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Question 10 - Professional Machine Learning Engineer discussion

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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
Answers
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.
Answers
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
Answers
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
Answers
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
Suggested answer: D

Explanation:

BigQuery is a serverless, scalable, and cost-effective data warehouse that allows users to run SQL queries on large volumes of data. BigQuery Load is a tool that can ingest data from Cloud Storage into BigQuery tables. BigQuery SQL is a dialect of SQL that supports many of the same functions and operations as PySpark, such as window functions, aggregate functions, joins, and subqueries. By using BigQuery Load and BigQuery SQL, you can rebuild your ML pipeline for structured data on Google Cloud without having to manage any servers or clusters, and with faster performance and lower cost than using PySpark on Dataproc. You can also use BigQuery ML to create and evaluate ML models using SQL commands.Reference:

BigQuery documentation

BigQuery Load documentation

BigQuery SQL reference

BigQuery ML documentation

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asked 18/09/2024
Dennis Spring
45 questions
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