Domain 2: Feature Engineering & Data Prep

Databricks Certified Machine Learning Associate · 62 questions

• What is the primary purpose of the Databricks Feature Store?
• Which method is used to write a DataFrame as a new feature table in the Databricks Feature Store?
• What is the main performance advantage of a Pandas UDF (vectorized UDF) over a standard Python UDF in Spark?
• How can Delta Lake's time travel feature help prevent training-serving skew in ML pipelines?
• A feature engineering pipeline computes 50 derived features from raw data. To avoid recomputing these features on every model training run, what is the best practice in Databricks?
• When preparing data with missing values for a Spark MLlib pipeline, which approach allows the Imputer transformer to handle multiple columns at once?
• When creating a training dataset using the Databricks Feature Store, what does fs.createtrainingset() require as a mandatory input alongside the feature lookups?
• In a Spark ML Pipeline, what is the correct order of transformers when converting a categorical string column to a numeric format suitable for tree-based models?
• A data scientist fits a StandardScaler on the entire dataset before splitting into train/test sets. What problem does this cause?
• After training a Random Forest classifier with Spark MLlib, which attribute provides feature importance scores?
• In the Databricks Feature Store, what is the purpose of specifying a timestamplookupkey in a FeatureLookup?
• Which PySpark construct is used to compute a rolling 7-day sum of transactions per user as a feature?
• Spark MLlib's VectorAssembler fails when input columns contain null values. Which parameter controls how VectorAssembler handles nulls?
• When a categorical feature has millions of unique values (e.g., URL paths), which Spark MLlib transformer handles encoding without creating millions of columns?
• In Spark MLlib, what is the correct pipeline to convert raw text documents to TF-IDF feature vectors?
• In Spark MLlib, what is the difference between Normalizer and StandardScaler?
• When applying PCA in Spark MLlib, what does the k parameter control?
• A feature engineering pipeline tries to write a new batch with an additional column to an existing Delta feature table. What happens by default?
• Which Spark MLlib transformer creates polynomial features and interaction terms from numeric features?
• When a feature engineering pipeline needs to connect to an external database, what is the Databricks-recommended way to handle credentials?
• What is the primary difference between offline and online feature tables in Databricks Feature Store?
• Point-in-time correct feature joins prevent which training flaw?
• How does Delta Lake time travel support ML reproducibility?
• FeatureStoreClient.createtrainingset is used to:
• Delta Live Tables (DLT) helps ML feature pipelines by:
• StandardScaler vs MinMaxScaler — which statement is TRUE?
• Fitting scalers on full data before train/test split causes:
• A pitfall of StringIndexer + OneHotEncoder in production is:
• Imputing missing values before tree models vs linear models:
• PCA before clustering high-dimensional numeric features can help by:
• Which feature is MOST suspicious for target leakage in churn prediction?
• Feature tables define primary keys so that:
• Stale online features can cause:
• Feature hashing (HashingTF) is useful for high-cardinality text/categories because:
• Explicit interaction features are MOST helpful for:
• Treating null as an explicit category can be useful when:
• IDF in a Spark text pipeline down-weights terms that:
• Aggressive deletion of outliers before fraud modeling may be harmful because:
• Delta schema enforcement helps ML pipelines by:
• Idempotent feature table writes matter because:
• Feature data contracts specify:
• scorebatch / batch feature lookups help by:
• For point-in-time joins, the label/event timestamp should represent:
• Z-Ordering feature tables on lookup keys can:
• One-hot encoding very high-cardinality categoricals can cause:
• For time-series forecasting models, random K-fold is dangerous because:
• Target encoding categoricals risks leakage unless:
• Removing highly correlated features before linear modeling can:
• Spark ML Imputer stage typically handles:
• Winsorizing extreme feature values can:
• Using learned embeddings as features is common when:
• Binning continuous features can help when:
• Feature Store lineage to models helps answer:
• Frequency encoding categoricals replaces levels with:
• Streaming updates into feature tables are useful when:
• Unstable feature importances across bootstrap resamples suggest:
• RobustScaler uses medians/IQR style scaling to:
• Rare category levels can be grouped into an OTHER bucket to:
• Monotonic constraints in boosting are used when:
• Writing feature tables with merge/upsert semantics helps:
• Quantile regression loss is appropriate when:
• Naive leave-one-out target encoding still risks: