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

Section	Weight	Objectives
Model Development	44%	- Feature Store and automated feature pipelines - Scalable ML pipelines with SparkML - Advanced MLflow usage - Distributed training and hyperparameter tuning
Model Deployment	12%	- Custom model serving - Model rollout and version management - Deployment strategies
ML Ops	44%	- Automated retraining workflows - Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles

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Databricks Certified Machine Learning Professional Sample Questions (Q163-Q168):

NEW QUESTION # 163
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. There is no way to accomplish this task with fs
B. fs.write_table
C. fs.read_table
D. fs.create_table
E. fs.get_table

Answer: D

NEW QUESTION # 164
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

A. JS does not require any manual threshold or cutoff determinations
B. None of these reasons
C. JS is not normalized or smoothed
D. All of these reasons
E. JS is more robust when working with large datasets

Answer: E

NEW QUESTION # 165
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables. Which of the following tools can the machine learning engineer use to assess their theory?

A. Two-way Chi-squared Test
B. Jenson-Shannon distance
C. One-way Chi-squared Test
D. None of these
E. Kolmogorov-Smirnov (KS) test

Answer: C

NEW QUESTION # 166
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors can only be used for logging models
B. Built-in library-specific flavors are required for model signature use
C. Built-in library-specific flavors allow models to be exported as library objects
D. Built-in library-specific flavors allow models to be used with any library/span>

Answer: C

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 167
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

A. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
B. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.
C. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.
D. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
Use groupby.applyInPandas.

Answer: C

Explanation:
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

NEW QUESTION # 168
......

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