Pass the Microsoft Certified: Azure Databricks Data Engineer DP-750 Questions and answers with ExamsMirror

Data lineage in Unity Catalog is captured automatically — but only when jobs and notebooks run on clusters that are Unity Catalog-aware. Specifically, clusters must use ' Shared ' or ' Single User ' access mode. Clusters set to ' No Isolation Shared ' or legacy ' High Concurrency ' mode do not emit lineage events to the Unity Catalog lineage service.

No instrumentation, logging code, or external tools are required. The lineage service operates transparently, intercepting read and write operations at the Spark plan level and recording the table-to-table and column-to-column relationships.

To review captured lineage, open Catalog Explorer, navigate to the table, and select the Lineage tab. This shows the upstream sources that populate the table and the downstream consumers that read from it — all as an interactive graph, with no additional tooling needed. This built-in visibility is one of the core governance benefits Unity Catalog provides.

The correct answer is B — a job failure.

The requirement draws a clear line: non-critical task failures should be logged silently; notifications should only fire when a critical failure causes the whole job to stop. Configuring the alert on ' Job Failure ' achieves this precisely — the notification triggers when the job itself reaches a Failed terminal state, which only happens when at least one critical task has failed and the job cannot complete.

Option A (task failure) would send a notification for every task-level failure, including non-critical ones. That ' s exactly the noise the question wants to avoid. Option C (job success) would never alert on failures at all. Option D (task success) confirms completion but doesn ' t catch failures.

Setting alerting at the job level rather than the task level is also simpler to configure — you don ' t need to mark individual tasks as critical or non-critical in the notification settings.

Two configurations are needed:

Task dependency with ' All succeeded ' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to ' All succeeded ' — this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.

Repair run for recovery: Lakeflow Jobs ' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair — their results are reused as-is.

Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.

The correct answer is A — Yes.

A foreign catalog created through Lakehouse Federation in Catalog Explorer is the correct solution for all three requirements. Here ' s why it works:

The schemas and tables of DB1 can be queried in Databricks — Lakehouse Federation pushes the query down to the external SQL Server and returns results, so analysts write normal SQL in Databricks.

They appear alongside other Unity Catalog objects — the foreign catalog sits in the same three-tier hierarchy as native catalogs, schemas, and tables, visible in Catalog Explorer alongside all other Unity Catalog assets.

The data is NOT copied into Databricks-managed storage — foreign catalogs query data in place at the source; nothing is replicated or materialised in Databricks storage.

This is exactly the scenario Lakehouse Federation was built for.

The correct answer is C — a Lakeflow Spark Declarative Pipelines (SDP) pipeline.

SDP is tailor-made for exactly this pattern: ingest from cloud storage, transform through cleansing and enrichment stages, and publish Delta tables to Unity Catalog. What sets it apart from the other options is built-in monitoring (the pipeline graph shows row counts, expectation metrics, and run history) and automatic retries (failed tasks retry automatically based on pipeline settings, without manual re-run triggers).

Option A (Structured Streaming job) gives you the streaming engine but nothing else — monitoring, alerting, and retry logic all have to be built from scratch. Option B (scheduled notebook job) is a batch approach that requires manual monitoring and lacks the declarative lineage tracking SDP provides. Option D (Azure Data Factory with data flows) works but adds a separate Azure service to manage, introduces ADF licensing costs, and doesn ' t integrate natively with Unity Catalog governance.

The correct answer is D — apply the main branch changes to Branch1 and resolve the conflicts.

When a merge fails due to conflicts, the right workflow is to bring main ' s changes into the feature branch, resolve conflicts there, and then merge the clean feature branch into main. This is the standard Git conflict resolution pattern — resolve in the feature branch, not in main — because it protects the main branch from partial or broken states during resolution.

Option A (clone Branch1 as a new repository) creates a disconnected copy; it doesn ' t resolve the conflict and breaks the relationship with the remote. Option B (apply changes directly to main) bypasses the feature branch entirely and risks overwriting the other developer ' s work. Option C (clone main as a new repository) again creates a disconnected copy — none of Branch1 ' s changes would be incorporated, and history would be lost.

The correct answer is C. Column masks are the right tool when you need to partially expose sensitive data rather than hide it entirely. A column mask is a SQL function attached to a column that rewrites the returned value based on who is querying. For email_address, the mask returns only the substring after ' @ ' . For credit_card_number, it returns only the last four digits. Business analysts get useful data without seeing anything sensitive, and they can still query the table without errors.

Option A (row-level filters) controls which rows a user sees, not the values within a row — it can ' t partially redact a column. Option B (grant SELECT only on non-sensitive columns) removes the columns entirely, so analysts can ' t see even partial email or card values — that doesn ' t meet the ' can see only partial values ' requirement. Option D (column-level encryption) requires key management infrastructure and decryption at query time, which is significantly more complex than column masking for this use case.

When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream — inserts, updates, and deletes — requires this pattern:

spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' )

The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.

Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.

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.

A Unity Catalog volume is the appropriate location because it provides governed file storage with permissions managed through Unity Catalog. After uploading the library package to the volume, it can be configured as a cluster library on Cluster1. This approach permits centralized access control, auditing, and lifecycle management. Option A installs the library without placing its source under Unity Catalog governance. Option B provides notebook-scoped dependency management but does not, by itself, satisfy the requirement that Unity Catalog control access to the library artifact. A schema is a logical container for tables, views, functions, models, and volumes; a library file cannot be uploaded directly to the schema itself, eliminating option D. Unity Catalog volumes explicitly support storing cluster libraries and job dependencies. Microsoft Learn