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Databricks Databricks-Machine-Learning-Associate PDF Questions

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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

Topic 2

ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

Topic 3

Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.

Topic 4

Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
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 memory rather than spilling to disk
D. The vectorized pandas UDFs process data in batches rather than one row at a time
E. The vectorized pandas UDFs allow for the use of type hints

Answer: D

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 # 76
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?

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

Answer: A

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

NEW QUESTION # 77
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

A. Grid Search
B. Halving Random Search
C. Random Search
D. Tree of Parzen Estimators

Answer: D

Explanation:
Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next.
This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.
Reference:
Hyperopt and TPE

NEW QUESTION # 78
A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

A. Spark ML decision trees test a random sample of feature variables in the splitting algorithm
B. Spark ML decision trees test more split candidates in the splitting algorithm
C. Spark ML decision trees test binned features values as representative split candidates
D. Spark ML decision trees test every feature variable in the splitting algorithm
E. Spark ML decision trees automatically prune overfit trees

Answer: C

Explanation:
One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.
Reference:
Spark MLlib Documentation (Decision Trees and Quantile Binning).

NEW QUESTION # 79
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. Delta Lake
B. Spark ML
C. Autoscaling clusters
D. MLflow Experiment Tracking
E. Autoscaling clusters

Answer: B

Explanation:
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

NEW QUESTION # 80
......

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