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What Is the Databricks-Machine-Learning-Associate Certification Exam?
The Databricks-Machine-Learning-Associate certification exam is a standardized assessment designed to measure a candidate's knowledge, competencies, and practical understanding within a defined professional field. It serves as the primary requirement for earning the ML Data Scientist, a credential that represents a recognized level of proficiency in its respective industry. Depending on the field, this may involve theoretical knowledge, applied problem-solving, regulatory understanding, or hands-on procedural competence.
The exam is typically developed and maintained by an accrediting body or professional organization that sets the standards for the ML Data Scientist. This ensures that anyone who earns the credential has met a consistent benchmark, regardless of where they studied or gained their experience. For many professionals, the Databricks-Machine-Learning-Associate Certification Exam represents a formal checkpoint in their career, one that confirms readiness to take on greater responsibility within their chosen field.
Why the ML Data Scientist Certification Matters?
Certifications like the ML Data Scientist exist because industries need a reliable way to verify competence beyond a resume or a job title. Earning this credential signals to employers, clients, and colleagues that a professional has invested time in building a structured foundation of knowledge and has been evaluated against an established standard.
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The Databricks-Machine-Learning-Associate exam is generally relevant to individuals who are either entering a field or looking to formalize skills they have already developed through experience. This can include early-career professionals seeking a credential to support their first steps into the industry, as well as experienced practitioners who want official recognition of knowledge gained on the job.
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A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice thatthey are receiving different results despite all of their data and manually specified hyperparametervalues being identical.Which of the following describes a reason that the single-node sklearn decision tree and the SparkML decision tree can differ?
A. Spark ML decision trees test every feature variable in the splitting algorithm B. Spark ML decision trees automatically prune overfit trees C. Spark ML decision trees test more split candidates in the splitting algorithm D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm E. Spark ML decision trees test binned features values as representative split candidates
Answer: E
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).
Question # 2
The implementation of linear regression in Spark ML first attempts to solve the linear regressionproblem using matrix decomposition, but this method does not scale well to large datasets with alarge number of variables.Which of the following approaches does Spark ML use to distribute the training of a linear regressionmodel for large data?
A. Logistic regression B. Spark ML cannot distribute linear regression training C. Iterative optimization D. Least-squares method E. Singular value decomposition
Answer: C
Explanation:
For large datasets with many variables, Spark ML distributes the training of a linear regression model
using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient
Descent or L-BFGS (Limited-memory Broyden“Fletcher“Goldfarb“Shanno) to iteratively minimize the
loss function. These iterative methods are suitable for distributed computing environments and can
handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing
parallel updates.
Reference:
Spark MLlib Documentation (Linear Regression with Iterative Optimization).
Question # 3
Which of the following machine learning algorithms typically uses bagging?
A. Gradient boosted trees B. K-means C. Random forest D. Linear regression E. Decision tree
Answer: C
Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating).
Bagging involves training multiple models independently on different random subsets of the data
and then combining their predictions. Random Forests consist of many decision trees trained on
random subsets of the training data and features, and their predictions are averaged to improve
accuracy and control overfitting. This method enhances model robustness and predictive
performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).
Question # 4
A data scientist has produced two models for a single machine learning problem. One of the modelsperforms well when one of the features has a value of less than 5, and the other model performswell when the value of that feature is greater than or equal to 5. The data scientist decides tocombine the two models into a single machine learning solution.Which of the following terms is used to describe this combination of models?
A. Bootstrap aggregation B. Support vector machines C. Bucketing D. Ensemble learning E. Stacking
Answer: D
Explanation:
Ensemble learning is a machine learning technique that involves combining several models to solve a
particular problem. The scenario described fits the concept of ensemble learning, where two
models, each performing well under different conditions, are combined to create a more robust
model. This approach often leads to better performance as it combines the strengths of multiple
A data scientist has been given an incomplete notebook from the data engineering team. Thenotebook uses a Spark DataFrame spark_df on which the data scientist needs to perform furtherfeature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrameAPI.Which of the following blocks of code can the data scientist run to be able to use the pandas API onSpark?
A. import pyspark.pandas as psdf = ps.DataFrame(spark_df) B. import pyspark.pandas as psdf = ps.to_pandas(spark_df) C. spark_df.to_sql() D. import pandas as pddf = pd.DataFrame(spark_df) E. spark_df.to_pandas()
Answer: A
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 pandasonSpark 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
Which of the following statements describes a Spark ML estimator?
