Medium — Databricks-ML-Associate Practice Quiz

Question 1

A data scientist has a scikit-learn model that expects a pandas DataFrame and returns a prediction plus a probability score. They want to distribute batch inference across a large Spark DataFrame by grouping records by 'region' and applying the model to each group's data as a pandas DataFrame using groupBy().applyInPandas(). What must be provided for this to execute correctly on Spark?

Accepted Answer

An explicit output schema (StructType) describing the columns and types the applied function returns

Answer

A registered Unity Catalog model URI passed directly to applyInPandas()

Answer

A returnType argument specifying only a single scalar type such as DoubleType

Answer

A vectorized pandas_udf decorator applied to the grouping function

Question 2

A data scientist runs an AutoML classification experiment through the Databricks AutoML UI to predict customer churn. After the experiment completes, they want to understand and customize the feature engineering and hyperparameters used by the top-performing model. What is the most direct way to accomplish this in Databricks AutoML?

Accepted Answer

Open the auto-generated notebook for the best trial from the AutoML experiment page, which contains the full source code for that model

Answer

Re-run the AutoML experiment with autologging enabled to capture the source code as an MLflow artifact

Answer

Download the registered model from Unity Catalog and decompile the serialized model file to inspect its code

Answer

Inspect the data exploration notebook, which contains the training code for every trial in the experiment

Question 3

A data scientist runs a Databricks AutoML classification experiment on a highly imbalanced dataset where only 4% of records belong to the positive (fraud) class. After the experiment completes, they open the AutoML UI to evaluate the trained models. Which behavior should they expect from AutoML regarding how it handles this imbalanced dataset and reports its results?

Accepted Answer

AutoML automatically detects the class imbalance, applies a balancing technique to the majority class in the training data, and surfaces this in the experiment's data exploration notebook.

Answer

AutoML requires the user to manually oversample the minority class before starting the experiment; otherwise it aborts with an error.

Answer

AutoML only supports balanced datasets and will silently drop the experiment if the class ratio exceeds a threshold.

Answer

AutoML ignores class distribution entirely and evaluates all models solely by overall accuracy, with no imbalance handling.

Question 4

A data scientist launches a Databricks AutoML classification experiment through the UI on a customer churn dataset. Before reviewing model results, they want to understand data quality issues such as missing values, high-cardinality columns, and column correlations that AutoML detected during preprocessing. Which artifact should they open to find this information?

Accepted Answer

The data exploration notebook automatically generated by AutoML at the top of the experiment page

Answer

The MLflow run artifacts of the best-performing model

Answer

The generated notebook for the best trial, in its model training cells

Answer

The experiment's 'Schema' tab in the Model Registry entry

Question 5

A data scientist launches an AutoML classification experiment through the Databricks AutoML UI on a customer-churn dataset. After the experiment completes, they want to understand exactly what AutoML produced automatically before doing any manual work. Which two artifacts does every completed AutoML experiment generate and expose in the experiment UI?

Accepted Answer

A data exploration notebook and a notebook for the best-performing model trial

Answer

A feature table in Unity Catalog and a registered model version with the Champion alias

Answer

A scheduled retraining job and a serving endpoint for the best model

Answer

A confusion-matrix image artifact and a SHAP dashboard notebook only

Question 6

A data scientist wants to launch an AutoML classification experiment directly from a notebook using the Python API rather than the AutoML UI. They have a Spark DataFrame named `churn_df`, the target column is `churned`, and they want AutoML to stop after 30 minutes. Which code snippet correctly starts the experiment?

Accepted Answer

from databricks import automl
summary = automl.classify(dataset=churn_df, target_col="churned", timeout_minutes=30)

Answer

from databricks import automl
summary = automl.fit(dataset=churn_df, label="churned", problem_type="classification", max_time=30)

Answer

import mlflow
summary = mlflow.automl.classify(data=churn_df, target="churned", timeout=30)

Answer

from databricks import automl
summary = automl.run(df=churn_df, target_col="churned", task="classify", minutes=30)

Question 7

A data scientist launches a Databricks AutoML forecasting experiment through the UI. They notice that AutoML asks them to select a 'time column' but does not offer options to manually configure a random train/validation/test split percentage the way they expect from a classification experiment. Why does AutoML handle the data split differently for this forecasting experiment?

