Exam Name: Databricks Certified Machine Learning Associate Exam
Certification Provider: Databricks
Corresponding Certification: ML Data Scientist
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- Total Questions: 76
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- Total Questions: 76
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Choosing the right study material is the first exam decision that matters. TestKingFree's Databricks-Machine-Learning-Associate question bank is written by a team of IT experts and certified trainers with rich experience in the Databricks Certified Machine Learning Associate field — and verified answer by answer in 2026.
Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Databricks Machine Learning | 38% | - Databricks ML Environment and Capabilities
|
| Model Deployment | 12% | - Model Registry and Serving
|
| Model Development | 31% | - Data Preparation and Feature Engineering
|
| ML Workflows | 19% | - MLflow Tracking and Experimentation
|
Databricks-Machine-Learning-Associate Exam Facts for Busy Professionals
According to the latest exam information, the Databricks-Machine-Learning-Associate exam contains 48 questions and runs for 90 minutes minutes. Simulating that exact limit in practice builds the pacing a tough exam demands.
The Databricks Certified Machine Learning Associate blueprint centers on these domains:
- Databricks Machine Learning (38%)
- ML Workflows (19%)
- Model Deployment (12%)
Further domains complete the official outline — the question bank covers every one.
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Databricks states the following prerequisites for the Databricks Certified Machine Learning Associate: No formal prerequisites; 6+ months hands-on Databricks ML experience recommended.
Confirm the current requirements on the official certification page before you register.
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Databricks Certified Machine Learning Associate Sample Questions:
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?
- A. Scikit-learn
- B. Keras
- C. PyTorch
- D. Spark ML
Correct Answer: D 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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?
- A. spark_df[spark_df["price"] > 0]
- B. SELECT * FROM spark_df WHERE price > 0
- C. spark_df.loc[:,spark_df["price"] > 0]
- D. spark_df.loc[spark_df["price"] > 0,:]
- E. spark_df.filter(col("price") > 0)
Correct Answer: E 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?
- A. Autoscaling clusters
- B. Delta Lake
- C. Autoscaling clusters
- D. Spark ML
- E. MLflow Experiment Tracking
Correct Answer: D 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:
The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?
- A. They need to use Stringlndexer prior to one-hot encodinq the features.
- B. They need to specify the method parameter to the OneHotEncoder.
- C. They need to use VectorAssembler prior to one-hot encoding the features.
- D. They need to remove the line with the fit operation.
Correct Answer: A 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed the train_model function, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:
Which of the following pieces of code can be used to fill in the above blank to complete the task?
- A. groupedApplyIn
- B. applyInPandas
- C. predict
- D. mapInPandas
- E. train_model
Correct Answer: D 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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