Databricks-Machine-Learning-Professional Exam Topics
Databricks-Machine-Learning-Professional Exam Overview :
| Exam Name: | Databricks Certified Machine Learning Professional |
| Exam Code: | Databricks-Machine-Learning-Professional |
| Certifications: | Databricks Machine Learning Professional Certification |
| Actual Exam Duration: | 120 minutes |
| Expected no. of Questions in Actual Exam: | 60 |
| Exam Registration Price: | $200 |
| See Expected Questions: | Databricks Databricks-Machine-Learning-Professional Expected Questions in Actual Exam |
Databricks-Machine-Learning-Professional Exam Objectives :
| Section | Objectives |
|---|---|
| Section 1: Experimentation | 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 |
| Section 2: Model Lifecycle Management | 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 |
| Section 3: Model Deployment | 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 |
| Section 4: Solution and Data Monitoring | 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 |
Updates in the Databricks-Machine-Learning-Professional Exam Topics:
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