Databricks Machine Learning Associate Exam Questions 2025

Databricks-Machine-Learning-Associate Exam Certification Exam Guide

The Databricks-Machine-Learning-Associate exam tests your ability to perform basic machine learning tasks in Databricks.

This certification is for data science, machine learning engineering and data engineering professionals who want to prove their skills in building and testing machine learning models.

Passing the exam proves you can work with Spark ML, PySpark and scikit-learn in Databricks.

This exam covers data preparation to model evaluation and hyperparameter tuning.

Why Databricks Machine Learning

Databricks has become the go to platform for machine learning because of its scalability especially for big data.

With the recent acquisition of MosaicML, Databricks has improved its ability to handle large language models (LLMs) and generative AI, so it’s a popular choice for companies that need to build custom machine learning solutions.

The Databricks-Machine-Learning-Associate certification is in high demand in the job market as companies are moving towards machine learning workflows for insights and automation.

Data Preparation and Feature Engineering

In machine learning, handling missing values is part of data preparation. For example, a data scientist may want to impute missing values with each feature’s median value. But simply replacing missing values will lose information.

To retain as much information as possible, you can do:

Create a binary feature variable to indicate if a value is missing.

Impute the missing values with the respective feature variable’s median or mean value.

Create a constant feature variable that originally had missing values that shows the percentage of rows where the value was missing.

Or let the machine learning algorithm decide how to handle the missing values instead of imputing them manually.

Handling missing values and creating new feature variables will make your machine-learning models as accurate as possible.

Machine Learning Workflows and Model Evaluation

When working with machine learning workflows, we use Cross Validation to prevent data leakage and to ensure the model’s performance is good.

Cross-validation ensures the model generalizes well to new data by splitting the dataset into multiple parts for training and validation.

Using Databricks Model Registry allows you to manage, track and compare different classification and machine-learning models in a structured way.

When evaluating your classification model make sure to understand and interpret the following classification metrics:

• Accuracy
• Precision
• Recall
• F1-score

These classification metrics will tell you how well your model is classifying positive cases or predicting outcomes for your data.

Feature Store and Advanced Techniques

Databricks also has a Feature Store, a centralized store for feature variables used in machine learning. A machine learning engineer or data scientist can create and reuse feature sets across multiple models and make the model building process more efficient.

For example you can programmatically create a feature table and get summary statistics using Feature Store Client. This is useful when working with complex datasets that need consistent preprocessing across different models.

In advanced workflows you can:

• Pandas API on Spark for data manipulation
• Apache Arrow for faster data transfer between Spark DataFrames and pandas DataFrames

Distributed Machine Learning and Scaling

Databricks uses distributed computing to run machine learning at scale. For example, Spark ML allows you to train models on big data, distribute the workload across many machines. So your machine learning workflows will scale as your data grows.

One way to speed up training is parallelized hyperparameter tuning where multiple configurations are tested at the same time.

This will speed up the tuning process. Increasing the number of cores used in the training will further speed it up as long as the dataset fits in each core’s memory.

Databricks also has tools like HyperOpt that allows you to optimize hyperparameters for distributed and single-machine models like scikit-learn and TensorFlow.

Study Materials

To pass the Databricks-Machine-Learning-Associate exam you should be familiar with Python and SQL as these are heavily used in the exam. The exam has 45 multiple-choice questions and you have 90 minutes to complete it.

Read the official exam guide thoroughly and take several mock exams to get familiar with the types of questions you will be asked. Test questions cover:

• Data preparation
• Feature engineering
• Classification metrics
• Cross-validation
• Model evaluation

FAQs

What is Databricks-Machine-Learning-Associate exam?

The exam tests your ability to perform machine learning tasks within the Databricks platform like data preparation, model building and evaluation.

What are the prerequisites for the exam?

None, but recommended 6 months of experience in machine learning on Databricks.

What is the format of the exam?

45 multiple-choice questions, 90 minutes.

How to prepare for the exam?

Read the exam guide, take mock exams and use Cert Empire exam dumps for targeted preparation.

Is the exam open book or open internet?

No, the exam is not open book or open internet. It’s a proctored exam and you will be monitored during the test to ensure the integrity of the exam process.

Can I retake the exam if I don’t pass on the first attempt?

Yes, you can retake the exam if you don’t pass. But you may have to wait for a specific period and there could be a retake fee.

What is the passing score for the Databricks-Machine-Learning-Associate exam?

Databricks doesn’t disclose the passing score. But aiming for at least 70-75% correct answers is recommended to pass.

How much does the Databricks-Machine-Learning-Associate exam cost?

Databricks-Machine-Learning-Associate exam costs USD 200. This fee is required at the time of exam registration.

How long is the Databricks-Machine-Learning-Associate certification valid?

2 years. After that you will need to recertify to maintain your certification.

What programming languages will be used in the exam?

The exam code will be in Python but some SQL will be used for data manipulation. You should be familiar with both.

Is there any official training for the Databricks-Machine-Learning-Associate exam?

Yes, Databricks offers various training courses and learning paths to help you prepare for the exam. It’s recommended to take at least one course if you are new to Databricks Machine Learning.

Are there any mock exams available for Databricks-Machine-Learning-Associate?

You can find mock exams and practice questions on several platforms including Cert Empire. These simulate the exam experience and help you focus on key areas.

Can I use Databricks Community Edition to prepare for the exam?

Databricks Community Edition is a great environment to practice machine learning tasks. It’s an excellent tool to get hands on experience with Databricks and prepare for the exam.

How to reschedule or cancel the exam?

You can reschedule or cancel the exam through the exam portal where you registered. Check Databricks’ cancellation policy as there might be penalty for last minute cancellations.

Can I take the exam remotely?

Yes, the exam is online and proctored remotely. You can take the exam from home or office as long as you meet the technical requirements for remote proctoring.

How do I get my Databricks-Machine-Learning-Associate certification after passing the exam?

Once you pass the exam you will receive an electronic certificate from Databricks. You can add it to your resume and share it on professional platforms like LinkedIn.