Databricks Certified Machine Learning Associate practice questions
30 questions in 3 tests. Open one to answer it on its own, or take a whole test.
Test 1
Take Test 1 ›- A registered model prod.fraud.scorer in Unity Catalog has version 3 carrying the alias champion. A nightly batch job loads the model with the URI… · MLflow Model Registry
- A data scientist trained a churn model with FeatureEngineeringClient. The training DataFrame held customer_id and the label churned, and a FeatureLookup on the feature table… · Feature Engineering in Unity Catalog
- An experiment holds about 200 runs from a regression tuning session, and each run logged the metric val_rmse. In a notebook, you need the ID of the single run with the best… · MLflow Tracking
- Your team is starting a new classification project. Your manager wants a strong baseline model quickly, but the data scientists insist on reviewing and changing whatever code… · AutoML
- A continuous feature, annual_income, is missing for 8% of rows. Its distribution is heavily right-skewed: most values fall between 30,000 and 90,000, but a few accounts report… · Data Preparation
- Before feature engineering, a data scientist wants a first look at a new Spark DataFrame in a Databricks notebook. For every column they want the missing-value count, the number… · Data Preparation
- A data scientist target-encodes a high-cardinality merchant_id column: each merchant is replaced with its mean label value, computed over the entire dataset. They then run 5-fold… · Model Evaluation
- In a payments dataset, 0.4% of transactions are fraudulent. Flagged transactions go to a small team of analysts, so the model must catch as much fraud as possible without… · Model Evaluation
- To reduce skew, a house-price regressor is trained on log(price). On the validation set, the data scientist computes RMSE between the model's raw predictions and log(price) and… · Model Evaluation
- A real-time endpoint serves version 4 of prod.recs.ranker from Unity Catalog. The team wants 10% of live requests to go to version 6 so they can compare the two on production… · Model Deployment
Test 2
Take Test 2 ›- A new team will train scikit-learn and XGBoost models in Databricks notebooks and track every run in MLflow. Clusters are terminated each evening and recreated each morning. The… · Databricks Machine Learning
- A data scientist loops over 40 hyperparameter combinations, calling model.fit() once per combination in a Databricks notebook. They want each combination recorded as its own… · MLflow Tracking
- A data scientist has a Spark DataFrame of per-customer aggregates keyed by customer_id and wants to publish it as a feature table in Unity Catalog with… · Feature Engineering in Unity Catalog
- A data scientist finishes a training run and calls: mlflow.register_model("runs:/<run_id>/model", "churn_model") The call succeeds, but the model appears in the workspace model… · MLflow Model Registry
- A Spark ML pipeline ends in a LogisticRegression estimator. One input, region, is a string column with six distinct values such as "north" and "west". VectorAssembler rejects the… · ML Workflows
- A notebook builds a training DataFrame from a chain of joins and filters over Delta tables, then splits it: train_df, test_df = df.randomSplit([0.8, 0.2], seed=42) The seed never… · ML Workflows
- A Spark ML tuning job searches a 36-combination parameter grid with CrossValidator(numFolds=3) over a 40-million-row training set. It fits 108 models and overruns the nightly… · Model Development
- A gradient-boosted classifier scores 0.99 accuracy on the training set and 0.72 on the validation set. As the data scientist adds trees and increases the maximum depth, the… · Model Development
- A random forest is trained on customer data. Ranking the model's feature_importances_ puts account_number, a near-unique numeric identifier, at the top. A stakeholder asks… · Model Development
- A nightly job must score a 500-million-row Delta table with a model registered in Unity Catalog. The current implementation calls toPandas() on the table and then… · Model Deployment
Test 3
Take Test 3 ›- Three data scientists are working on the same churn project, each in their own notebook. Every run is landing in a separate notebook experiment, so nobody can sort all the runs… · MLflow Tracking
- A data scientist runs Databricks AutoML on two years of weekly demand records to forecast next quarter's demand. The best trial reports excellent validation error, but when the… · AutoML
- A feature table holds one row per customer for 40 million customers. A nightly job recomputes features only for the roughly 800,000 customers who were active that day and writes… · Feature Engineering in Unity Catalog
- A data scientist logs a scikit-learn model with mlflow.sklearn.log_model(model, name="model") and then tries to register it to the Unity Catalog model ml.churn.scorer… · MLflow Model Registry
- A pipeline fits an L2-regularised LogisticRegression on features that include annual_income (tens of thousands), age (tens) and visits_last_week (single digits). A colleague… · ML Workflows
- A model predicts, at the moment a support ticket is opened, whether that ticket will breach its SLA. It is trained on a table of closed tickets whose columns include priority… · ML Workflows
- A lender must give every declined applicant a written reason citing the specific factors that counted against that individual application. A regulator requires the reason to… · Model Development
- A team is tuning a gradient-boosted model with six hyperparameters. Four are continuous, such as the learning rate and the subsample fraction, and previous projects suggest only… · Model Development
- A model predicts parcel delivery time in minutes. Most deliveries fall between 20 and 90 minutes, but a small number of shipments are delayed for days by weather or customs… · Model Evaluation
- A retailer scores all 20 million customers once a week to choose who receives a printed catalogue. The mailing house collects the scores from a table each Monday morning. No… · Model Deployment