DP-750 Exam Dumps : Implementing Data Engineering Solutions Using Azure Databricks

The right ingestion tool depends on the source characteristics:

File-based telemetry and maintenance data → Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.

Real-time telemetry from Event Hubs → Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying ' resume processing after failures without reprocessing. '

Structured maintenance data from PostgreSQL → JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.

Daily CSV ERP extracts (50–100 GB) → COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.

The correct answer is B — define a pipeline expectation.

SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline ' s event log as expectation violations — available for review without any extra code.

Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code.

Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views — they ' re not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the ' pipeline continues to run ' requirement.

Delta Sharing is designed to share governed data securely with recipients outside an Azure Databricks workspace. The external organization can query the shared data from its own compatible compute platform without receiving workspace access or requiring a Databricks SQL warehouse. Unity Catalog centrally controls which tables, views, or other objects are included in the share and which recipients can access them. Moving files to an SFTP server creates additional copies and requires custom transfer and security administration. Lakeflow Connect is intended for ingesting data into Databricks rather than sharing curated data externally. Granting workspace access or creating a SQL warehouse would require the recipient to use Databricks-managed resources. Delta Sharing therefore provides the required open access model, centralized governance, and minimal administrative effort.