Databricks Certified Machine Learning Professional
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Exam Code
Databricks-Machine-Learning-Professional
Exam Name
Databricks Certified Machine Learning Professional
Questions
60 Questions Answers With Explanation
Update Date
August 03, 2026
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What Is the Databricks-Machine-Learning-Professional Certification Exam?
The Databricks-Machine-Learning-Professional 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-Professional 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.
Beyond individual recognition, the ML Data Scientist certification often supports broader professional development. It can influence hiring decisions, contribute to internal advancement, or serve as a prerequisite for more specialized roles within the field. In many industries, certifications also help standardize expectations across organizations, making it easier for professionals to move between employers or sectors while carrying a credential that is widely understood and respected.
Who Should Take the Databricks-Machine-Learning-Professional Exam?
The Databricks-Machine-Learning-Professional 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.
Students preparing to enter the workforce may also pursue the Databricks-Machine-Learning-Professional exam as a way to strengthen their qualifications before graduating or applying for their first roles. In some fields, employers actively encourage or require staff to pursue this certification as part of ongoing professional development, particularly in industries where standards, safety, or compliance play a significant role in daily responsibilities.
Knowledge and Skills Evaluated in the Databricks Certified Machine Learning Professional
The Databricks Certified Machine Learning Professional is built to evaluate both foundational knowledge and the practical judgment needed to apply that knowledge in real situations. Candidates are generally expected to understand core principles and terminology relevant to their field, along with the reasoning behind established procedures, standards, or best practices.
Depending on the industry, this may include understanding regulatory requirements, following established protocols, applying analytical or technical methods, or exercising sound judgment in situations that require careful decision-making. Rather than testing isolated facts in a vacuum, the Databricks Certified Machine Learning Professional tends to reward candidates who can connect concepts to realistic scenarios, reflecting the kind of thinking expected in day-to-day professional practice.
Preparing for the Databricks-Machine-Learning-Professional certification exam becomes more effective when using high-quality and up-to-date study materials. MyCertsHub provides resources designed to help candidates build knowledge, practice consistently, and become familiar with the actual exam format.
How to Prepare for the Databricks-Machine-Learning-Professional Certification Exam?
Effective preparation for the Databricks-Machine-Learning-Professional certification exam usually begins with a clear understanding of the exam's objectives and structure. Reviewing official guidelines or documentation published by the certifying body provides the most accurate picture of what will be covered and how heavily different areas are weighted.
From there, many candidates benefit from building a structured study plan that breaks preparation into manageable sections over a set period of time. A well-organized Databricks-Machine-Learning-Professional Study Guide can help sequence this material logically, especially for those approaching a topic for the first time. Consistent review, paired with realistic practice, tends to produce better retention than concentrated last-minute studying.
Practical experience, where applicable to the field, also plays an important role in preparation. Working through Databricks-Machine-Learning-Professional Practice Questions and a Databricks-Machine-Learning-Professional practice test can help candidates identify gaps in their understanding and become familiar with the format and pacing of the actual exam. In fields where hands-on skill is assessed, supplementing study with real-world practice or supervised experience often makes the difference between recognizing correct information and genuinely understanding it.
Benefits of Earning the ML Data Scientist Certification
Successfully earning the ML Data Scientist certification offers benefits that extend well beyond passing a single exam. It provides documented proof of competence that can be referenced on a resume, professional profile, or internal performance review, offering a clear, third-party validation of skill and knowledge.
The credential can also strengthen professional credibility when working with clients, patients, stakeholders, or colleagues who may not be positioned to evaluate technical or specialized knowledge directly. Over time, this recognition often contributes to expanded career opportunities, whether through new responsibilities, higher-level roles, or eligibility for additional certifications that build on this foundational credential.
Prepare for the Databricks-Machine-Learning-Professional Exam with MyCertsHub
Preparing for the Databricks-Machine-Learning-Professional exam is a process that benefits from organized, consistent effort rather than rushed, last-minute review. MyCertsHub is designed to support that process by offering study resources, practice materials, and educational content that help candidates understand what the Databricks Certified Machine Learning Professional covers and how to approach their preparation thoughtfully.
Whether someone is just beginning to explore the ML Data Scientist or is in the final stages of reviewing material before their exam date, MyCertsHub aims to serve as a dependable resource throughout that journey. Every candidate's path to certification looks a little different, and the goal remains the same: to provide clear, genuinely useful information that supports real understanding of the subject matter.
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 # 4
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for
regression problems
A. F1 B. R-squared C. MAE D. MSE
Answer: A Explanation:
The code block provided by the machine learning engineer will perform the desired inference when
the Feature Store feature set was logged with the model at model_uri. This ensures that all
necessary feature transformations and metadata are available for the model to make predictions.
The Feature Store in Databricks allows for seamless integration of features and models, ensuring that
the required features are correctly used during inference.
Reference:
Databricks documentation on Feature Store: Feature Store in Databrick
Question # 5
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 # 8
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 # 9
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
Which of the following tools can be used to parallelize the hyperparameter tuning process for singlenode machine learning models using a Spark cluster?
A. MLflow Experiment Tracking B. Spark ML C. Autoscaling clusters D. Autoscaling clusters E. Delta Lake
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
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 # 11
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 # 12
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 # 14
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 # 15
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 # 16
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: 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.
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.
Question # 18
A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the
data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?
A. Single Node cluster B. Standard cluster C. SQL Warehouse D. None of these compute tools support this task
Answer: B Explanation:
For a data scientist using Spark SQL to import data and then performing machine learning tasks using
Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks
provides the necessary resources and scalability to handle large datasets and perform distributed
computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.
Reference:
Databricks documentation on clusters: Clusters in Databricks
Question # 19
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 # 20
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 # 21
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 # 22
A machine learning engineering team has a Job with three successive tasks. Each task runs a single
notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the
failure?
A. Run each notebook interactively B. Review the matrix view in the Job's runs C. Migrate the Job to a Delta Live Tables pipeline D. Change each Tasks setting to use a dedicated cluster
Answer: B Explanation:
To identify which task is causing the failure in the job, the team should review the matrix view in the
Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the
team to quickly identify which task failed. This approach is more efficient than running each
notebook interactively, as it provides immediate insights into the job's execution flow and any issues
that occurred during the run.
Reference:
Databricks documentation on Jobs: Jobs in Databricks
Question # 23
A new data scientist has started working on an existing machine learning project. The project is a
scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data
scientist has been tasked with improving the feature engineering of the pipelines preprocessing
stage. The data scientist wants to make necessary updates to the code that can be easily adopted
into the project without changing what is being run each day.
Which approach should the data scientist take to complete this task?
A. They can create a new branch in Databricks, commit their changes, and push those changes to the
Git provider B. They can clone the notebooks in the repository into a Databricks Workspace folder and make the
necessary changes. C. They can create a new Git repository, import it into Databricks, and copy and paste the existing
code from the original repository before making changes. D. They can clone the notebooks in the repository into a new Databricks Repo and make the
necessary changes
Answer: A Explanation:
The best approach for the data scientist to take in this scenario is to create a new branch in
Databricks, commit their changes, and push those changes to the Git provider. This approach allows
the data scientist to make updates and improvements to the feature engineering part of the
preprocessing pipeline without affecting the main codebase that runs daily. By creating a new
branch, they can work on their changes in isolation. Once the changes are ready and tested, they can
be merged back into the main branch through a pull request, ensuring a smooth integration process
and allowing for code review and collaboration with other team members.
Reference:
Databricks documentation on Git integration: Databricks Repos
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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