A. An estimator is a hyperparameter arid that can be used to train a model B. An estimator chains multiple alqorithms toqether to specify an ML workflow C. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame withpredictions D. An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer E. An estimator is an evaluation tool to assess to the quality of a model
Answer: D
Explanation:
In the context of Spark MLlib, an estimator refers to an algorithm which can be "fit" on a DataFrame
to produce a model (referred to as a Transformer), which can then be used to transform one
DataFrame into another, typically adding predictions or model scores. This is a fundamental concept
in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to
Which of the following tools can be used to distribute large-scale feature engineering without theuse of a UDF or pandas Function API for machine learning pipelines?
A. Keras B. pandas C. PvTorch D. Spark ML E. Scikit-learn
Answer: D
Explanation:
Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing
and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale
feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function
API, allowing for more scalable and efficient data transformations directly distributed across a Spark
cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed
environment suitable for big data scenarios.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).
Question # 8
Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySparkUDFs?
A. The vectorized pandas UDFs allow for the use of type hints B. The vectorized pandas UDFs process data in batches rather than one row at a time C. The vectorized pandas UDFs allow for pandas API use inside of the function D. The vectorized pandas UDFs work on distributed DataFrames E. The vectorized pandas UDFs process data in memory rather than spilling to disk
Answer: 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.
A machine learning engineer is trying to scale a machine learning pipeline by distributing its featureengineering 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
Answer: 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
Which of the Spark operations can be used to randomly split a Spark DataFrame into a trainingDataFrame and a test DataFrame for downstream use?
A. TrainValidationSplit B. DataFrame.where C. CrossValidator D. TrainValidationSplitModel E. DataFrame.randomSplit
Answer: E
Explanation:
The correct method to randomly split a Spark DataFrame into training and test sets is by using the
randomSplit method. This method allows you to specify the proportions for the split as a list of
weights and returns multiple DataFrames according to those weights. This is directly intended for
splitting DataFrames randomly and is the appropriate choice for preparing data for training and
testing in machine learning workflows.
Reference:
Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).
Question # 11
A data scientist has written a data cleaning notebook that utilizes the pandas library, but theircolleague 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 timerefactoring 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.
Answer: E
Explanation:
The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as
pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing
pandas-based code while scaling to handle big data using Spark's distributed computing capabilities.
pandas on Spark provides a similar API to pandas, making the transition smoother and faster
compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or
Spark SQL.
Reference:
Databricks documentation on pandas API on Spark (formerly Koalas)
Question # 12
Which of the following describes the relationship between native Spark DataFrames and pandas APIon Spark DataFrames?
A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additionalmetadata B. pandas API on Spark DataFrames are more performant than Spark DataFrames C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames E. pandas API on Spark DataFrames are unrelated to Spark DataFrames
Answer: C
Explanation:
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark.
It allows users to perform pandas operations on large datasets using Spark's distributed compute
capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling
operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and
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. Heres 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 clusters 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 # 14
A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up thetraining process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.Which of the following describes why?
A. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization B. Gradient boosting requires access to all data at once which cannot happen during parallelization. C. Gradient boosting calculates gradients in evaluation metrics using all cores which preventsparallelization. D. Gradient boosting is an iterative algorithm that requires information from the previous iterationto perform the next step.
Answer: D
Explanation:
Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the
errors of the previous ones. This sequential dependency makes it difficult to parallelize the training
of trees in gradient boosting, as each step relies on the results from the preceding step.
Parallelization in this context would undermine the core methodology of the algorithm, which
depends on sequentially improving the model's performance with each iteration.
Reference:
Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).
Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each
new model corrects the errors made by the previous ones. This sequential dependency means that
each iteration requires the results of the previous iteration to make corrections. Here is a step-bystep
explanation of why this makes parallelization challenging:
Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the
residual errors of the previous trees. This requires the model to complete one iteration before
starting the next.
Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions
made by the previous models. Therefore, the model must wait until the previous tree has been fully
trained and evaluated before starting to train the next tree.
Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training
process. Unlike algorithms that process data independently in each step (e.g., random forests),
gradient boosting cannot easily distribute the work across multiple processors or cores for
simultaneous execution.
This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize
effectively.
Reference
Gradient Boosting Machine Learning Algorithm
Understanding Gradient Boosting Machines
Question # 15
What is the name of the method that transforms categorical features into a series of binary indicatorfeature variables?
A. Leave-one-out encoding B. Target encoding C. One-hot encoding D. Categorical E. String indexing
Answer: C
Explanation:
The method that transforms categorical features into a series of binary indicator variables is known
as one-hot encoding. This technique converts each categorical value into a new binary column, which
is essential for models that require numerical input. One-hot encoding is widely used because it
helps to handle categorical data without introducing a false ordinal relationship among categories.
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regressionproblem. The following root-mean-squared-error values are calculated on each of the validationfolds:10.012.017.0Which of the following values represents the overall cross-validation root-mean-squared error?
A. 13.0 B. 17.0 C. 12.0 D. 39.0 E. 10.0
Answer: A
Explanation:
To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE
values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the
three folds, the overall cross-validation RMSE is calculated as the average of these three values:
Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.
Reference:
Cross-validation in Regression (Understanding Cross-Validation Metrics).
Question # 17
A data scientist has created two linear regression models. The first model uses price as a labelvariable and the second model uses log(price) as a label variable. When evaluating the RMSE of eachmodel by comparing the label predictions to the actual price values, the data scientist notices thatthe RMSE for the second model is much larger than the RMSE of the first model.Which of the following possible explanations for this difference is invalid?
A. The second model is much more accurate than the first model B. The data scientist failed to exponentiate the predictions in the second model prior to computingthe RMSE C. The data scientist failed to take the log of the predictions in the first model prior to computing theRMSE D. The first model is much more accurate than the second model E. The RMSE is an invalid evaluation metric for regression problems
Answer: E
Explanation:
The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the
accuracy of regression models. The statement that it is invalid is incorrect. Heres a breakdown of
Why the other statements are or are not valid:
Transformations and RMSE Calculation: If the model predictions were transformed (e.g., using log),
they should be converted back to their original scale before calculating RMSE to ensure accuracy in
the evaluation. Missteps in this conversion process can lead to misleading RMSE values.
Accuracy of Models: Without additional information, we can't definitively say which model is more
accurate without considering their RMSE values properly scaled back to the original price scale.
Appropriateness of RMSE: RMSE is entirely valid for regression problems as it provides a measure of
how accurately a model predicts the outcome, expressed in the same units as the dependent
variable.
Reference
"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the
chapters discussing model evaluation metrics.
Question # 18
An organization is developing a feature repository and is electing to one-hot encode all categoricalfeature variables. A data scientist suggests that the categorical feature variables should not be onehotencoded within the feature repository.Which of the following explanations justifies this suggestion?
A. One-hot encoding is not supported by most machine learning libraries. B. One-hot encoding is dependent on the target variable's values which differ for each application. C. One-hot encoding is computationally intensive and should only be performed on small samples oftraining sets for individual machine learning problems. D. One-hot encoding is not a common strategy for representing categorical feature variablesnumerically. E. One-hot encoding is a potentially problematic categorical variable strategy for some machinelearning algorithms.
Answer: E
Explanation:
One-hot encoding transforms categorical variables into a format that can be provided to machine
learning algorithms to better predict the output. However, when done prematurely or universally
within a feature repository, it can be problematic:
Dimensionality Increase: One-hot encoding significantly increases the feature space, especially with
high cardinality features, which can lead to high memory consumption and slower computation.
Model Specificity: Some models handle categorical variables natively (like decision trees and
boosting algorithms), and premature one-hot encoding can lead to inefficiency and loss of
information (e.g., ordinal relationships).
Sparse Matrix Issue: It often results in a sparse matrix where most values are zero, which can be
inefficient in both storage and computation for some algorithms.
Generalization vs. Specificity: Encoding should ideally be tailored to specific models and use cases
rather than applied generally in a feature repository.
Reference
"Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and
Kjell Johnson (CRC Press, 2019).
Question # 19
A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The datascientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartilerange (IQR) for each numerical feature.Which of the following lines of code can the data scientist run to accomplish the task?
A. spark_df.summary () B. spark_df.stats() C. spark_df.describe().head() D. spark_df.printSchema() E. spark_df.toPandas()
Answer: A
Explanation:
The summary() function in PySpark's DataFrame API provides descriptive statistics which include
count, mean, standard deviation, min, max, and quantiles for numeric columns. Here are the steps
on how it can be used:
Import PySpark: Ensure PySpark is installed and correctly configured in the Databricks environment.
