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Free Databricks-Machine-Learning-Associate Exam Braindumps (page: 6)

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QUESTION: 21

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?

One-hot encoding categorical features
Target encoding categorical features
Imputing missing feature values with the mean
Imputing missing feature values with the true median
Creating binary indicator features for missing values

Answer(s): D

Explanation:

Among the options listed, calculating the true median for imputing missing feature values is the least efficient to distribute. This is because the true median requires knowledge of the entire data distribution, which can be computationally expensive in a distributed environment. Unlike mean or mode, finding the median requires sorting the data or maintaining a full distribution, which is more intensive and often requires shuffling the data across partitions.
Reference
Challenges in parallel processing and distributed computing for data aggregation like median calculation: https://www.apache.org

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QUESTION: 22

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

The vectorized pandas UDFs allow for the use of type hints
The vectorized pandas UDFs process data in batches rather than one row at a time
The vectorized pandas UDFs allow for pandas API use inside of the function
The vectorized pandas UDFs work on distributed DataFrames
The vectorized pandas UDFs process data in memory rather than spilling to disk

Answer(s): B

Explanation:

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much more efficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.
Reference
PySpark Documentation on UDFs:
https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs- a-k-a-vectorized-udfs

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QUESTION: 23

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

Change SparkTrials() to Trials()
Reduce num_evals to be less than 10
Change fmin() to fmax()
Remove the trials=trials argument
Remove the algo=tpe.suggest argument

Answer(s): A

Explanation:

The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.

Reference
Hyperopt documentation: http://hyperopt.github.io/hyperopt/

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QUESTION: 24

A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.

The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

They need to call the transform method on train df
They need to convert the features column to be a vector
They do not need to make any changes
They need to utilize a Pipeline to fit the model
They need to split the features column out into one column for each feature

Answer(s): B

Explanation:

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.
Reference
Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml- classification-regression.html#linear-regression

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