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PySparkSnowflakeDatabricks

Must-Know Databricks Interview Questions & Answers (Real Company Scenarios) – Part 5

46. How is MLflow integrated with Databricks for model management?​

Story-Driven​

MLflow is like a lab notebook for your machine learning models in Databricks. Every experiment, parameter, and result is automatically tracked, so you never lose your progress.

Professional / Hands-On​

  • Databricks comes pre-integrated with MLflow.
  • Key capabilities:
    • Tracking experiments: Parameters, metrics, artifacts.
    • Model registry: Versioning, staging, production deployment.
  • Example:
import mlflow
mlflow.set_experiment("/Users/john/ml_experiment")
with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "model")

47. Explain Databricks Unity Catalog​

Story-Driven​

Unity Catalog is like a single master key to your data kingdom. It centralizes access, permissions, and governance across all Databricks workspaces.

Professional / Hands-On​

  • Unity Catalog provides:

    • Centralized data governance
    • Fine-grained access control
    • Cross-workspace data sharing

  • Manage permissions via SQL or workspace UI:

GRANT SELECT ON TABLE sales TO `analyst_group`;

48. How do you handle multi-cloud deployments in Databricks?​

Story-Driven​

Multi-cloud is like running your stores in New York, London, and Tokyo. You want consistent inventory, management, and workflows everywhere.

Professional / Hands-On​

  • Databricks supports deployment on AWS, Azure, GCP.

  • Strategies:

    • Use Terraform or Databricks CLI for infrastructure as code.
    • Maintain consistent notebooks, jobs, and cluster configurations.
    • Use Unity Catalog to manage shared data across clouds.

  • Benefits: High availability, reduced vendor lock-in, disaster recovery.

49. Difference between Photon Engine vs Spark Engine​

Story-Driven​

Photon Engine is like a sports car tuned for speed, while Spark Engine is the SUV—reliable, capable, but slower for certain workloads.

Professional / Hands-On​

FeatureSpark EnginePhoton Engine
ExecutionJVM-basedNative vectorized engine
SpeedStandardFaster for SQL and Delta queries
Use CaseGeneral workloadsAnalytics-heavy workloads

50. Describe a scenario where Delta Live Tables can be used​

Story-Driven​

Delta Live Tables is like an autopilot system for your ETL pipelines. It ensures your data flows, transforms, and updates continuously without manual intervention.

Professional / Hands-On​

  • Scenario: Streaming clickstream data for analytics.

  • Steps:

    1. Define transformations declaratively.
    2. Delta Live Tables automatically manages pipelines, dependencies, and quality checks.
from dlt import table

@table
def cleaned_clickstream():
    return (spark.readStream.format("json").load("/raw/clickstream")
            .filter("event_type IS NOT NULL"))

51. How do you implement model monitoring in Databricks?​

Story-Driven​

Imagine sending your car to the mechanic for regular checkups. Model monitoring ensures your deployed ML models continue performing well.

Professional / Hands-On​

  • Monitor metrics drift, prediction distribution, and errors.
  • Use MLflow Model Registry + Delta tables to log predictions.
  • Alert on anomalies with Databricks jobs or notebooks.

52. Explain Databricks Feature Store​

Story-Driven​

Feature Store is like a pantry where all your preprocessed ingredients (features) are stored, so multiple chefs (ML models) can reuse them consistently.

Professional / Hands-On​

  • Centralized repository for features.
  • Supports online (real-time) and offline (batch) features.
  • Example:
from databricks.feature_store import FeatureStoreClient
fs = FeatureStoreClient()
fs.create_feature_table(...)

53. How do you perform data lineage tracking in Databricks?​

Story-Driven​

Data lineage is like tracking the journey of a letter from sender to recipient. You always know where the data came from, and what transformations it went through.

Professional / Hands-On​

  • Use Unity Catalog and Delta Live Tables.
  • Databricks provides visual lineage in UI.
  • Helps with auditing, debugging, and compliance.

54. How do you manage large-scale ML training in Databricks?​

Story-Driven​

Large-scale ML training is like training a fleet of robots simultaneously—you need coordination, resources, and monitoring to finish efficiently.

Professional / Hands-On​

  • Use distributed ML frameworks: PyTorch, TensorFlow, XGBoost.
  • Use GPU-enabled clusters.
  • Integrate with MLflow for experiment tracking.
  • Example:
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression(maxIter=10)
lr_model = lr.fit(training_data)

55. How do you implement data quality checks in Databricks?​

Story-Driven​

Data quality checks are like inspecting ingredients before cooking—they ensure your final dish (analytics/ML) is accurate.

Professional / Hands-On​

  • Use Delta Live Tables expectations:
from dlt import expectation

@expectation
def no_null_ids(df):
    return df["id"].isNotNull()
  • Use assertions in notebooks or Great Expectations integration.

56. Explain structured vs unstructured streaming in Databricks​

Story-Driven​

Structured streaming is like a conveyor belt with fixed compartments; unstructured streaming is more like a river, unpredictable and flexible.

Professional / Hands-On​

  • Structured: Works with DataFrames, schema enforced.
  • Unstructured: Raw data streams, schema not enforced.
  • Use case: JSON logs → structured streaming; raw sensor data → unstructured streaming.

57. What are Databricks Workflows and why are they useful?​

Story-Driven​

Workflows are like a factory assembly line—they ensure every step runs in order and can be monitored automatically.

Professional / Hands-On​

  • Combine notebooks, jobs, and pipelines.
  • Supports dependency scheduling, retries, and alerts.
  • Use UI or REST API to manage workflows.

58. How do you integrate Databricks with BI tools?​

Story-Driven​

Connecting Databricks to BI tools is like plugging a data river into your analytics dashboard—you get real-time insights without manual exports.

Professional / Hands-On​

  • Supported tools: Power BI, Tableau, Looker.
  • Use JDBC/ODBC connectors.
  • Example: Query Delta tables directly from Tableau via Databricks SQL endpoint.

59. How do you implement end-to-end ETL pipelines in Databricks?​

Story-Driven​

ETL pipelines are like a fully automated kitchen: you gather ingredients (Extract), prep them (Transform), and serve them (Load) consistently.

Professional / Hands-On​

  • Extract: Read from S3/ADLS/GCS.
  • Transform: Spark/Delta Live Tables.
  • Load: Delta tables or data warehouse.
df = spark.read.format("parquet").load("/raw/sales")
df_clean = df.dropna().filter("amount > 0")
df_clean.write.format("delta").save("/delta/sales")

60. What is Databricks SQL and how is it used?​

Story-Driven​

Databricks SQL is like the universal translator for your data—anyone who knows SQL can query, visualize, and report data easily.

Professional / Hands-On​

  • SQL-based analytics on Delta tables.
  • Supports dashboards, queries, and alerts.
  • Example:
SELECT region, SUM(amount) AS total_sales
FROM sales_delta
GROUP BY region
ORDER BY total_sales DESC