Accepted Answer

For forecasting, AutoML splits the data chronologically based on the time column so that validation and test sets always come after the training period, preserving temporal order.

Answer

For forecasting, AutoML always uses the entire dataset for training and skips validation entirely, so no split configuration is exposed.

Answer

For forecasting, AutoML randomly shuffles rows and applies an 80/20 split identical to classification, but hides the setting to reduce clutter.

Answer

For forecasting, AutoML requires the user to pre-split the data into separate tables before starting the experiment, which is why no split UI appears.

Question 8

A data scientist launches a Databricks AutoML classification experiment through the UI and sets the 'Timeout (minutes)' field to 30. After 30 minutes the experiment stops while several trial runs are still queued. What happens to the results the experiment has produced so far?

Accepted Answer

AutoML returns the best model found within the time limit, and the generated notebooks and logged runs for completed trials remain available.

Answer

The experiment is discarded entirely because it did not run to completion, and no notebooks or metrics are saved.

Answer

AutoML automatically extends the timeout until all queued trials finish before returning any results.

Answer

Only the data exploration notebook is retained; all model trial runs are deleted when the timeout is reached.

Question 9

A demand-planning team wants to use Databricks AutoML through the UI to forecast weekly product sales for the next 12 weeks. Their Delta table contains a 'sales_date' column, a numeric 'units_sold' column, and a 'store_id' column identifying 40 different stores, each with its own time series. Which configuration is required for the AutoML forecasting experiment to produce separate forecasts per store?

Accepted Answer

Set 'units_sold' as the prediction target, 'sales_date' as the time column, and specify 'store_id' as the identity (time series identifier) column, with a horizon of 12 and frequency of weeks.

Answer

Set 'units_sold' as the prediction target, 'sales_date' as the time column, and leave the identity column blank so AutoML automatically detects the 40 series.

Answer

Run 40 separate classification experiments, one per store, each filtered to a single 'store_id'.

Answer

Set 'store_id' as the prediction target and 'sales_date' as the time column, using a horizon of 12 weeks.

Question 10

A data scientist launches a Databricks AutoML forecasting experiment through the UI on a table of daily retail sales. After the experiment completes, they want to understand how far into the future the best model can predict and how AutoML determined the number of future periods to generate. Which combination of AutoML forecasting configuration settings controls the number of future time steps produced by the resulting model?

Accepted Answer

The forecast frequency (e.g., daily) and the forecast horizon, which together define how many future periods are generated

Answer

The primary metric and the timeout, which cap how many future periods the model can produce

Answer

The number of trials and the training/validation split ratio, which determine the future window size

Answer

The time column data type and the country holidays setting, which set the prediction length automatically

Question 11

A data scientist runs a Databricks AutoML classification experiment. After it completes, they want to improve on the best model by manually adjusting a preprocessing step and re-tuning some hyperparameters, while keeping AutoML's automatically generated feature engineering as a starting point. What is the recommended way to do this within the AutoML workflow?

Accepted Answer

Open the generated source-code notebook for the best trial, clone or edit it, and modify the preprocessing and hyperparameters directly since AutoML uses a glass-box approach

Answer

Re-run the AutoML experiment with a longer timeout, because AutoML will eventually discover the manual changes on its own

Answer

Export the best model as a black-box artifact from the Model Registry and retrain it externally, since AutoML notebooks are read-only and cannot be modified

Answer

Delete the AutoML experiment and rebuild the entire pipeline from scratch in a new notebook without any AutoML assistance

Question 12

A data scientist launches an AutoML regression experiment through the Databricks UI to predict housing prices. After the experiment starts, they want to review how AutoML profiled the input dataset — including missing value counts, feature distributions, and detected column types — before any models were trained. Which AutoML-generated artifact should they open?

Accepted Answer

The data exploration notebook automatically generated at the start of the experiment

Answer

The best trial notebook linked from the top-performing run

Answer

The MLflow run's metrics tab for the primary run

Answer

The model artifact's MLmodel file in the run's artifacts

Question 13

A data scientist launches a Databricks AutoML regression experiment through the UI to predict house prices. They want AutoML to rank and select the best trial based on how well predictions match actual values on a scale that penalizes larger errors more heavily, expressed in the same units as the target. Which primary evaluation metric should they configure for the experiment?

