Vendor: Databricks
Exam Code: DATABRICKS-MACHINE-LEARNING-ASSOCIATE
Exam Name: Databricks Certified Machine Learning Associate
Certification: Databricks Certifications
Total Questions: 74 Q&A
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Updated on: Jun 12, 2026
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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?
A. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.
B. They can check the Databricks Runtime ML box when creating their clusters.
C. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
D. They can set the runtime-version variable in their Spark session to "ml".
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?
A. Theycan turn on Databricks Autologging
B. Theycan specify nested=True when startingthe child run for each unique combination of hyperparameter values
C. Theycan start each child run inside the parentrun's indented code block usingmlflow.start runO
D. They can start each child run with the same experiment ID as the parent run
E. They can specify nested=True when starting the parent run for the tuningprocess
A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the pathmodel_urifor the DataFramebatch_df.
batch_dfhas the following schema:
customer_id STRING
The machine learning engineer runs the following code block to perform inference onbatch_dfusing the linear regression model atmodel_uri:

In which situation will the machine learning engineer's code block perform the desired inference?
A. When the Feature Store feature set was logged with the model at model_uri
B. When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark
C. When the model at model_uri only uses customer_id as a feature
D. This code block will not perform the desired inference in any situation.
E. When all of the features used by the model at model_uri are in a single Feature Store table
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