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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Model Development	47%	- Feature Store 1. Automated feature pipelines 2. Feature Store concepts and usage - MLflow Advanced Features 1. Model registry and versioning 2. Experiment tracking and model management - SparkML and Scaling 1. Distributed training and hyperparameter tuning 2. Building scalable ML pipelines with SparkML 3. Ray/Optuna distributed tuning
Model Lifecycle Management (MLOps)	43%	- Environment Management 1. Environment configuration and reproducibility 2. Databricks Asset Bundles (DABs) - Monitoring 1. Model performance monitoring 2. Lakehouse Monitoring for drift detection - Automated Workflows 1. CI/CD pipelines for ML 2. Automated retraining workflows - Testing Strategies 1. Model testing and validation 2. Data quality testing
Model Deployment	10%	- Deployment Strategies 1. Batch Deployment 2. Streaming Deployment 3. Real-time Deployment - Model Serving 1. Databricks Model Serving 2. Custom serving solutions

Section

Weight

Objectives

Model Development

47%

- Feature Store

1. Automated feature pipelines
2. Feature Store concepts and usage

- MLflow Advanced Features

1. Model registry and versioning
2. Experiment tracking and model management

- SparkML and Scaling

1. Distributed training and hyperparameter tuning
2. Building scalable ML pipelines with SparkML
3. Ray/Optuna distributed tuning

Model Lifecycle Management (MLOps)

43%

- Environment Management

1. Environment configuration and reproducibility
2. Databricks Asset Bundles (DABs)

- Monitoring

1. Model performance monitoring
2. Lakehouse Monitoring for drift detection

- Automated Workflows

1. CI/CD pipelines for ML
2. Automated retraining workflows

- Testing Strategies

1. Model testing and validation
2. Data quality testing

Model Deployment

10%

- Deployment Strategies

1. Batch Deployment
2. Streaming Deployment
3. Real-time Deployment

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

>> Valid Databricks-Machine-Learning-Professional Mock Exam <<

2026 Unparalleled Valid Databricks-Machine-Learning-Professional Mock Exam & Databricks Certified Machine Learning Professional Test Dates

Undoubtedly, passing the Databricks Databricks-Machine-Learning-Professional Certification Exam is one big achievement. Regardless of how tough the Databricks Certified Machine Learning Professional (Databricks-Machine-Learning-Professional) exam is, it serves an important purpose of improving your skills and knowledge of a specific field. Once you become certified by Databricks, a whole new career scope will open up to you.

Databricks Certified Machine Learning Professional Sample Questions (Q11-Q16):

NEW QUESTION # 11
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. fs.score_batch(model_uri, spark_df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
C. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
E. fs.score_model(model_uri, spark_df)

Answer: A

NEW QUESTION # 12
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?

A. mlflow.spark.autolog()
B. mlflow.sklearn.autolog()
C. mlflow.autolog()
D. It is not possible to automatically log MLflow runs.
E. spark.conf.set("autologging", True)

Answer: E

NEW QUESTION # 13
A data scientist is building a model to predict which communication channel (Phone, SMS, Email, or Post) is most likely to be effective for a given customer. Which model type is suited to this task?

A. ARIMA
B. Softmax Classifier
C. Logistic Regression
D. Linear Regression

Answer: B

Explanation:
This task requires predicting one class from multiple mutually exclusive categories. A softmax classifier is designed for multiclass classification problems and outputs a probability distribution across all possible communication channels, allowing selection of the most likely effective channel.

NEW QUESTION # 14
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.sklearn.log_model(sklearn_model, "model")
B. mlflow.sklearn.track_model(sklearn_model, "model")
C. mlflow.sklearn.load_model("model")
D. mlflow.spark.track_model(sklearn_model, "model")
E. mlflow.spark.log_model(sklearn_model, "model")

Answer: D

NEW QUESTION # 15
A machine learning engineer has developed a random forest model using scikit-learn and registered the model using MLflow. They now want to deploy that model in parallel. Which of the following operations can they use to create a function they can use to deploy the registered scikit- learn model in parallel?

A. mlflow.spark.pandas_udf
B. It is not possible to deploy a scikit-learn model in parallel.
C. mlflow.pyfunk.spark.udf
D. mlflow.sklearn.spark.udf

Answer: D

Explanation:
The correct operation is mlflow.sklearn.spark_udf, which creates a Spark UDF from a registered scikit-learn model. This allows the model to be applied in parallel across a Spark DataFrame, enabling scalable batch inference using Spark.

NEW QUESTION # 16
......

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