Accepted Answer

Root mean squared error (RMSE)

Answer

R-squared (R2)

Answer

F1 score

Answer

Mean absolute error (MAE)

Question 14

A data scientist is preparing a customer churn dataset. The 'age' feature is continuous and highly right-skewed, and the team wants to convert it into categorical bins so that each bin contains roughly the same number of customers, which will help a downstream tree ensemble handle the skew more evenly. Which binning strategy should they apply?

Accepted Answer

Quantile (equal-frequency) binning, which places cut points so each bin holds approximately the same number of records

Answer

Equal-width binning, which divides the age range into intervals of identical size

Answer

Standard scaling the age column so it has mean 0 and standard deviation 1

Answer

One-hot encoding the raw age values as-is into indicator columns

Question 15

During exploratory data analysis on a Spark DataFrame of customer records, a data scientist computes summary statistics with df.describe() and notices that the numeric column 'account_region_code' has a standard deviation of 0.0 and identical min, max, and mean values across all 500,000 rows. What is the most appropriate action to take before model training?

Accepted Answer

Drop the column, because a feature with zero variance provides no discriminative information for the model

Answer

Apply StandardScaler to the column so its variance is normalized to 1.0 before training

Answer

Impute the column values with the median to introduce variability into the feature

Answer

Keep the column and apply one-hot encoding since it appears to be a categorical identifier

Question 16

A data scientist is preparing features for a linear regression model that predicts house prices. During EDA, they compute a Pearson correlation matrix and notice that 'total_square_feet' and 'number_of_rooms' have a correlation coefficient of 0.94 with each other, while each correlates around 0.60 with the target price. What is the most appropriate action to take before training the linear model?

Accepted Answer

Drop one of the two highly correlated features (or combine them) to reduce multicollinearity between predictors

Answer

Drop both features because their correlation with the target is only moderate (0.60)

Answer

Keep both features unchanged, since high correlation between predictors improves a linear model's accuracy

Answer

Apply StandardScaler to both features, which will eliminate the correlation between them

Question 17

A data scientist is building a regression model on a small dataset of only 300 labeled records. A single 80/20 train/test split produces validation metrics that vary widely each time the seed changes, making it hard to trust the model's estimated performance. The scientist wants a more reliable estimate of generalization error before registering the model. Which change to the training/validation strategy best addresses this problem?

Accepted Answer

Use k-fold cross-validation (e.g., 5-fold) so every record is used for both training and validation across folds, then average the metrics

Answer

Increase the test split from 20% to 50% so the validation set is larger and more stable

Answer

Remove the random seed entirely so results reflect natural variability

Answer

Train on 100% of the data and report the training-set metric as the performance estimate

Question 18

A data scientist working in a Databricks notebook on a Databricks Runtime for ML cluster wants to reuse a set of helper functions that are defined in a separate notebook named 'utils' located in the same workspace folder. They want the functions to become directly callable in their current notebook's Python session. Which approach accomplishes this?

Accepted Answer

Use the magic command %run ./utils at the top of the current notebook

Answer

Use dbutils.notebook.run('./utils', 60) to import the functions

Answer

Use the magic command %pip install ./utils to load the helper notebook

Answer

Use import utils as a standard Python module import

Question 19

A data scientist is setting up a new cluster to train a deep learning model with TensorFlow and track experiments with MLflow. They want an environment where common ML libraries and MLflow are preinstalled and GPU support is available without manual pip installs or configuration. Which cluster configuration best satisfies these requirements?

Accepted Answer

A cluster running the Databricks Runtime for Machine Learning (GPU variant)

Answer

A cluster running the standard Databricks Runtime, with TensorFlow and MLflow installed via a cluster-scoped init script

Answer

A cluster running the standard Databricks Runtime, installing all libraries through %pip in the notebook

Answer

A SQL warehouse configured with a GPU-backed compute profile

Question 20

A retail analytics team needs to score customer churn predictions for its entire 40-million-row customer base once every night. The results are written to a Delta table that a BI dashboard reads the next morning. There is no requirement for sub-second response times, and no external application calls the model interactively. Which deployment paradigm best fits this workload?

