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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 Price:	USD 200
Available Languages:	English
Exam Format:	Multiple choice
Real Exam Qty:	60
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Passing Score:	70%
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

>> Databricks-Machine-Learning-Professional Test Lab Questions <<

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

Topic	Details
Topic 1	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 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	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

Databricks Certified Machine Learning Professional Sample Questions (Q179-Q184):

NEW QUESTION # 179
A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Obtain the observed values (actual) feature values
B. Measure the latency of the prediction time
C. None of these should be completed as Step #3
D. Retrain the model
E. Compute the evaluation metric using the observed and predicted values

Answer: C

NEW QUESTION # 180
Which MLflow command logs a trained model?

A. mlflow.run()
B. mlflow.start_run()
C. mlflow.log_model()
D. mlflow.register_model()

Answer: C

Explanation:
mlflow.log_model() saves the model artifact within the run.

NEW QUESTION # 181
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 lines of code can be used to fill in the blank so the code block can successfully complete the task?

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

Answer: E

NEW QUESTION # 182
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. There are no necessary changes
B. Replace list with webhooks in the endpoint URL
C. Replace POST with PUT in the call to http request
D. Replace POST with GET in the call to http request
E. Replace list with view in the endpoint URL

Answer: D

NEW QUESTION # 183
A Machine Learning Engineer is working with a Spark DataFrame containing 100 million rows of retail transactions across thousands of stores. Each store requires its own demand forecasting model using the same scikit-learn pipeline. They want to train and apply these models in parallel for each store without collecting the data to the driver. Which approach will do this?

A. Convert the DataFrame to a Pandas DataFrame using .toPandas() and loop through each store locally.
B. Use rdd.mapPartitions() to iterate through each store and apply the model logic in Python.
C. Use groupBy("store_id").applyInPandas() to train and apply the model per group using a Pandas UDF.
D. Use pyspark.pandas to call .groupby("store_id").apply() and train the model on each group.

Answer: C

Explanation:
groupBy().applyInPandas() enables grouped Pandas UDFs that receive each store's data as a Pandas DataFrame on the executors. This allows training and applying a separate scikit-learn model per store in parallel without collecting data to the driver, making it the correct and scalable approach for large Spark DataFrames.

NEW QUESTION # 184
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