Free Access to Databricks.Databricks-Machine-Learning-Associate.v2025-05-09.q25 with Valid Practice Test (Page 6)

Question 21

Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

A.MLflow Experiment Tracking
B.Spark ML
C.Autoscaling clusters
D.Autoscaling clusters
E.Delta Lake

Correct Answer: B

Spark ML (part of Apache Spark's MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark.
Reference
Apache Spark MLlib Guide: https://spark.apache.org/docs/latest/ml-guide.html Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here's a detailed explanation of how Spark ML can be used:
Hyperparameter Tuning with CrossValidator: Spark ML includes the CrossValidator and TrainValidationSplit classes, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster.
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator
# Define the model
model = ...
# Create a parameter grid
paramGrid = ParamGridBuilder() \
.addGrid(model.hyperparam1, [value1, value2]) \
.addGrid(model.hyperparam2, [value3, value4]) \
.build()
# Define the evaluator
evaluator = BinaryClassificationEvaluator()
# Define the CrossValidator
crossval = CrossValidator(estimator=model,
estimatorParamMaps=paramGrid,
evaluator=evaluator,
numFolds=3)
Parallel Execution: Spark distributes the tasks of training models with different hyperparameters across the cluster's nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously.
Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations.
Reference
Apache Spark MLlib Documentation
Hyperparameter Tuning in Spark ML

Question 22

A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A.They can refactor their notebook to process the data in parallel.
B.They can refactor their notebook to use the PySpark DataFrame API.
C.They can refactor their notebook to use the Scala Dataset API.
D.They can refactor their notebook to use Spark SQL.
E.They can refactor their notebook to utilize the pandas API on Spark.

Question 23

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0.
Which of the following code blocks will accomplish this task?

A.spark_df[spark_df["price"] > 0]
B.spark_df.filter(col("price") > 0)
C.SELECT * FROM spark_df WHERE price > 0
D.spark_df.loc[spark_df["price"] > 0,:]
E.spark_df.loc[:,spark_df["price"] > 0]

Question 24

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

A.The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
B.The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
C.The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.
D.The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.

Question 25

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

Question 21

Question 22

Question 23

Question 24

Question 25

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