Databricks Certified Machine Learning Associate Practice Test Overview
Total Questions: 60 (This is an estimate, the actual number may vary slightly)
Target Job Roles: Data Scientists, Machine Learning Engineers, Data Analysts, Business Analysts working with Machine Learning solutions on the Databricks platform. Anyone looking to demonstrate proficiency in using Databricks for machine learning tasks.
Language: English
I. Data Exploration and Preprocessing (Approximately 15-20 Questions)
- A. Data Ingestion and Loading:
- Questions on various data sources (CSV, Parquet, JSON, databases) and their efficient loading into Databricks using Spark. Think about how you'd handle large datasets – it's not just about knowing the commands, but understanding the why behind choosing one method over another.
- Example: Compare and contrast using
spark.read.csv vs. spark.read.parquet for a 10GB dataset. What are the trade-offs?
- B. Data Cleaning and Transformation:
- Handling missing values – imputation techniques, dropping rows/columns. This isn't just about filling in blanks; it's about understanding the impact on your model.
- Data type conversions and feature engineering – creating new features from existing ones (e.g., extracting date components, creating interaction terms). I remember struggling with this on my first project! It takes practice.
- Questions on using built-in Spark functions and UDFs (User Defined Functions) for data manipulation.
- C. Exploratory Data Analysis (EDA):
- Descriptive statistics (mean, median, mode, standard deviation). Knowing the difference and when to apply each is crucial.
- Data visualization using libraries like Matplotlib and Seaborn within Databricks notebooks. Think: histograms, scatter plots, box plots – and when would you use each?
- Identifying patterns, outliers, and anomalies in the data.
II. Model Building and Training (Approximately 20-25 Questions)
- A. Algorithm Selection:
- Understanding different machine learning algorithms (regression, classification, clustering) and their suitability for various problem types. Don't just memorize algorithms; understand their strengths and weaknesses.
- Example: When would you choose a decision tree over a linear regression model?
- B. Model Training with MLlib:
- Using MLlib APIs for training different types of models (linear regression, logistic regression, random forests, gradient-boosted trees, etc.). Hands-on experience with this is vital.
- Hyperparameter tuning – techniques like grid search and cross-validation. This isn't just about running code; it's about understanding the underlying principles.
- C. Model Evaluation:
- Evaluating model performance using appropriate metrics (accuracy, precision, recall, F1-score, AUC-ROC, RMSE, etc.). Understanding these metrics and their implications is key.
- Interpreting confusion matrices and ROC curves.
III. Model Deployment and Monitoring (Approximately 10-15 Questions)
- A. Model Deployment Options:
- Deploying models using Databricks features like MLflow. Think real-world scenarios: batch vs. real-time prediction.
- Serving models using REST APIs or other methods.
- B. Model Monitoring and Retraining:
- Tracking model performance over time and identifying potential issues (concept drift, data quality changes). This is critical for maintaining model accuracy in production.
- Retraining models with new data as needed.
IV. Databricks Platform and Ecosystem (Approximately 5-10 Questions)
- A. Databricks Workspace Navigation: Familiarity with the Databricks interface, notebooks, clusters, and other key components.
- B. Security and Access Control: Understanding Databricks' security features and best practices.
- C. Databricks Utilities (optional): Basic understanding of relevant Databricks utilities might be included.
Note: This outline is for guidance. The actual exam may contain questions across these areas with varying weights. Hands-on experience with Databricks and its ML features is highly recommended. Good luck!
