Hello Databricks Community,
While preparing for the Databricks Machine Learning Associate certification, I came across a question in a practice assessment that I believe may have an incorrect or incomplete answer key.
Question:
"In a Spark ML Pipeline, which two of the following are Estimators — meaning they learn parameters from the training data during fit()? (Select two)"
Options:
OneHotEncoder
VectorAssembler
Imputer
StandardScaler
My selected answers:
OneHotEncoder
StandardScaler
Assessment result: Partially correct — 2.5 out of 5 marks.
Answers provided by the assessment:
My understanding:
According to the Apache Spark ML API, three of the listed components are Estimators:
OneHotEncoder: An Estimator that learns category sizes during fit() and produces a OneHotEncoderModel.
Imputer: An Estimator that learns imputation statistics and produces an ImputerModel.
StandardScaler: An Estimator that learns feature statistics and produces a StandardScalerModel.
VectorAssembler is a Transformer because it combines feature columns without learning parameters through fit().
Therefore, technically, the question appears to have three correct answers rather than two.
Could someone from the Databricks training or certification team clarify whether the question or its answer key needs correction?
I would appreciate clarification so that learners can align their understanding with the actual Spark ML API.
Thank you!