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Databricks Machine Learning Associate Exam Syllabus

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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
Vendor
Databricks Machine Learning Associate
Exam Code
74
Total Questions
4
Total Exam Domains

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Databricks-Machine-Learning-Associate EXAM QUESTIONS

Databricks Machine Learning Associate Exam Objectives

Section 1: Databricks Machine Learning
Weight:
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 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
Section 2: ML Workflows
Weight:
19%
  • Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries
  • Remove outliers from a Spark DataFrame based on standard deviation or IQR
  • Create visualizations for categorical or continuous features
  • Compare two categorical or two continuous features using the appropriate method
  • Compare and contrast imputing missing values with the mean or median or mode value
  • Impute missing values with the mode, mean, or median value
  • Use one-hot encoding for categorical features
  • Identify and explain the model types or data sets for which one-hot encoding is or is not appropriate.
  • Identify scenarios where log scale transformation is appropriate
Section 3: Model Development
Weight:
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 model performance
Section 4: Model Deployment
Weight:
12%
  • Identify the differences and advantages of model serving approaches: batch, realtime, 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 realtime inference
  • Split data between endpoints for realtime interference
Info
https://www.databricks.com/learn/certification/machine-learning-associate