Free Practice Questions for Microsoft DP-750 Exam

Two task-level settings solve this:

Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.

Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked — it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.

These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.

The correct answer is B — No.

This is a common Python-to-PySpark trap. In pure Python, comparing a value to None with != works as expected. In PySpark, null comparisons follow SQL null semantics: any comparison involving NULL returns NULL (not True or False). So df.filter(df.order_amount != None) doesn ' t evaluate to True for non-null rows — the comparison itself returns NULL for null values, and Spark interprets NULL in a filter as False, effectively dropping null rows. But the behaviour is undefined in edge cases and is not the documented approach.

More practically, Python ' s None and Spark ' s SQL NULL are different concepts. PySpark Column objects don ' t support Python ' s native equality/inequality semantics for null checking. The result is typically an empty DataFrame or incorrect filtering behaviour.

Always use .isNotNull() or .isNull() for null checks in PySpark column expressions. These methods are specifically designed for SQL-null-aware comparisons and produce correct, predictable results.

Job1 should contain one ingestion task that acts as the common upstream dependency for two separate transformation tasks. Once ingestion succeeds, Lakeflow Jobs can start both downstream tasks concurrently because neither transformation depends on the other. This design represents the actual workflow, avoids duplicated ingestion, and reduces total execution time through parallelism. Creating two ingestion tasks would repeat the same source processing and could introduce inconsistent results or unnecessary costs. A single sequential task would prevent parallel transformation and make failures harder to isolate and retry. Defining three independent tasks without dependencies could allow transformations to start before ingestion has completed. An explicit directed task graph therefore provides the required execution order while preserving parallelism for independent downstream processing.

Two features work together to keep performance consistent and update costs low:

OPTIMIZE with ZORDER BY (CustomerId, EventDate). Z-Ordering co-locates rows with the same CustomerId and EventDate values in the same Parquet files. When a query filters on those columns, the Delta engine uses file statistics to skip files that can ' t possibly contain matching rows (data skipping). As the table grows, skipping scales proportionally — query time stays consistent.

Deletion Vectors (delta.enableDeletionVectors = true). When a row is updated or deleted, instead of rewriting the entire Parquet file, Delta marks the affected row in a small companion deletion vector file. This dramatically reduces write amplification for the frequent compliance-driven updates and deletions the question describes. Actual file rewrites are deferred to the next OPTIMIZE run.

The correct answer is A. The Unity Catalog DDL for creating a schema inside a specific catalog and setting a custom managed storage path uses the three-part name (catalog.schema) and the MANAGED LOCATION clause:

CREATE SCHEMA catalog1.schema2 MANAGED LOCATION ' abfss://... ' ;

The three-part name explicitly places the schema inside catalog1. MANAGED LOCATION tells Unity Catalog where to store managed tables and volumes created under this schema — any managed table without its own explicit location will inherit this path.

Option B uses CREATE CATALOG, which creates an entirely new catalog rather than a schema. Option C uses the LOCATION keyword without MANAGED — that syntax is for external locations, not for overriding the managed storage path of a schema. Option D uses WITH DBPROPERTIES, which stores arbitrary key-value metadata but has no effect on where Unity Catalog physically stores data.

The correct answers are C and D. Together they deliver cost-efficient autoscaling:

D (Enable autoscaling) allows the cluster to grow when workloads increase and shrink when they ease off. This satisfies both scale-up and scale-down requirements without manual intervention.

C (Auto-termination after 30 minutes of inactivity) ensures the cluster stops entirely when no work is running, eliminating the cost of an idle cluster. This is the cheapest possible state.

Option A (disable Photon) reduces compute acceleration — that ' s a performance regression with no meaningful cost benefit for autoscaling. Option B (compute policy that lets users manage settings) adds governance overhead and doesn ' t address scaling behaviour. Option E (fixed number of workers) is the opposite of autoscaling — a static worker count that either over-provisions during quiet periods or under-provisions during peaks.

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.

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 file arrival trigger starts Job1 when new files are detected in the monitored Azure Storage location. Because the job is launched only after a qualifying arrival, compute does not remain active while the source is idle. This is well suited to unpredictable file-delivery patterns and avoids the unnecessary executions produced by a fixed schedule. A continuous trigger keeps the workload running and therefore consumes compute even when no files are available. A scheduled trigger starts the job at predetermined times whether or not new data exists. A manual trigger cannot provide automatic processing. File arrival triggers consequently provide the required event-driven behavior while improving resource utilization and controlling cost during inactive periods. Microsoft Learn

The Finance catalog: USE CATALOG

The Purchases schema: USE SCHEMA

The Transactions table: SELECT