Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark’s distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets.

References:

• Databricks documentation on Spark MLlib: Spark MLlib

To filter rows in a Spark DataFrame based on a condition, you use thefiltermethod along with a column condition. The correct syntax in PySpark to accomplish this task isspark_df.filter(col("price") > 0), which filters the DataFrame to include only those rows where the value in the "price" column is greater than 0. Thecolfunction is used to specify column-based operations. The other options provided either do not use correct Spark DataFrame syntax or are intended for different types of data manipulation frameworks like pandas.References:

• PySpark DataFrame API documentation (Filtering DataFrames).

To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.

Correct code:

dbutils.data.summarize(spark_df)

Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.

References:

• Databricks Utilities Documentation