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Databricks ML-ASSOC Cheat Sheet: Features, Training, and Deployment

April 13, 2026

Databricks ML-ASSOC cheat sheet for features, training, deployment, traps, and final review.

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Use this for last-mile review. Keep it open while drilling mixed questions. ML-ASSOC usually gets easier when you classify the issue first:

  1. Data and feature lane: feature construction, leakage, or split discipline?
  2. Training and metric lane: metric choice, validation strategy, or imbalance?
  3. MLflow lane: run tracking, artifact logging, reproducibility, or comparison?
  4. Registry and deployment lane: versioning, promotion, inference consistency, or monitoring?

ML workflow map

    flowchart TD
	  Platform["ML Runtimes + AutoML + Feature Tables"] --> Features["Features + Splits + Data Checks"]
	  Features --> Train["Training + Metrics"]
	  Train --> Run["MLflow Run"]
	  Run --> Compare["Run Comparison"]
	  Compare --> Registry["Model Registry"]
	  Registry --> Deploy["Deployment + Monitoring"]
	  DataVer["Data / Feature Version"] -. supports reproducibility .-> Run
	  DataVer -. supports reproducibility .-> Compare

ML-ASSOC answer sequence

Use this when the stem mixes features, splits, metrics, MLflow, registry, or deployment.

    flowchart TD
	  S["Scenario"] --> D["Check data and feature lane"]
	  D --> T["Check training and metric lane"]
	  T --> R["Check MLflow tracking and reproducibility"]
	  R --> P["Check registry, versioning, and promotion"]
	  P --> O["Check deployment and monitoring fit"]

Feature and split rules

If the question is mainly about… Strongest first lane
inputs available only after prediction time leakage risk
train and test data influencing each other split contamination
repeated transformation mismatch in production feature pipeline inconsistency
reproducible training inputs tracked feature prep and stable splits
AutoML or feature-store question Databricks ML platform workflow, not only raw algorithm choice

Databricks ML platform picker

If the question is really about… Strongest first lane
faster model or feature exploration AutoML
reproducible feature reuse across teams feature tables in Unity Catalog
environment optimized for ML work ML runtimes
experiment workflow and run comparison MLflow

Leakage and contamination table

Risk What it looks like Safer approach
feature leakage feature uses future or unavailable information use only information available at prediction time
label leakage feature derives from or strongly encodes the target remove or rebuild the feature
train/test contamination transforms or statistics fit on the full dataset fit transforms on train only and apply to test

Data-processing quick rules

If the issue is mainly about… Strongest first lane
broad summary of a Spark DataFrame .summary() or built-in summary tools
extreme values harming training outlier review using standard deviation or IQR logic
comparing two categorical or continuous features choose the comparison and visualization that matches the data type
missing values pick mean, median, or mode based on the feature and distribution
categorical encoding use one-hot encoding only where it actually fits
skewed numeric feature consider log transform where appropriate

Metric chooser

Task Common metrics What to watch
classification accuracy, precision, recall, F1, AUC imbalance and false-positive/false-negative cost
regression RMSE, MAE, R² sensitivity to large errors and interpretability

Metric traps

Trap Better reading
using accuracy on a clearly imbalanced problem think precision, recall, F1, or AUC depending on the trade-off
choosing one regression metric without error-context thinking classify whether large errors should be penalized more heavily
trusting a very strong score immediately check leakage, split quality, and feature pipeline consistency first

MLflow boundaries

MLflow concept What it stores Why the exam cares
run one training or evaluation attempt comparison and reproducibility
params model and training configuration explain how the run was produced
metrics evaluation numbers rank candidates consistently
artifacts plots, files, models, reports reproduce and inspect outputs
registry named model versions and lifecycle management controlled promotion and deployment

Fast MLflow picker

If the question is mainly about… Strongest first lane
comparing experiments runs with logged params and metrics
preserving the produced model and supporting files artifacts
controlled model version promotion registry
explaining how a result happened params, metrics, artifacts, and data or version context together
promoting by champion or challenger pattern aliases in the registry

Reproducibility rules

  • log params, metrics, and the model artifact
  • keep track of the data or feature version when it materially affects the result
  • avoid manual side notes as the only record of a training run
  • treat reproducibility as part of the experiment, not a later cleanup task

Training and evaluation quick rules

Requirement Strongest first lane
compare two candidate models fairly same split discipline and comparable metrics
explain why a model improved compare logged runs and feature or config differences
too-good-to-be-true performance investigate leakage, split quality, and artifact consistency
offline result differs from production check schema, preprocessing, feature availability, and serving consistency
choose search strategy random, grid, or Bayesian search based on the search need and cost
estimate training count in grid search plus CV multiply parameter combinations by fold count

Registry and deployment cues

Step What happens Why it matters
register model create named model with versions stable deployment reference
create new version tie a version back to a run or model artifact traceability
promote version controlled movement toward production use governance and rollout discipline
deploy or serve expose the chosen version for inference consistency matters more than novelty
split traffic between endpoints compare live realtime inference behavior safely rollout control

Deployment traps

Trap Better reading
strong offline metrics mean production is solved check preprocessing, schema, and feature parity
registry is just storage registry adds versioning and promotion control
logging a metric is enough for reproducibility params and artifacts matter too

High-confusion pairs

Pair Keep this distinction clear
params vs metrics training configuration versus evaluation results
run vs registry version experiment attempt versus promoted managed model version
leakage vs class imbalance bad feature boundary versus data distribution problem
offline success vs production success benchmark result versus operational consistency
estimator vs transformer learning component versus data-transformation component
AutoML vs MLflow automated model search aid versus lifecycle tracking system
feature table vs registered model reusable features versus managed model artifact lineage
batch vs realtime vs streaming inference bulk scoring versus endpoint serving versus continuous event-driven inference

Last 15-minute review

Recheck this Because the miss often hides here
what information is available at prediction time leakage questions often hinge on that boundary
metric choice for the real business risk accuracy is not the default winner
what MLflow logs at each layer runs, artifacts, and registry roles blur easily
reproducibility versus mere experimentation the exam prefers the controlled workflow
registry and deployment consistency versioning and inference parity matter
feature-store, AutoML, and registry roles Databricks ML nouns blur easily under time pressure

What strong ML-ASSOC answers usually do

  • protect reproducibility before chasing model complexity
  • catch leakage and bad metric choice early
  • keep feature engineering, training, evaluation, and deployment roles separate
  • understand what MLflow stores, compares, versions, and promotes at each layer
Revised on Sunday, May 10, 2026
Study Plan
Sample Questions