Databricks Certified Machine Learning Associate Exam Syllabus

- Databricks ML

• Identify when a standard cluster is preferred over a single-node cluster and vice versa
• Connect a repo from an external Git provider to Databricks repos.
• Commit changes from a Databricks Repo to an external Git provider.
• Create a new branch and commit changes to an external Git provider.
• Pull changes from an external Git provider back to a Databricks workspace.
• Orchestrate multi-task ML workflows using Databricks jobs.

- Databricks Runtime for Machine Learning

• Create a cluster with the Databricks Runtime for Machine Learning.
• Install a Python library to be available to all notebooks that run on a cluster.

- AutoML

• Identify the steps of the machine learning workflow completed by AutoML.
• Identify how to locate the source code for the best model produced by AutoML.
• Identify which evaluation metrics AutoML can use for regression problems.
• Identify the key attributes of the data set using the AutoML data exploration notebook.

- Feature Store

• Describe the benefits of using Feature Store to store and access features for machine learning pipelines.
• Create a feature store table.
• Write data to a feature store table.
• Train a model with features from a feature store table.
• Score a model using features from a feature store table.

- Managed MLflow

• Identify the best run using the MLflow Client API.
• Manually log metrics, artifacts, and models in an MLflow Run.
• Create a nested Run for deeper Tracking organization. 
• Locate the time a run was executed in the MLflow UI.
• Locate the code that was executed with a run in the MLflow UI.
• Register a model using the MLflow Client API.
• Transition a model’s stage using the Model Registry UI page.
• Transition a model’s stage using the MLflow Client API.
• Request to transition a model’s stage using the ML Registry UI page.

• Compute summary statistics on a Spark DataFrame using .summary()
• Compute summary statistics on a Spark DataFrame using dbutils data summaries.
• Remove outliers from a Spark DataFrame that are beyond or less than a designated threshold.

- Feature Engineering

• Identify why it is important to add indicator variables for missing values that have been imputed or replaced.
• Describe when replacing missing values with the mode value is an appropriate way to handle missing values.
• Compare and contrast imputing missing values with the mean value or median value.
• Impute missing values with the mean or median value.
• Describe the process of one-hot encoding categorical features.
• Describe why one-hot encoding categorical features can be inefficient for tree-based models.

- Training

• Perform random search as a method for tuning hyperparameters.
• Describe the basics of Bayesian methods for tuning hyperparameters.
• Describe why parallelizing sequential/iterative models can be difficult.
• Understand the balance between compute resources and parallelization.
• Parallelize the tuning of hyperparameters using Hyperopt and SparkTrials.
• Identify the usage of SparkTrials as the tool that enables parallelization for tuning single-node models.

- Evaluation and Selection

• Describe cross-validation and the benefits of downsides of using cross-validation over a train-validation split.
• Perform cross-validation as a part of model fitting.
• Identify the number of models being trained in conjunction with a grid-search and cross-validation process.
• Describe Recall and F1 as evaluation metrics.
• Identify the need to exponentiate the RMSE when the log of the label variable is used
• Identify that the RMSE has not been exponentiated when the log of the label variable is used.

• Describe some of the difficulties associated with distributing machine learning models.
• Identify Spark ML as a key library for distributing traditional machine learning work.
• Identify scikit-learn as a single-node solution relative to Spark ML.

- Spark ML Modeling APIs

• Split data using Spark ML.
• Identify key gotchas when splitting distributed data using Spark ML.
• Train / evaluate a machine learning model using Spark ML.
• Describe Spark ML estimator and Spark ML transformer.
• Develop a Pipeline using Spark ML.
• Identify key gotchas when developing a Spark ML Pipeline.

- Hyperopt

• Identify Hyperopt as a solution for parallelizing the tuning of single-node models.
• Identify Hyperopt as a solution for Bayesian hyperparameter inference for distributed models.
• Parallelize the tuning of hyperparameters for Spark ML models using Hyperopt and Trials.
• Identify the relationship between the number of trials and model accuracy.

- Pandas API on Spark

• Describe key differences between Spark DataFrames and Pandas on Spark DataFrames.
• Identify the usage of an InternalFrame making Pandas API on Spark not quite as fast as native Spark.
• Identify Pandas API on Spark as a solution for scaling data pipelines without much refactoring.
• Convert data between a PySpark DataFrame and a Pandas on Spark DataFrame.
• Identify how to import and use the Pandas on Spark APIs.

- Pandas UDFs/Function APIs

• Identify Apache Arrow as the key to Pandas <-> Spark conversions.
• Describe why iterator UDFs are preferred for large data.
• Apply a model in parallel using a Pandas UDF.
• Identify that pandas code can be used inside of a UDF function.
• Train / apply group-specific models using the Pandas Function API.

- Model Distribution

• Describe how Spark scales linear regression.
• Describe how Spark scales decision trees.

Ensembling Distribution

• Describe the basic concepts of ensemble learning
• Compare and contrast bagging, boosting, and stacking.