Free Practice Questions for Databricks Certified Machine Learning Associate Certification

1.1: Identify the best practices of an MLOps strategy

1.2: Identify the advantages of using ML runtimes

1.3: Identify how AutoML facilitates model/feature selection.

1.4: Identify the advantages AutoML brings to the model development process

1.5: Identify the benefits of creating feature store tables at the account level in Unity Catalog in Databricks vs at the workspace level

1.6: Create a feature store table in Unity Catalog

1.7: Write data to a feature store table

1.8: Train a model with features from a feature store table.

1.9: Score a model using features from a feature store table.

1.10: Describe the differences between online and offline feature tables

1.11: Identify the best run using the MLflow Client API.

1.12: Manually log metrics, artifacts, and models in an MLflow Run.

1.13: Identify information available in the MLFlow UI

1.14: Register a model using the MLflow Client API in the Unity Catalog registry

1.15: Identify benefits of registering models in the Unity Catalog registry over the workspace registry

1.16: Identify scenarios where promoting code is preferred over promoting models and vice versa

1.17: Set or remove a tag for a model

1.18: Promote a challenger model to a champion model using aliases

2.1: Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries

2.2: Remove outliers from a Spark DataFrame based on standard deviation or IQR

2.3: Create visualizations for categorical or continuous features

2.4: Compare two categorical or two continuous features using the appropriate method

2.5: Compare and contrast imputing missing values with the mean or median or mode value

2.6: Impute missing values with the mode, mean, or median value

2.7: Use one-hot encoding for categorical features

2.8: Identify and explain the model types or data sets for which one-hot encoding is or is not appropriate.

2.9: Identify scenarios where log scale transformation is appropriate

3.1: Use ML foundations to select the appropriate algorithm for a given model scenario

3.2: Identify methods to mitigate data imbalance in training data

3.3: Compare estimators and transformers

3.4: Develop a training pipeline

3.5: Use Hyperopt's fmin operation to tune a model's hyperparameters

3.6: Perform random or grid search or Bayesian search as a method for tuning hyperparameters.

3.7: Parallelize single node models for hyperparameter tuning

3.8: Describe the benefits and downsides of using cross-validation over a train-validation split.

3.9: Perform cross-validation as a part of model fitting.

3.10: Identify the number of models being trained in conjunction with a grid-search and cross-validation process.

3.11: Use common classification metrics: F1, Log Loss, ROC/AUC, etc

3.12: Use common regression metrics: RMSE, MAE, R-squared, etc.

3.13: Choose the most appropriate metric for a given scenario objective

3.14: Identify the need to exponentiate log-transformed variables before calculating evaluation metrics or interpreting predictions

3.15: Assess the impact of model complexity and the bias variance tradeoff on model performance

4.1: Identify the differences and advantages of model serving approaches: batch, realtime, and streaming

4.2: Deploy a custom model to a model endpoint

4.3: Use pandas to perform batch inference

4.4: Identify how streaming inference is performed with Delta Live Tables

4.5: Deploy and query a model for realtime inference

4.6: Split data between endpoints for realtime interference