Databricks Machine Learning Professional Practice Test Overview
Total Questions: 60 (This is an estimate, the actual number might vary)
Target Job Roles: Data Scientists, Machine Learning Engineers, Data Engineers, AI Specialists, and anyone looking to demonstrate proficiency in building and deploying machine learning solutions on the Databricks platform.
Language: English
I. Data Exploration & Preprocessing (Approx. 15 questions)
- A. Data Ingestion and Loading:
- Different methods for data ingestion into Databricks (e.g., DBFS, cloud storage, structured streaming). Think of it like getting groceries – you need to choose the right method depending on where your data is coming from!
- Handling various data formats (CSV, JSON, Parquet, Avro).
- Understanding data size and its implications for processing.
- B. Data Cleaning and Transformation:
- Identifying and handling missing values (imputation, removal). Imagine baking a cake – you can't leave ingredients out, you need to account for them!
- Data type conversion and manipulation.
- Feature scaling and encoding techniques (one-hot encoding, standardization).
- C. Exploratory Data Analysis (EDA):
- Using visualization tools within Databricks to understand data distributions. Visualizations are your best friend when exploring data – they tell a story much easier than tables!
- Identifying outliers and anomalies.
- Generating summary statistics.
II. Model Building & Training (Approx. 20 questions)
- A. Algorithm Selection:
- Choosing appropriate algorithms based on data characteristics and problem type (classification, regression, clustering). Think of it like choosing the right tool for the right job – a hammer for a nail, not a screwdriver!
- Understanding model assumptions and limitations.
- B. Model Training and Evaluation:
- Using Databricks MLflow for experiment tracking, model versioning, and deployment. MLflow is like your notebook for your ML experiments – keep it organized!
- Evaluating model performance using appropriate metrics (precision, recall, F1-score, AUC).
- Techniques for hyperparameter tuning and optimization (grid search, random search).
- C. Feature Engineering:
- Creating new features from existing ones to improve model performance. Feature engineering is like adding secret ingredients to your recipe to elevate the flavor!
- Understanding feature importance and selection methods.
III. Model Deployment & Monitoring (Approx. 15 questions)
- A. Model Deployment Strategies:
- Deploying models using Databricks APIs and tools.
- Batch inference vs. real-time inference.
- B. Model Monitoring and Maintenance:
- Tracking model performance over time.
- Detecting model drift and retraining strategies.
- Addressing model bias and fairness.
- Once your model is out there, don't forget about it! Regular checkups are critical for its long term health.
IV. Databricks Ecosystem & Security (Approx. 10 questions)
- A. Databricks Platform Basics:
- Understanding clusters, workspaces, and notebooks.
- Working with Delta Lake and its benefits.
- B. Security and Access Control:
- Implementing security best practices within Databricks.
- Managing access control and permissions.
Note: This is a high-level overview. The actual exam may cover additional topics or delve deeper into specific areas. Remember to consult the official Databricks documentation and practice with hands-on exercises for optimal preparation. Good luck!
