Free Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam Questions and Answers
61 verified practice questions for Databricks-Machine-Learning-Associate.
The first 10 questions on this page are free to read, answers included — no account and no card. A plan opens the rest of the bank, the full timed practice test and your weak-topic reporting.
Last updated: September 19, 2026
- Provider
- Databricks
- Questions in our bank
- 1000+
- Free to read
- First 10, with answers
- Our test mode duration & pass mark
- 130 mins · 70%
- Verified answers
- Reviewed weekly
- Practice format
- Multiple choice
Recommended: Switch to Test Mode to start a practice test that simulates the real exam experience.
Question #1
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?
Please select an optionIncorrectCorrect answer: C
Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next. This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process. References: • Hyperopt and TPE
Was this answer correct?Question #2
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrametrain_dfto train the model. The Spark DataFrametrain_dfhas the following schema: The machine learning engineer shares the following code block: Which of the following changes does the machine learning engineer need to make to complete the task?
Please select an optionIncorrectCorrect answer: B
In Spark ML, the linear regression model expects the feature column to be a vector type. However, if thefeaturescolumn in the DataFrametrain_dfis not already in this format (such as being a column of type UDT or a non-vectorized type), the engineerneeds to convert it to a vector column using a transformer likeVectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column. References • Spark MLlib documentation forLinearRegression:https://spark.apache.org/docs/latest/ml-classification- regression.html#linear-regression
Was this answer correct?Question #3
A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block: Which of the following is a negative consequence of includingpipelineas the estimator in the cross-validation process rather thanrfras the estimator?
Please select an optionIncorrectCorrect answer: A
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re- execution of these preprocessing steps, which can be computationally expensive. If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead. References: • Databricks documentation on cross-validation: Cross Validation
Was this answer correct?Question #4
A data scientist is using Spark ML to engineer features for an exploratory machine learning project. They decide they want to standardize their features using the following code block: 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?
Please select an optionIncorrectCorrect answer: E
To address the concern about standardizing features prior to splitting the data, the correct approach is to use the Pipeline API to ensure that only the training data's summary statistics are used to standardize the test data. This is achieved by fitting the StandardScaler (or any scaler) on the training data and then transforming both the training and test data using the fitted scaler. This approach prevents information leakage from the test data into the model training process and ensures that the model is evaluated fairly. References: • Best Practices in Preprocessing in Spark ML (Handling Data Splits and Feature Standardization).
Was this answer correct?Question #5
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
Please select an optionIncorrectCorrect answer: A
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. References: • Databricks documentation on Feature Store: Feature Store in Databricks
Was this answer correct?Question #6
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?
Please select an optionIncorrectCorrect answer: B
To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is: mlflow.register_model(f"runs:/{run_id}/model","best_model") This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem. References • MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model
Was this answer correct?Question #7
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?
Please select an optionIncorrectCorrect answer: A
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: Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0 =339.0=13.0 Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds. References: • Cross-validation in Regression (Understanding Cross-Validation Metrics).
Was this answer correct?Question #8
A data scientist has created a linear regression model that useslog(price)as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFramepreds_df. They are using the following code block to evaluate the model: regression_evaluator.setMetricName("rmse").evaluate(preds_df) Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable withprice?
Please select an optionIncorrectCorrect answer: D
When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error. References: • Databricks documentation on regression evaluation: Regression Evaluation
Was this answer correct?Question #9
Which statement describes a Spark ML transformer?
Please select an optionIncorrectCorrect answer: A
In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers likeStringIndexer,VectorAssembler, andStandardScaler. References: • Databricks documentation on transformers: Transformers in Spark ML
Was this answer correct?Question #10
A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed theapply_modelfunction that will look up and load the correct model for each group, and they want to apply it to each group of DataFramedf. They have written the following incomplete code block: Which piece of code can be used to fill in the above blank to complete the task?
