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Databricks Databricks-Machine-Learning-Associate Test Dumps

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Question # 1



A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API. Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?
A. import pyspark.pandas as ps
df = ps.DataFrame(spark_df)
B. import pyspark.pandas as ps
df = ps.to_pandas(spark_df)
C. spark_df.to_sql()
D. import pandas as pd
df = pd.DataFrame(spark_df)
E. spark_df.to_pandas()



A.
  import pyspark.pandas as ps
df = ps.DataFrame(spark_df)





Question # 2



Which of the following statements describes a Spark ML estimator?
A. An estimator is a hyperparameter grid that can be used to train a model
B. An estimator chains multiple algorithms together to specify an ML workflow
C. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
D. An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
E. An estimator is an evaluation tool to assess to the quality of a model



D.
  An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer





Question # 3



Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?
A. The vectorized pandas UDFs allow for the use of type hints
B. The vectorized pandas UDFs process data in batches rather than one row at a time
C. The vectorized pandas UDFs allow for pandas API use inside of the function
D. The vectorized pandas UDFs work on distributed DataFrames
E. The vectorized pandas UDFs process data in memory rather than spilling to disk



B.
  The vectorized pandas UDFs process data in batches rather than one row at a time





Question # 4



A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process. Which of the following feature engineering tasks will be the least efficient to distribute?
A. One-hot encoding categorical features
B. Target encoding categorical features
C. Imputing missing feature values with the mean
D. Imputing missing feature values with the true median
E. Creating binary indicator features for missing values



D.
  Imputing missing feature values with the true median





Question # 5



Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?
A. TrainValidationSplit
B. DataFrame.where
C. CrossValidator
D. TrainValidationSplitModel
E. DataFrame.randomSplit



E.
  DataFrame.randomSplit





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Databricks Certified Machine Learning Associate Exam Dumps


Exam Code: Databricks-Machine-Learning-Associate
Exam Name: Databricks Certified Machine Learning Associate

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