Microsoft DP-750 Practice Test Questions Answers

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.

To record historical changes: Implement a Type 2 slowly changing dimension (SCD).

To support temporal analysis: Use Delta Lake time travel.

A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.

For authentication, a Managed Identity (via a Databricks Access Connector) is the right choice. The Access Connector wraps an Azure-managed identity so Databricks can authenticate to Azure Storage without any credentials being stored in the workspace. The cloud security team controls the identity through Azure RBAC — there are no secrets to rotate or leak inside Databricks.

For the permission, READ FILES on the volume is exactly right. It allows users to read and list files through the volume path while blocking writes, deletes, and modifications. This is the minimum necessary access, honouring the principle of least privilege.

WRITE FILES would allow modifications, contradicting ' users can access but NOT modify. ' ALL PRIVILEGES grants far more than needed. Service principals with stored client secrets would mean credentials inside Databricks, violating the ' does not require storing credentials ' requirement.

Registering an ADLS Gen2 account as an external location in Unity Catalog requires three steps in a specific order:

Step 1: Create a Databricks Access Connector. This Azure resource wraps a system-assigned or user-assigned managed identity. It is the credential-less authentication bridge between Databricks and Azure Storage — no SAS tokens or access keys are stored anywhere in the workspace.

Step 2: Create a Storage Credential in Unity Catalog that references the Access Connector. This object tells Unity Catalog ' use this identity when accessing storage. ' The Storage Credential is the reusable authentication object.

Step 3: Create an External Location that maps a Unity Catalog path to the specific ADLS Gen2 container using the Storage Credential. This is the object that grants Databricks users access to files under that path, and it respects the principle of least privilege by scoping access to a specific container.

A CHECK constraint enforces a Boolean condition whenever data is inserted or updated. The constraint can require payment_amount > = 0 AND payment_amount < = 10000, causing a transaction containing an invalid value to fail instead of allowing the record into Payments. This provides storage-level data-quality enforcement regardless of which pipeline, notebook, or SQL statement performs the write. Row-level security controls which existing records users can see; it does not reject invalid writes. SELECT statements filter results only when they are executed and therefore cannot protect the underlying table. Table update triggers are not the standard Delta Lake mechanism for this requirement. Azure Databricks classifies CHECK constraints as enforced constraints and rejects transactions when their conditions are violated. Microsoft Learn