DSA-C02 Exam Dumps - SnowPro Advanced: Data Scientist Certification Exam

Explanation

By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

Explanation

As a consumer, you can do the following:

· Discover and test third-party data sources.

· Receive frictionless access to raw data products from vendors.

· Combine new datasets with your existing data in Snowflake to derive new business insights.

· Have datasets available instantly and updated continually for users.

· Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.

· Use the business intelligence (BI) tools of your choice.

Explanation

Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.

Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:

The potential for better performance if your Python code operates efficiently on batches of rows.

Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.

When you use vectorized Python UDFs:

You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.

As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.

Explanation

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model’s predictions.