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Databricks Certified Machine Learning Associate Free Exam Questions

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1 / 45

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

When the features contain no missing values

When the features are of the boolean type

When the features contain no outliers

When the features are of the categorical type

When the features contain a lot of extreme outliers

2 / 45

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 the Feature Table. They now want to retrieve that metadata programmatically.
Which of the following lines of code will return the metadata description?

fs.get_table("new_table").description

fs.get_table("new_table")

fs.create_training_set("new_table")

There is no way to return the metadata description programmatically

fs.get_table("new_table").load_df()

3 / 45

A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?

Recall

Precision

RMSE

Accuracy

Area under the residual operating curve

4 / 45

A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?

Create a binary feature variable for each feature that contained missing values indicating whether each row’s value has been imputed

Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing

Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them

Impute the missing values using each respective feature variable’s mean value instead of the median value

Remove all feature variables that originally contained missing values from the feature set

5 / 45

A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model by comparing the label predictions to the actual price values, the data scientist notices that the 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?

The data scientist failed to take the log of the predictions in the first model prior to computing the RMSE

The second model is much more accurate than the first model

The RMSE is an invalid evaluation metric for regression problems

The first model is much more accurate than the second model

The data scientist failed to exponentiate the predictions in the second model prior to computing the RMSE

6 / 45

A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

The cross-validation process will no longer be parallelizable

The model will take longer to train for each unique combination of hyperparameter values

The feature engineering stages will be computed using validation data

The cross-validation process will no longer be reproducible

The model will be refit one more per cross-validation fold

7 / 45

A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.
Which of the following lines of code can the data scientist run to accomplish the task?

spark_df.printSchema()

spark_df.stats()

spark_df.toPandas()

spark_df.describe().head()

spark_df.summary ()

8 / 45

A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
• 10.0
• 12.0
• 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?

12.0

17.0

10.0

13.0

39.0

9 / 45

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.
Which of the following describes why?

Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step

Gradient boosting is not a linear algebra-based algorithm which is required for parallelization

Gradient boosting uses decision trees in each iteration which cannot be parallelized

Gradient boosting requires access to all data at once which cannot happen during parallelization

Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization

10 / 45

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?

Replace the GridSearchCV operation with ParameterGrid

Replace the GridSearchCV operation with cross_validate

Replace the GridSearchCV operation with RandomizedSearchCV

Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

Replace the random_state=0 argument with random_state=1

11 / 45

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

Pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

Pandas API on Spark DataFrames are unrelated to Spark DataFrames

Pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

Pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

Pandas API on Spark DataFrames are more performant than Spark DataFrames

12 / 45

What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

One-hot encoding

String indexing

Leave-one-out encoding

Categorical embeddings

Target encoding

13 / 45

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?

spark_df.loc[spark_df["price"] > 0,:]

SELECT * FROM spark_df WHERE price > 0

spark_df[spark_df["price"] > 0]

spark_df.loc[:,spark_df["price"] > 0]

spark_df.filter(col("price") > 0)

14 / 45

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

Replace the r2 return value with -r2

Replace the fmin operation with the fmax operation

Add test set validation process

Remove the mean operation that is wrapping the cross_val_score operation

Add a random_state argument to the RandomForestRegressor operation

15 / 45

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

Hyperopt

Delta Lake

MLflow Experiment Tracking

Autoscaling clusters

Spark ML

16 / 45

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?

They can refactor their notebook to use the PySpark DataFrame API

They can refactor their notebook to utilize the pandas API on Spark

They can refactor their notebook to process the data in parallel

They can refactor their notebook to use Spark SQL

They can refactor their notebook to use the Scala Dataset API

17 / 45

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms

One-hot encoding is not supported by most machine learning libraries

One-hot encoding is dependent on the target variable’s values which differ for each application

One-hot encoding is not a common strategy for representing categorical feature variables numerically

One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems

18 / 45

A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

predict(Iterator(spark_df))

predict(*spark_df.columns)

predict(spark_df.columns)

mapInPandas(predict(spark_df.columns))

mapInPandas(predict)

19 / 45

Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

DataFrame.where

TrainValidationSplit

DataFrame.randomSplit

TrainValidationSplitModel

CrossValidator

20 / 45

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

The vectorized pandas UDFs allow for pandas API use inside of the function

The vectorized pandas UDFs allow for the use of type hints

The vectorized pandas UDFs process data in memory rather than spilling to disk

The vectorized pandas UDFs work on distributed DataFrames

The vectorized pandas UDFs process data in batches rather than one row at a time

21 / 45

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.
Which of the following terms is used to describe this combination of models?

Ensemble learning

Stacking

Bucketing

Bootstrap aggregation

Support vector machines

22 / 45

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?

import pyspark.pandas as ps df = ps.to_pandas(spark_df)

spark_df.to_sql()

import pandas as pd df = pd.DataFrame(spark_df)

import pyspark.pandas as ps df = ps.DataFrame(spark_df)

spark_df.to_pandas()

23 / 45

Which of the following statements describes a Spark ML estimator?

