You Are Building a Data Pipeline on Google Cloud

question 149

Multiple Choice

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A) Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
• B) Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
• C) Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
• D) Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.

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Definitions:

P-value

The likelihood of seeing test outcomes that are as extreme or more so than the actually observed results, assuming the null hypothesis is true.

Negative Value

A number that is less than zero, representing a decrease, loss, or position below a starting point.

Positive Value

A numerical value greater than zero, representing quantities that have magnitude and direction.

Null Hypothesis

A statement asserting there is no effect or no difference, serving as the default or starting assumption in hypothesis testing.