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Databricks Certified Machine Learning Associate Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

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

Answer: C

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


NEW QUESTION # 61
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. spark_df.to_sql()
  • B. import pyspark.pandas as ps
    df = ps.to_pandas(spark_df)
  • C. import pandas as pd
    df = pd.DataFrame(spark_df)
  • D. import pyspark.pandas as ps
    df = ps.DataFrame(spark_df)
  • E. spark_df.to_pandas()

Answer: D

Explanation:
To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed to pandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.
Reference
Pandas API on Spark Documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html


NEW QUESTION # 62
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

  • A. Singular value decomposition
  • B. Spark ML cannot distribute linear regression training
  • C. Iterative optimization
  • D. Logistic regression
  • E. Least-squares method

Answer: C


NEW QUESTION # 63
A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

  • A. Replace the GridSearchCV operation with ParameterGrid
  • B. Replace the random_state=0 argument with random_state=1
  • C. Replace the GridSearchCV operation with RandomizedSearchCV
  • D. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
  • E. Replace the GridSearchCV operation with cross_validate

Answer: C

Explanation:
The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming. RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization


NEW QUESTION # 64
Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

  • A. Keras
  • B. PyTorch
  • C. Spark ML
  • D. Scikit-learn

Answer: C

Explanation:
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.
Reference:
Databricks documentation on Spark MLlib: Spark MLlib


NEW QUESTION # 65
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