63 real Databricks questions from the Big Data bank, as asked in Indian campus drives and tech interviews. Every question has a verified answer and an AI-tutor explanation on placd — free to start.
1. What is Unity Catalog?
Junior
A.the Databricks serverless warehouse offering for running BI and ad-hoc SQL on lakehouse tables with Photon
B.a Databricks compute option that runs SQL on instantly available managed clusters, removing warehouse startup and idle-capacity management
C.Lakeflow pipeline data-quality rules that validate rows and can warn, drop, or fail the pipeline on violations
D.the Databricks unified governance layer that centralizes access control, lineage, and discovery across data and AI assets
A.Unity Catalog — the Databricks unified governance layer that centralizes access control, lineage, and discovery across data and AI assets
B.Unity Catalog — the Databricks vectorized C++ query engine that accelerates SQL and DataFrame workloads while staying API compatible with Spark
C.Unity Catalog — a declarative data-quality rule in Lakeflow Declarative Pipelines that validates rows and can warn, drop, or fail the pipeline on violation
D.Unity Catalog — Databricks compute managed entirely by the platform so users run workloads without provisioning or sizing clusters
5. Which term means: "the Databricks vectorized C++ query engine that accelerates SQL and DataFrame workloads while staying API compatible with Spark"?
A.the Databricks tradeoff where Liquid Clustering adapts layout to evolving query patterns without the rigidity of physical partitions
B.the Databricks feature (CLUSTER BY AUTO) where predictive optimization chooses and updates clustering keys for a managed table based on query patterns
C.the Databricks table layout that replaces partitioning and Z-ordering with flexible clustering keys that can change without rewriting data
D.an open protocol from Databricks for securely sharing live data across organizations without copying it
A.Delta Sharing — an open protocol from Databricks for securely sharing live data across organizations without copying it
B.Delta Sharing — the Databricks feature (CLUSTER BY AUTO) where predictive optimization chooses and updates clustering keys for a managed table based on query patterns
C.Delta Sharing — Databricks compute managed entirely by the platform so users run workloads without provisioning or sizing clusters
D.Delta Sharing — the Databricks feature that automatically runs maintenance such as compaction, clustering, and vacuum on managed tables based on usage
11. Which term means: "the Databricks framework, evolved from Delta Live Tables, where you declare target tables and quality expectations and it manages the pipeline"?
A.Lakeflow Declarative Pipelines — Lakeflow pipeline data-quality rules that validate rows and can warn, drop, or fail the pipeline on violations
B.Lakeflow Declarative Pipelines — the Databricks unified governance layer that centralizes access control, lineage, and discovery across data and AI assets
C.Lakeflow Declarative Pipelines — the Databricks framework, evolved from Delta Live Tables, where you declare target tables and quality expectations and it manages the pipeline
D.Lakeflow Declarative Pipelines — the Databricks tradeoff where Liquid Clustering adapts layout to evolving query patterns without the rigidity of physical partitions
A.Databricks SQL — an open protocol from Databricks for securely sharing live data across organizations without copying it
B.Databricks SQL — the Databricks table layout that replaces partitioning and Z-ordering with flexible clustering keys that can change without rewriting data
C.Databricks SQL — Lakeflow pipeline data-quality rules that validate rows and can warn, drop, or fail the pipeline on violations
D.Databricks SQL — the Databricks serverless warehouse offering for running BI and ad-hoc SQL on lakehouse tables with Photon
A.Unity Catalog lineage — Databricks-managed Unity Catalog tables exposing operational metadata such as billing, audit logs, and lineage for analysis
B.Unity Catalog lineage — the Databricks table layout that replaces partitioning and Z-ordering with flexible clustering keys that can change without rewriting data
C.Unity Catalog lineage — the Databricks serverless warehouse offering for running BI and ad-hoc SQL on lakehouse tables with Photon
D.Unity Catalog lineage — the automatic column- and table-level lineage Unity Catalog captures across queries, notebooks, and pipelines
23. Which term means: "the Databricks tradeoff where Liquid Clustering adapts layout to evolving query patterns without the rigidity of physical partitions"?
A.Liquid Clustering vs partitioning — Databricks compute managed entirely by the platform so users run workloads without provisioning or sizing clusters
B.Liquid Clustering vs partitioning — the Databricks serverless warehouse offering for running BI and ad-hoc SQL on lakehouse tables with Photon
C.Liquid Clustering vs partitioning — the Databricks tradeoff where Liquid Clustering adapts layout to evolving query patterns without the rigidity of physical partitions
D.Liquid Clustering vs partitioning — Lakeflow pipeline data-quality rules that validate rows and can warn, drop, or fail the pipeline on violations
A.Serverless compute — Databricks compute managed entirely by the platform so users run workloads without provisioning or sizing clusters
B.Serverless compute — the Databricks feature that automatically runs maintenance such as compaction, clustering, and vacuum on managed tables based on usage
C.Serverless compute — the Databricks natural-language interface that lets business users ask questions of governed data and get SQL-backed answers
D.Serverless compute — Databricks-managed Unity Catalog tables exposing operational metadata such as billing, audit logs, and lineage for analysis
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