Please select an optionIncorrectCorrect answer: A
To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use theapplyInPandasfunction. This function allows you to apply a Python function on each group of a DataFrame and return a DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance. prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) ) In this code: • groupby("device_id"): Groups the DataFrame by the "device_id" column. • applyInPandas(apply_model, schema=apply_return_schema): Applies theapply_modelfunction to each group and specifies the schema of the return DataFrame. References: • PySpark Pandas UDFs Documentation
Was this answer correct?
Continue with Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam
Unlock the full question bank
You have read the first 10 questions. A subscription opens every question in Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam, the full timed practice test, and your progress and weak-topic reporting.
Single exam
$19.99for 30 days
Full question bank and practice test for one exam, for 30 days.
Single exam
$49.99for 1 year
One exam for a full year. Nothing renews and nothing to cancel.
Full access
$39.99/mo
Every exam in the catalogue, month to month.
Full access
$199.99/yr
Every exam in the catalogue for a year.
Already subscribed? Sign in to pick up where you left off.
Other Databricks certifications
- Certified Machine Learning Associate (opens in a new tab)
- Certified Machine Learning Professional (opens in a new tab)
- Certified Data Engineer Associate (opens in a new tab)
- Certified Data Analyst Associate (opens in a new tab)
- Certified Associate Developer for Apache Spark (opens in a new tab)
- Certified Data Engineer Professional (opens in a new tab)
Reviews
★★★★★
This platform is a lifesaver. The practice questions and explanations are so detailed. It’s the best study tool I’ve ever used.
Hannah Smith
USA
★★★★★
I highly recommend Exam Practice. The feedback after each test helped me improve significantly, and I passed my exams easily.
Oscar Nyström
Sweden
★★★★★
Exam Practice is worth every penny. The mock exams are realistic, and the feedback helped me focus on key areas.
Amit Sharma
India
FAQ
Learn More: https://www.databricks.com/learn/training/certification
- Q1: What are Databricks Certification Exams?
- A: Databricks Certification Exams validate your expertise in using Databricks’ unified data analytics platform. These certifications demonstrate your proficiency in data engineering, data analysis, and machine learning, utilizing Databricks tools and the Apache Spark framework.
- Q2: Why should I pursue Databricks Certification?
- A: Databricks Certification enhances your professional credibility, showcasing your skills and knowledge in big data analytics and machine learning. This can lead to better job opportunities, higher salaries, and career advancement in data science, data engineering, and IT industries.
- Q3: What are the benefits of Databricks Certification?
- A: Benefits include recognition as a certified data professional, improved job performance, access to exclusive resources, continuing education opportunities, and staying current with the latest data analytics technologies and best practices.
- Q4: Who should take Databricks Certification Exams?
- A: Data engineers, data scientists, data analysts, machine learning practitioners, and anyone involved in processing and analyzing large datasets should consider these certifications to validate their expertise and advance their careers.
- Q5: What types of Databricks Certification Exams are available?
- A: Databricks offers various certification paths, including:
- Q6: How do I prepare for Databricks Certification Exams?
- A: Preparation can include official Databricks training courses, study guides, practice exams, online tutorials, and hands-on experience with Databricks tools and the Apache Spark framework.
- Q7: Where can I take Databricks Certification Exams?
- A: Databricks Certification Exams can be taken online, providing flexibility to fit your schedule and location.
- Q8: How do Databricks Certifications impact my career?
- A: Databricks Certifications significantly boost your career by demonstrating your expertise to employers, making you a more competitive candidate for advanced roles and promotions in data science, data engineering, and IT.
- Q9: Are there any prerequisites for Databricks Certification Exams?
- A: Some exams may have prerequisites, such as foundational knowledge or prior experience with Databricks and Apache Spark. Check the specific requirements for each certification path on the Databricks website.
- Q10: How often do I need to recertify for Databricks Certifications?
- A: Databricks Certifications typically require recertification every two years to ensure that certified professionals stay updated with the latest data analytics technologies and industry practices.