Databricks Databricks-Machine-Learning-Professional Exam Topics
Databricks Certified Machine Learning Professional
Total Questions: 60What is Included in the Databricks-Machine-Learning-Professional Exam?
Authentic information about the syllabus is essential to go through the Databricks Databricks-Machine-Learning-Professional exam in the first attempt. Study4Exam provides you with comprehensive information about Databricks Databricks-Machine-Learning-Professional exam topics listed in the official syllabus. You should get this information at the start of your preparation because it helps you make an effective study plan. We have designed this Databricks Machine Learning Professional certification exam preparation guide to give the exam overview, practice questions, practice test, prerequisites, and information about exam topics that help to go through the Databricks Certified Machine Learning Professional exam. We recommend you use our preparation material to cover the entire Databricks Databricks-Machine-Learning-Professional exam syllabus. Study4Exam offers 3 formats of Databricks Databricks-Machine-Learning-Professional exam preparation material. Each format provides new practice questions in PDF format, web-based and desktop practice exams to get passing marks in the first attempt.
Databricks Databricks-Machine-Learning-Professional Exam Overview :
| Exam Name | Databricks Certified Machine Learning Professional |
| Exam Code | Databricks-Machine-Learning-Professional |
| Actual Exam Duration | 120 minutes |
| Expected no. of Questions in Actual Exam | 60 |
| Exam Registration Price | $200 |
| Official Information | https://www.databricks.com/learn/certification/machine-learning-professional |
| See Expected Questions | Databricks Databricks-Machine-Learning-Professional Expected Questions in Actual Exam |
| Take Self-Assessment | Use Databricks Databricks-Machine-Learning-Professional Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
Databricks Databricks-Machine-Learning-Professional Exam Topics :
| 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 |
Updates in the Databricks-Machine-Learning-Professional Exam Topics:
Databricks Databricks-Machine-Learning-Professional exam questions and practice test are the best ways to get fully prepared. Study4exam's trusted preparation material consists of both practice questions and practice test. To pass the actual Machine Learning Professional Databricks-Machine-Learning-Professional exam on the first attempt, you need to put in hard work on these questions as they cover all updated Databricks Databricks-Machine-Learning-Professional exam topics included in the official syllabus. Besides studying actual questions, you should take the Databricks Databricks-Machine-Learning-Professional practice test for self-assessment and actual exam simulation. Revise actual exam questions and remove your mistakes with the Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional exam practice test. Online and Windows-based formats of the Databricks-Machine-Learning-Professional exam practice test are available for self-assessment.
- 50000+ Customers feedbacks involved in Products
- Customize your exam based on your objectives
- User-Friendly interface
- Exam History and Progress reports
- Self-Assessment Features
- Various Learning Modes