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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`;

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Unity Catalog — Architecture & Real-World Usage

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

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Photon Execution Engine — When & Why to Use It

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"))

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Delta Live Tables (DLT Pipelines) — Hands-On Concepts

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(...)

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Databricks Feature Store — Central Feature Management

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.

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Table & Column Lineage — Data Tracking

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​

Think of Databricks data quality like a factory assembly line:

DLT Expectations = quality inspectors on the line Delta constraints = machines that physically prevent defects Great Expectations = independent auditors Alerts & metrics = dashboards showing defect rates

If bad data enters, it’s either blocked, quarantined, or logged, not silently passed downstream.

Professional / Hands-On​

In Databricks, I implement data quality using Delta Live Tables (DLT) expectations, constraint enforcement in Delta Lake, and data validation frameworks like Great Expectations, combined with monitoring and alerting to ensure issues are caught early in pipelines.

  • Use Delta Live Tables expectations:
import dlt
from pyspark.sql.functions import col

@dlt.table
@dlt.expect_or_drop("valid_id", "id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def cleaned_orders():
    return spark.read.table("raw.orders")

Key interview points:

expect → logs violations expect_or_drop → removes bad records expect_or_fail → stops the pipeline Quality metrics are automatically tracked in DLT UI

👉 This is Databricks’ native, production-grade approach.

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 Streaming processes data as an unbounded table with a defined schema, exactly-once guarantees, and support for event time and watermarks. Unstructured streaming refers to raw data ingestion where schema is unknown or evolving, such as logs or sensor data, and schema is applied later. In Databricks, we typically ingest unstructured data into a Bronze layer and convert it into structured streaming for reliable processing.

  • 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​

Databricks Workflows orchestrate end-to-end data pipelines by chaining notebooks, jobs, and Delta Live Tables with dependencies. They provide scheduling, retries, alerts, and monitoring, ensuring reliable, automated execution of production data and ML workflows.

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

💡 Want a deeper, story-driven explanation?
Explore a real-world, modern data story walkthrough here →
Databricks Workflows (New) — Production Orchestration

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​

Databricks integrates with BI tools like Power BI, Tableau, and Looker using JDBC/ODBC connectors or Databricks SQL endpoints. BI tools query Delta tables directly through SQL warehouses, enabling secure, scalable, and near real-time analytics without data movement.

  • 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​

In Databricks, I build end-to-end ETL pipelines using the medallion architecture. I extract data from sources like S3, ADLS, or Kafka into Bronze Delta tables, apply transformations and data quality checks using Spark or Delta Live Tables to create Silver tables, and finally load curated data into Gold tables optimized for BI or ML. Pipelines are orchestrated using Databricks Workflows, with checkpoints, retries, and monitoring for reliability.

  • 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​

Databricks SQL is a SQL analytics layer in Databricks that allows users to query Delta tables using standard SQL. It’s used to build interactive queries, dashboards, and alerts on top of data stored in the lake, enabling BI tools and analysts to access data without Spark code.

  • 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
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