Load Data: Load the data into a Spark DataFrame.
Apply Summary: Use spark_df.summary() to generate summary statistics.
View Results: The output from the summary() function includes the statistics specified in the query
(count, mean, standard deviation, min, max, and potentially quartiles which approximate the
A data scientist has replaced missing values in their feature set with each respective featurevariables median value. A colleague suggests that the data scientist is throwing away valuableinformation by doing this.Which of the following approaches can they take to include as much information as possible in thefeature set?
A. Impute the missing values using each respective feature variable's mean value instead of the median value B. Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them C. Remove all feature variables that originally contained missing values from the feature set D. Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed E. Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing
Answer: D
Explanation:
By creating a binary feature variable for each feature with missing values to indicate whether a value
has been imputed, the data scientist can preserve information about the original state of the data.
This approach maintains the integrity of the dataset by marking which values are original and which
are synthetic (imputed). Here are the steps to implement this approach:
Identify Missing Values: Determine which features contain missing values.
Impute Missing Values: Continue with median imputation or choose another method (mean, mode,
regression, etc.) to fill missing values.
Create Indicator Variables: For each feature that had missing values, add a new binary feature. This
feature should be '1' if the original value was missing and imputed, and '0' otherwise.
Data Integration: Integrate these new binary features into the existing dataset. This maintains a
record of where data imputation occurred, allowing models to potentially weight these observations
differently.
Model Adjustment: Adjust machine learning models to account for these new features, which might
involve considering interactions between these binary indicators and other features.
Reference
"Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media,
2018), especially the sections on handling missing data.
In which of the following situations is it preferable to impute missing feature values with theirmedian value over the mean value?
A. When the features are of the categorical type B. When the features are of the boolean type C. When the features contain a lot of extreme outliers D. When the features contain no outliers E. When the features contain no missing no values
Answer: C Explanation:
Imputing missing values with the median is often preferred over the mean in scenarios where the
data contains a lot of extreme outliers. The median is a more robust measure of central tendency in
such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that
the imputed values are more representative of the typical data point, thus preserving the integrity of
the dataset's distribution. The other options are not specifically relevant to the question of handling
outliers in numerical data.
Reference:
Data Imputation Techniques (Dealing with Outliers).
Question # 22
A health organization is developing a classification model to determine whether or not a patientcurrently has a specific type of infection. The organization's leaders want to maximize the number ofpositive cases identified by the model.Which of the following classification metrics should be used to evaluate the model?
A. RMSE B. Precision C. Area under the residual operating curve D. Accuracy E. Recall
Answer: E
Explanation:
When the goal is to maximize the identification of positive cases in a classification task, the metric of
interest is Recall. Recall, also known as sensitivity, measures the proportion of actual positives that
are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where
missing a positive case (false negative) has serious implications, such as in medical diagnostics. The
other metrics like Precision, RMSE, and Accuracy serve different aspects of performance
measurement and are not specifically focused on maximizing the detection of positive cases alone.
Reference:
Classification Metrics in Machine Learning (Understanding Recall).
Question # 23
A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame thatcontains 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]
Answer: B
Explanation:
To filter rows in a Spark DataFrame based on a condition, you use the filter method along with a
column condition. The correct syntax in PySpark to accomplish this task is spark_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. The col function 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.
Reference:
PySpark DataFrame API documentation (Filtering DataFrames).
Question # 24
A machine learning engineer has created a Feature Table new_table using Feature Store Client fs.When creating the table, they specified a metadata description with key information about theFeature Table. They now want to retrieve that metadata programmatically.Which of the following lines of code will return the metadata description?
A. There is no way to return the metadata description programmatically. B. fs.create_training_set("new_table") C. fs.get_table("new_table").description D. fs.get_table("new_table").load_df() E. fs.get_table("new_table")
Answer: C
Explanation:
To retrieve the metadata description of a feature table created using the Feature Store Client
(referred here as fs), the correct method involves calling get_table on the fs client with the table
name as an argument, followed by accessing the description attribute of the returned object. The
code snippet fs.get_table("new_table").description correctly achieves this by fetching the table
object for "new_table" and then accessing its description attribute, where the metadata is stored.
The other options do not correctly focus on retrieving the metadata description.
Reference:
Databricks Feature Store documentation (Accessing Feature Table Metadata).
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Hector Morgan
Karim Padmanabhan
Corentin Clement
Addison Boucher
Piper Phillips