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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Exam Format:	Scenario-based questions, Multiple choice, Multiple select
Available Languages:	English
Certificate Validity Period:	2 years
Passing Score:	Not publicly disclosed
Exam Price:	$200 USD
Related Certifications:	Databricks Certified Machine Learning Associate
Real Exam Qty:	Approximately 45–60
Exam Duration:	120 minutes
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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

Topic	Details
Topic 1	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 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 10	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 11	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

Databricks Certified Machine Learning Professional Sample Questions (Q128-Q133):

NEW QUESTION # 128
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. The new model must perform better than the original model on a random subset of the available data
B. The new model must perform better than the original model on the most recently available data
C. All of these statements must be true prior to deploying the new model
D. The now model must perform better than the original model on all of the available data

Answer: B

Explanation:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

NEW QUESTION # 129
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Experiments
B. Model Registry
C. Model Serving
D. Feature Store
E. Models

Answer: C

NEW QUESTION # 130
Which MLflow function registers a model to the registry?

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

Answer: D

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

NEW QUESTION # 131
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. VERSION
B. TIMESTAMP
C. DESCRIBE
D. HISTORY
E. DESCRIBE HISTORY

Answer: E

NEW QUESTION # 132
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 the training set logged with the model, but a few of the feature values in the column spend have since been updated and arc present in the customer-level Spark DataFrame spark_df. The customer_id column is the primary key of spark_df and the training set used when training and logging the model. Which code block can be used to compute predictions for the training set while overwriting its old spend values with the new spend values from spark_df?

A. fs.score_batch(model_uri, spark_df)
B. df = fs.get_updated_feature(spark_df, model=uri)
fs.score_batch(model_uri, df)
C. df = fs.get_updated_features(spark_df)
fs.score_batch(model_uri, df)
D. fs.score_model(model_uri, spark_df)

Answer: C

Explanation:
To perform batch inference while incorporating updated feature values (like spend) from a DataFrame (spark_df), the correct approach is to use fs.get_updated_features(spark_df) to refresh the relevant features based on the primary key (customer_id), then score the model using fs.score_batch(...). This ensures predictions are made with the latest data.

NEW QUESTION # 133
......

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