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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Exam Format:	Multiple choice, Multi-select
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Duration:	120 minutes
Real Exam Qty:	59
Passing Score:	Not publicly disclosed (approximately 70%)
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A.
B.
C.
D. features_df.write.mode("feature").path("new_table")
E. features_df.write.mode("fs").path("new_table")

Answer: B

NEW QUESTION # 144
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

A. PySpark DataFrarne
B. array
C. dict
D. list

Answer: C

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.

NEW QUESTION # 145
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?

A. mlflow.pyfunc.read_model(model_uri)
B. mlflow.spark.load_model(model_uri)
C. mlflow.sklearn.read_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)
E. mlflow.pyfunc.load_model(model_uri)

Answer: E

NEW QUESTION # 146
Which evaluator is used for binary classification?

A. BinaryClassificationEvaluator
B. RegressionEvaluator
C. MulticlassEvaluator
D. ClusteringEvaluator

Answer: A

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.

NEW QUESTION # 147
Which MLflow function registers a model to the registry?

A. mlflow.log_param()
B. mlflow.create_model()
C. mlflow.register_model()
D. mlflow.deploy_model()

Answer: C

Explanation:
mlflow.register_model() promotes a model into the Model Registry.

NEW QUESTION # 148
......

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