Databricks ML Associate
Free Databricks Certified Machine Learning Associate Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2004 exam domainsLevel Intermediate2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A data scientist has a labeled classification dataset in a Delta table and wants to quickly generate a baseline model along with the full source code used to train it, so they can inspect and customize the winning approach. Which Databricks capability BEST meets this need?
AutoML automatically preprocesses data, trains multiple candidate models, and produces editable source-code notebooks for each trial, giving both a fast baseline and full transparency into the generated code.
A data scientist is preparing a dataset for a linear regression model on Databricks. They plan to scale all numeric features using StandardScaler before training. Which approach BEST avoids data leakage during this process?
Fitting the scaler only on training data and reusing those fitted parameters (mean/std) on the test set ensures the test set statistics never influence training, which is the core requirement for avoiding leakage.
A data scientist is training a binary classifier to detect fraudulent transactions where only 0.5% of transactions are fraudulent. The model achieves 99.4% accuracy but fails to catch almost any fraud cases. Which evaluation metric should the data scientist prioritize instead to properly assess this model?
With severe class imbalance, accuracy is misleading because a naive model that always predicts the majority class still scores high. F1 (harmonic mean of precision and recall) or PR-AUC directly measure how well the minority (fraud) class is detected.
A retail company needs to score credit-card transactions for fraud in under 100 milliseconds as each transaction occurs, so a decision can be returned to the point-of-sale system synchronously. Which Databricks deployment approach BEST meets this requirement?
Model Serving endpoints expose a low-latency REST API backed by autoscaling compute, purpose-built for real-time, per-request scoring with sub-second response times.
Several teams across an organization need to reuse the same curated customer features for multiple models, require point-in-time correct joins when building training sets, and want automatic lineage tracking from features back to source tables and forward to trained models. Which Databricks capability BEST satisfies these requirements?
Feature Engineering in Unity Catalog (Feature Store) provides managed feature tables with primary keys, point-in-time correct training set creation, automatic lineage to source data and downstream models, and reuse across teams.
A team has an existing pandas-based feature engineering pipeline that runs fine on a 5GB dataset but now needs to process a 500GB Delta Lake table on Databricks, with minimal rewriting of existing pandas-style code. Which approach BEST meets this requirement?
The pandas API on Spark (pyspark.pandas) provides a pandas-compatible API that executes in a distributed manner across the cluster, letting teams reuse most of their existing pandas syntax while scaling to large datasets.
A data scientist is running Hyperopt to tune hyperparameters for a scikit-learn model and wants each hyperparameter trial to run in parallel across the worker nodes of a Databricks cluster. Which Hyperopt component should be passed as the `trials` argument to `fmin()`?
SparkTrials distributes Hyperopt trial evaluations across the Spark cluster's workers, enabling parallel execution of single-node model training trials, which is ideal for tuning scikit-learn or other non-distributed models on Databricks.
A data scientist has a registered MLflow model and needs to apply it to score 500 million rows stored in a Delta table as part of a scheduled daily job, distributing the work across a cluster. Which approach should be used?
mlflow.pyfunc.spark_udf wraps a registered model as a Spark UDF, allowing it to be applied in parallel across all executors, which is the standard scalable batch-inference pattern on Databricks.
A data scientist is training a scikit-learn model and wants MLflow to automatically capture parameters, metrics, and the trained model as an artifact without manually calling logging functions for each item. Which single line should be added before training starts?
mlflow.autolog() enables automatic logging of parameters, metrics, and the trained model artifact for supported libraries (including scikit-learn) without requiring explicit log_param/log_metric calls.
A dataset column 'income' has 12% missing values due to a data collection glitch (missing completely at random) and the column is right-skewed. The team wants to impute the missing values while minimizing distortion of the overall distribution. Which imputation strategy is BEST?
For skewed numeric distributions, the median is robust to outliers and skew, making it a better single-value imputation choice than the mean and preserving more of the dataset than dropping rows.
A data scientist wants each hyperparameter combination evaluated during a Hyperopt sweep to be logged as its own separate MLflow run, grouped under a single parent run that represents the entire tuning job. Which MLflow configuration accomplishes this?
Wrapping the objective function's per-trial run in mlflow.start_run(nested=True), while an outer parent run is already active, creates child runs nested under the parent — the standard pattern for organizing Hyperopt sweeps in the MLflow UI.
In Unity Catalog, which URI pattern correctly loads a model version tagged with the alias 'champion' for use with mlflow.pyfunc.load_model?
Unity Catalog model registry uses the @alias syntax (e.g., models:/catalog.schema.model_name@champion) to reference a specific model version by alias rather than a numeric version or legacy stage.
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How much does the Databricks ML Associate exam cost?
The exam fee is approximately $200 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 4 domains: Databricks Machine Learning (38%), ML Workflows (19%), Model Development (31%), Model Deployment (12%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.