An estimator is an evaluation tool to assess to the quality of a model

An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer

An estimator chains multiple algorithms together to specify an ML workflow

An estimator is a hyperparameter grid that can be used to train a model

An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions

24 / 45

A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

mlflow.register_model(f"runs:/{run_id}/best_model", "model")

millow.register_model(f"runs:/{run_id)/model")

mlflow.register_model(run_id, "best_model")

mlflow.register_model(f"runs:/{run_id}/model”, "best_model”)

25 / 45

Which of the following machine learning algorithms typically uses bagging?

Linear regression

K-means

Decision tree

Gradient boosted trees

Random forest

26 / 45

A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

The model version page in the MLflow Model Registry

The home page of the MLflow Model Registry

The model page in the MLflow Model Registry

The experiment page in the Experiments observatory

27 / 45

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?

Least-squares method

Spark ML cannot distribute linear regression training

Singular value decomposition

Logistic regression

Iterative optimization

28 / 45

A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

They can start each child run inside the parent run's indented code block using mlflow.start_run()

They can turn on Databricks Autologging

They can specify nested=True when starting the parent run for the tuning process

They can start each child run with the same experiment ID as the parent run

They can specify nested=True when starting the child run for each unique combination of hyperparameter values

29 / 45

A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters

They can set the runtime-version variable in their Spark session to “ml”

They can check the Databricks Runtime ML box when creating their clusters

They can add a line enabling Databricks Runtime ML in their init script when creating their clusters

30 / 45

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?

Spark ML decision trees test binned features values as representative split candidates

Spark ML decision trees test a random sample of feature variables in the splitting algorithm

Spark ML decision trees automatically prune overfit trees

Spark ML decision trees test every feature variable in the splitting algorithm

Spark ML decision trees test more split candidates in the splitting algorithm

31 / 45

Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

Click the “Source” link in the row corresponding to the run in the MLflow experiment page

Click the “Start Time” link in the row corresponding to the run in the MLflow experiment page

Click the “Models” link in the row corresponding to the run in the MLflow experiment page

Open the MLmodel artifact in the MLflow run page

32 / 45

A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

Model deployment

Exploratory data analysis

Model evaluation

Model tuning

33 / 45

A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task?

dbutils.data(spark_df).summarize()

dbutils.data.summarize (spark_df)

spark_df.describe()

spark_df.summary()

This task cannot be accomplished in a single line of code

34 / 45

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 pipeline’s 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?

They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes

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

They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes

They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider

35 / 45

A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:
Hyperparameter 1: [2, 5, 10]
Hyperparameter 2: [50, 100]
Which of the following represents the number of machine learning models that can be trained in parallel during this process?

36 / 45

A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df. batch_df has the following schema: customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:

In which situation will the machine learning engineer’s code block perform the desired inference?

When the model at model_uri only uses customer_id as a feature

When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark

When all of the features used by the model at model_uri are in a single Feature Store table

When the Feature Store feature set was logged with the model at model_uri

This code block will not perform the desired inference in any situation

37 / 45

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?

None of these compute tools support this task

SQL Warehouse

Single Node cluster

Standard cluster

38 / 45

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

MAE

MSE

R-squared

39 / 45

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?

Change each Task’s setting to use a dedicated cluster

Run each notebook interactively

Migrate the Job to a Delta Live Tables pipeline

Review the matrix view in the Job’s runs

40 / 45

A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column’s median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?

It does not fit the imputer on the data to create an ImputerModel

The fit method needs to be called instead of transform

It does not impute both the training and test data sets

The inputCols and outputCols need to be exactly the same

41 / 45

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?

Spark ML

Scikit-learn

pandas

PyTorch

Keras

42 / 45

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

Parallelize with Hyperopt

Implement MLflow Experiment Tracking

Enable autoscaling clusters

Scale up with Spark ML

43 / 45

A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference

When the new solution requires that each model computes a prediction for every record

When the new solution's models have an average size that is larger than the size of the original model

When the new solution requires if-else logic determining which model to use to compute each prediction

When the new solution's models have an average latency that is larger than the size of the original model

When the new solution requires the use of fewer feature variables than the original model

44 / 45

Which of the following changes do they need to make to the above code block in order to accomplish the task?

Remove the trials=trials argument

Reduce num_evals to be less than 10

Change fmin() to fmax()

Remove the algo=tpe.suggest argument

Change SparkTrials() to Trials()

45 / 45

Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.

Which of the following changes can the data scientist make to address the concern?

Utilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values

Utilize the Pipeline API to standardize the test data according to the training data's summary statisticscorrect

Utilize a cross-validation process rather than a train-test split process to remove the need for standardizing data

Utilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values

Utilize the Pipeline API to standardize the training data according to the test data's summary statistics

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Free Machine Learning Associate Practice Exam – 2025 Updated

Study Smarter for the Machine Learning Associate Exam with Our Free and Accurate Machine Learning Associate Exam Questions – Updated for 2025.

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