Accepted Answer

Batch inference using an MLflow model loaded as a Spark UDF over the full customer DataFrame

Answer

Real-time inference via a Databricks Model Serving endpoint queried per customer

Answer

Streaming inference with Structured Streaming reading from a continuous source

Answer

Real-time serving with an autoscaling endpoint behind a REST API gateway

Question 21

During exploratory data analysis on a housing dataset, an analyst computes a covariance matrix between 'square_footage' (measured in thousands of square feet) and 'lot_size' (measured in acres). The covariance value is 45.2, and the analyst wants to determine the strength of the linear relationship between the two features to decide whether one is redundant. Why is the correlation coefficient a more appropriate metric than the raw covariance value here?

Accepted Answer

Correlation is normalized to a range between -1 and 1, making it comparable across features with different units and scales, whereas covariance magnitude depends on the units of measurement.

Answer

Correlation can detect non-linear relationships between features, whereas covariance only measures linear relationships.

Answer

Correlation handles missing values automatically, while covariance requires complete cases and will error on nulls.

Answer

Covariance is always positive and therefore cannot indicate the direction of a relationship, while correlation can.

Question 22

A data scientist runs df.describe() on a Spark DataFrame of loan applications before modeling. For the 'annual_income' column, the summary shows: count = 48,500 (out of 50,000 rows), min = -1, mean = 62,300, 50% (median) = 55,000, max = 9,999,999. Which single observation from these statistics most strongly indicates a data quality problem that should be investigated before feature engineering?

Accepted Answer

The min value of -1 is present, since annual income cannot logically be negative.

Answer

The mean (62,300) is higher than the median (55,000), which proves the data is corrupted.

Answer

The count of 48,500 is fine because Spark automatically imputes the 1,500 missing values.

Answer

The max of 9,999,999 confirms the column must be dropped because it contains only outliers.

Question 23

A data scientist is building a scikit-learn pipeline to preprocess a dataset before training a logistic regression model. The dataset contains numeric features with widely different ranges and several rows with missing values in those numeric columns. The scientist wants to (1) impute missing numeric values with the column median and (2) standardize the features. To avoid data leakage and produce correct results, in what order should these steps be arranged inside the pipeline, and how should they be fit?

Accepted Answer

Fit the median imputer first, then the StandardScaler, chaining both inside a single Pipeline that is fit only on the training data.

Answer

Fit the StandardScaler first on the full dataset, then impute missing values with the median so the scaler sees complete data.

Answer

Impute and scale the entire dataset before performing the train/test split so both sets share identical statistics.

Answer

Fit the imputer and scaler separately on both the training and test sets so each set is scaled to its own distribution.

Question 24

A data scientist is using a Databricks Feature Engineering client to build a training dataset. They call fe.create_training_set() with a DataFrame containing a 'customer_id' column and several FeatureLookup objects that pull columns from a feature table keyed on 'customer_id'. When they later call fe.log_model() and score new data with fe.score_batch(), they want the model to automatically retrieve the looked-up features at inference time using only the raw input containing 'customer_id'. Which behavior of the returned TrainingSet supports this workflow?

Accepted Answer

The TrainingSet packages the feature lookup metadata so that a model logged with fe.log_model() records the feature table dependencies and can rehydrate features automatically during fe.score_batch().

Answer

The TrainingSet materializes the joined features into a new permanent Unity Catalog table that fe.score_batch() must be pointed to explicitly at inference time.

Answer

The TrainingSet requires the data scientist to manually re-join the feature table during scoring because lookup metadata is not persisted with the model.

Answer

The TrainingSet strips the 'customer_id' lookup key from the training DataFrame, so it must be re-added by hand before calling fe.score_batch().

Question 25

A data scientist creates a feature table in Unity Catalog containing customer aggregates keyed by customer_id. They train a model using a FeatureLookup so that the training set joins these features automatically. When the model is later deployed for batch inference, the inference DataFrame only contains customer_id and a few raw request columns. Which behavior should the data scientist expect when using score_batch (or the equivalent Feature Engineering scoring API) with this logged model?

Accepted Answer

The scoring API automatically retrieves the required features from the feature table using customer_id, so the inference DataFrame does not need to include the looked-up feature columns.

Answer

The inference DataFrame must contain all feature columns because the model does not store any information about the feature lookups used during training.

Answer

The features must be manually joined to the inference DataFrame before scoring, since feature metadata is discarded once the model is logged.

Answer

Batch inference will fail unless the feature table is first exported to an online store, because offline feature tables cannot be used at scoring time.