1. Home
  2. Databricks
  3. Databricks-Machine-Learning-Associate Exam Syllabus

Databricks Machine Learning Associate Exam Syllabus

Start Free Databricks Machine Learning Associate Exam Practice After Reviewing the Topics

Before starting your Databricks Machine Learning Associate exam preparation, it is recommended to review the complete Databricks Certified Machine Learning Associate Exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free Databricks Machine Learning Associate questions. We also provide premium Databricks Machine Learning Associate practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Databricks Machine Learning Associate Exam Objectives

Section Weight Objectives
Databricks Machine Learning 38%
  • Identify the best practices of an MLOps strategy
? Identify the advantages of using ML runtimes
? Identify how AutoML facilitates model/feature selection.
? Identify the advantages AutoML brings to the model development process
? Identify the benefits of creating feature store tables at the account level in Unity Catalog in
Databricks vs at the workspace level
? Create a feature store table in Unity Catalog
? 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.
? Describe the differences between online and offline feature tables
? Identify the best run using the MLflow Client API.
? Manually log metrics, artifacts, and models in an MLflow Run.
? Identify information available in the MLFlow UI
? Register a model using the MLflow Client API in the Unity Catalog registry
? Identify the benefits of registering models in the Unity Catalog registry over the workspace
registry
? Identify scenarios where promoting code is preferred over promoting models and vice
versa
Set or remove a tag for a model
? Promote a challenger model to a champion model using aliases
ML Workflows 19%
  • Identify the best practices of an MLOps strategy
  • Identify the advantages of using ML runtimes
? Identify how AutoML facilitates model/feature selection.
? Identify the advantages AutoML brings to the model development process
? Identify the benefits of creating feature store tables at the account level in Unity Catalog in
Databricks vs at the workspace level
? Create a feature store table in Unity Catalog
? 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.
? Describe the differences between online and offline feature tables
? Identify the best run using the MLflow Client API.
? Manually log metrics, artifacts, and models in an MLflow Run.
? Identify information available in the MLFlow UI
? Register a model using the MLflow Client API in the Unity Catalog registry
? Identify benefits of registering models in the Unity Catalog registry over the workspace
registry
? Identify scenarios where promoting code is preferred over promoting models and vice
versa
? Set or remove a tag for a model
Model Development 31%
  • Use ML foundations to select the appropriate algorithm for a given model scenario
? Identify methods to mitigate data imbalance in training data
? Compare estimators and transformers
? Develop a training pipeline
? Use Hyperopt's fmin operation to tune a model's hyperparameters
? Perform random or grid search or Bayesian search as a method for tuning hyperparameters.
? Parallelize single node models for hyperparameter tuning
? Describe the benefits and 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.
? Use common classification metrics: F1, Log Loss, ROC/AUC, etc
? Use common regression metrics: RMSE, MAE, R-squared, etc.
? Choose the most appropriate metric for a given scenario objective
? Identify the need to exponentiate log-transformed variables before calculating evaluation
metrics or interpreting predictions
? Assess the impact of model complexity and the bias-variance tradeoff on the model
performance
Model Deployment 12%
  • Identify the differences and advantages of model serving approaches: batch, real-time, and streaming
? Deploy a custom model to a model endpoint
? Use pandas to perform batch inference
? Identify how streaming inference is performed with Delta Live Tables
? Deploy and query a model for real-time inference
? Split data between endpoints for real-time interference
Official Information https://www.databricks.com/learn/certification/machine-learning-associate

Limited Time Offer

50%

Off

Get Premium Databricks Machine Learning Associate Questions as Interactive Practice Test or PDF