professional Machine Learning Engineer Study Guide says B (I think there several errors in the book)
upvoted 2 times
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joqu
5 months, 2 weeks ago
Selected Answer: D
People giving other answers are to hang up on the fact that it currently runs in PySpark. The data is in GCS, you want quick serverless solution and use SQL syntax - BigQuery is the only good option that "meets the speed and processing requirements".
upvoted 2 times
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LeumaS_NoswaY
7 months, 2 weeks ago
B. You need Cloud Dataproc to transform the data from PySpark to Spark SQL
upvoted 1 times
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TornikePirveli
8 months, 2 weeks ago
Serverless, SQL syntax -> BigQuery, simple as that
upvoted 3 times
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jsalvasoler
9 months ago
I am very curious. Why are the solutions (when I click Reveal Solution) generally WRONG?
upvoted 2 times
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tadeupan
9 months, 2 weeks ago
option D because needs a serveless solution and sql sintax and BigQuery offer this. Datarproc is not serverless, so B is incorrect, D is correct option.
upvoted 2 times
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Yorko
9 months, 3 weeks ago
Selected Answer: D
There's an updated version of this question in the official Google Cloud certified PMLE study guide. Option D is marked as correct
upvoted 2 times
TornikePirveli
8 months, 2 weeks ago
Can you link the updated version? On Amazon it's still 1st version and marked B
upvoted 1 times
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PhilipKoku
11 months ago
Selected Answer: D
The best approach is option D: Ingest data into BigQuery and use SQL queries for transformations. This leverages BigQuery’s serverless capabilities, efficient processing, and seamless integration with other Google Cloud services.
upvoted 2 times
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fragkris
1 year, 5 months ago
Selected Answer: D
D - BigQuery is the only serverless and SQL-syntax option.
upvoted 1 times
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Sum_Sum
1 year, 5 months ago
Selected Answer: D
D - as BQ is server less and supports SQL none of the other options match both criteria
upvoted 2 times
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12112
1 year, 9 months ago
Selected Answer: D
I'll go with D.
upvoted 1 times
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M25
1 year, 11 months ago
Selected Answer: D
Went with D
upvoted 3 times
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asava
2 years, 1 month ago
Selected Answer: B
BQ is the serverless solution
upvoted 3 times
TornikePirveli
8 months, 2 weeks ago
But using dataproc is not serverless, so answer should be D
upvoted 1 times
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mellowed
2 years, 3 months ago
Correct option is D
upvoted 1 times
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ssaporylo
2 years, 3 months ago
Vote D
upvoted 1 times
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