Databricks Machine Learning Professional Exam Syllabus
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Before starting your Databricks Machine Learning Professional exam preparation, it is recommended to review the complete Databricks Certified Machine Learning Professional 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 Professional questions. We also provide premium Databricks Machine Learning Professional practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.
Databricks Machine Learning Professional Exam Objectives
| Section | Weight | Objectives |
|---|---|---|
| Experimentation | 30% | Data Management ? Read and write a Delta table ? View Delta table history and load a previous version of a Delta table ? Create, overwrite, merge, and read Feature Store tables in machine learning workflows Experiment Tracking ? Manually log parameters, models, and evaluation metrics using MLflow ? Programmatically access and use data, metadata, and models from MLflow experiments Advanced Experiment Tracking ? Perform MLflow experiment tracking workflows using model signatures and input examples ? Identify the requirements for tracking nested runs ? Describe the process of enabling autologging, including with the use of Hyperopt ? Log and view artifacts like SHAP plots, custom visualizations, feature data, images, and metadata |
| Model Lifecycle Management | 30% | Preprocessing Logic ? Describe an MLflow flavor and the benefits of using MLflow flavors ? Describe the advantages of using the pyfunc MLflow flavor ? Describe the process and benefits of including preprocessing logic and context in custom model classes and objects Model Management ? Describe the basic purpose and user interactions with Model Registry ? Programmatically register a new model or new model version. ? Add metadata to a registered model and a registered model version ? Identify, compare, and contrast the available model stages ? Transition, archive, and delete model versions Model Lifecycle Automation ? Identify the role of automated testing in ML CI/CD pipelines ? Describe how to automate the model lifecycle using Model Registry Webhooks and Databricks Jobs ? Identify advantages of using Job clusters over all-purpose clusters ? Describe how to create a Job that triggers when a model transitions between stages, given a scenario ? Describe how to connect a Webhook with a Job ? Identify which code block will trigger a shown webhook ? Identify a use case for HTTP webhooks and where the Webhook URL needs to come. ? Describe how to list all webhooks and how to delete a webhook |
| Model Deployment | 25% | Batch ? Describe batch deployment as the appropriate use case for the vast majority of deployment use cases ? Identify how batch deployment computes predictions and saves them somewhere for later use ? Identify live serving benefits of querying precomputed batch predictions ? Identify less performant data storage as a solution for other use cases ? Load registered models with load_model ? Deploy a single-node model in parallel using spark_udf ? Identify z-ordering as a solution for reducing the amount of time to read predictions from a table ? Identify partitioning on a common column to speed up querying ? Describe the practical benefits of using the score_batch operation Streaming ? Describe Structured Streaming as a common processing tool for ETL pipelines ? Identify structured streaming as a continuous inference solution on incoming data ? Describe why complex business logic must be handled in streaming deployments ? Identify that data can arrive out-of-order with structured streaming ? Identify continuous predictions in time-based prediction store as a scenario for streaming deployments ? Convert a batch deployment pipeline inference to a streaming deployment pipeline ? Convert a batch deployment pipeline writing to a streaming deployment pipeline Real-time ? Describe the benefits of using real-time inference for a small number of records or when fast prediction computations are needed ? Identify JIT feature values as a need for real-time deployment ? Describe model serving deploys and endpoint for every stage ? Identify how model serving uses one all-purpose cluster for a model deployment ? Query a Model Serving enabled model in the Production stage and Staging stage ? Identify how cloud-provided RESTful services in containers is the best solution for production-grade real-time deployments |
| Solution and Data Monitoring | 15% | Drift Types ? Compare and contrast label drift and feature drift ? Identify scenarios in which feature drift and/or label drift are likely to occur ? Describe concept drift and its impact on model efficacy Drift Tests and Monitoring ? Describe summary statistic monitoring as a simple solution for numeric feature drift ? Describe mode, unique values, and missing values as simple solutions for categorical feature drift ? Describe tests as more robust monitoring solutions for numeric feature drift than simple summary statistics ? Describe tests as more robust monitoring solutions for categorical feature drift than simple summary statistics ? Compare and contrast Jenson-Shannon divergence and Kolmogorov-Smirnov tests for numerical drift detection ? Identify a scenario in which a chi-square test would be useful Comprehensive Drift Solutions ? Describe a common workflow for measuring concept drift and feature drift ? Identify when retraining and deploying an updated model is a probable solution to drift ? Test whether the updated model performs better on the more recent data |
| Official Information | https://www.databricks.com/learn/certification/machine-learning-professional |
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